Resource optimization methods, equipment, devices, systems and storage media

Abstract

This application provides a resource optimization method, device, apparatus, system, and storage medium. The method is used in a network device of a D2D collaborative computing system, which also includes a TU and an RU. The method includes: receiving a charging indication signal from the TU for a D2D collaborative computing task, the charging indication signal instructing the network device to charge the RU according to the D2D collaborative computing task; determining a resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal; determining resource optimization values ​​corresponding to the D2D collaborative computing task based on the resource optimization model, the resource optimization values ​​including the TU's transmit power optimization value, the network device's transmit power optimization value, and the RU's computational resource optimization value; and sending the TU's transmit power optimization value and the RU's computational resource optimization value to the TU and RU respectively. This application can reduce the latency and energy consumption of collaborative computing in a D2D system and improve resource optimization efficiency.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a resource optimization method, device, apparatus, system and storage medium. Background Technology

[0002] In a Device-to-Device (D2D) network, when D2D Transmitting Users (TU) and D2D Receiving Users (RU) have the same computational task, the D2D Transmitting Users will transmit the task to the D2D Receiving Users, who will then complete the computational task independently and share the computational results with the D2D Transmitting Users.

[0003] However, the charging efficiency of D2D transmitting users to D2D receiving users in existing technologies is not high, which has become a key problem that urgently needs to be solved. Summary of the Invention

[0004] This application provides a resource optimization method, device, apparatus, system, and storage medium to solve the problem of low charging efficiency between D2D transmitting users and D2D receiving users in the prior art, thereby increasing the enthusiasm of D2D receiving users to participate in collaborative computing and improving resource optimization efficiency.

[0005] In a first aspect, embodiments of this application provide a resource optimization method, the method being used in a network device of a device-to-device (D2D) collaborative computing system, the D2D collaborative computing system further including a transmitting user (TU) and a receiving user (RU); the method includes:

[0006] The network device receives a charging indication signal sent by the TU for a D2D collaborative computing task, the charging indication signal being used to instruct the network device to charge the RU according to the D2D collaborative computing task;

[0007] The resource optimization model corresponding to the D2D collaborative computing task is determined based on the charging indication signal.

[0008] The resource optimization value corresponding to the D2D collaborative computing task is determined according to the resource optimization model. The resource optimization value includes the transmit power optimization value of the TU, the transmit power optimization value of the network device, and the computing resource optimization value of the RU.

[0009] The optimized transmit power value of the TU and the optimized computational resource value of the RU are sent to the TU and the RU, respectively.

[0010] Optionally, the resource optimization method according to one embodiment of this application further includes:

[0011] The RU is charged according to the optimized transmit power value of the network device.

[0012] Optionally, according to a resource optimization method of one embodiment of this application, the charging indication signal includes task information for characterizing the D2D collaborative computing task, the task information including the size of the D2D input data and the computing resources required by the D2D collaborative computing task;

[0013] The step of determining the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal includes:

[0014] The resource optimization model is determined based on the input data size and the computing resources.

[0015] Optionally, according to one embodiment of the resource optimization method of this application, the step of determining the resource optimization model based on the input data size and the computing resources includes: determining a first utility model for characterizing the computing time saved by the TU, a second utility model for characterizing the energy saved by the TU, a third utility model for characterizing the fees paid by the TU to the network device, and a fourth utility model for characterizing the energy collected by the RU, based on the input data size and the computing resources;

[0016] Based on the first utility model, the second utility model, the third utility model, and the fourth utility model, a resource optimization model is determined to characterize the total utility of the network device, the TU, and the RU.

[0017] Optionally, according to one embodiment of the resource optimization method of this application, the step of determining the resource optimization value corresponding to the D2D collaborative computing task based on the resource optimization model includes:

[0018] A first value range is determined, which is the range of values ​​for the transmission power of the TU;

[0019] Within the first value range, the first maximum value of the total utility is determined by the resource optimization model, and the transmit power of the TU corresponding to the first maximum value is the optimized transmit power value of the TU.

[0020] Optionally, according to one embodiment of the resource optimization method of this application, the step of determining the resource optimization value corresponding to the D2D collaborative computing task based on the resource optimization model includes:

[0021] A second value range is determined, which is the range of values ​​for the transmission power of the network device;

[0022] Within the second value range, the second maximum value of the total utility is determined according to the resource optimization model, and the transmit power of the network device corresponding to the second maximum value is the optimized transmit power value of the network device.

[0023] Optionally, according to one embodiment of the resource optimization method of this application, the step of determining the resource optimization value corresponding to the D2D collaborative computing task based on the resource optimization model includes:

[0024] A third value range is determined, wherein the third value range is the value range of the computing resources of the RU;

[0025] Within the third value range, the third maximum value of the total utility is determined according to the resource optimization model, and the computational resources of the RU corresponding to the third maximum value are the computational resource optimization values ​​of the RU.

[0026] Secondly, embodiments of this application also provide a resource optimization method, the method being used in a device-to-device (D2D) collaborative computing system for transmitting users (TUs), the D2D collaborative computing system further including network devices and receiving users (RUs); the method includes:

[0027] Define the D2D collaborative computing task;

[0028] Send a charging indication signal to the network device for the D2D collaborative computing task, the charging indication signal being used to instruct the network device to charge the RU according to the D2D collaborative computing task;

[0029] Receive the optimized transmit power value of the TU sent by the network device;

[0030] The D2D collaborative computing task is transmitted to the RU based on the optimized transmit power value of the TU.

[0031] Thirdly, embodiments of this application also provide a resource optimization method, the method being used in a device-to-device (D2D) collaborative computing system whereby the receiving user (RU) further includes a network device and a transmitting user (TU); the method includes:

[0032] The network device receives the optimized computing resource value of the RU sent by the network device. The optimized computing resource value of the RU is the optimized value determined by the network device based on the D2D collaborative computing task transmitted from the TU to the RU.

[0033] Receive the D2D collaborative computing task transmitted by the TU;

[0034] The D2D collaborative computing task is processed collaboratively based on the RU's optimized computing resource value to obtain the collaborative computing result.

[0035] Fourthly, embodiments of this application also provide a network device for a device-to-device (D2D) collaborative computing system, wherein the D2D collaborative computing system further includes a transmitting user (TU) and a receiving user (RU); the network device includes: a memory, a transceiver, and a processor.

[0036] A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations:

[0037] The network device receives a charging indication signal sent by the TU for a D2D collaborative computing task, the charging indication signal being used to instruct the network device to charge the RU according to the D2D collaborative computing task;

[0038] The resource optimization model corresponding to the D2D collaborative computing task is determined based on the charging indication signal.

[0039] The resource optimization value corresponding to the D2D collaborative computing task is determined according to the resource optimization model. The resource optimization value includes the transmit power optimization value of the TU, the transmit power optimization value of the network device, and the computing resource optimization value of the RU.

[0040] The optimized transmit power value of the TU and the optimized computational resource value of the RU are sent to the TU and the RU, respectively.

[0041] Fifthly, embodiments of this application also provide a transmitting user TU, which is used in a device-to-device (D2D) collaborative computing system. The D2D collaborative computing system further includes a network device and a receiving user RU. The transmitting user TU includes: a memory, a transceiver, and a processor.

[0042] A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations:

[0043] Define the D2D collaborative computing task;

[0044] Send a charging indication signal to the network device for the D2D collaborative computing task, the charging indication signal being used to instruct the network device to charge the RU according to the D2D collaborative computing task;

[0045] Receive the optimized transmit power value of the TU sent by the network device;

[0046] The D2D collaborative computing task is transmitted to the RU based on the optimized transmit power value of the TU.

[0047] Sixthly, embodiments of this application also provide a receiving user RU, which is used in a device-to-device (D2D) collaborative computing system. The D2D collaborative computing system further includes a network device and a transmitting user TU. The receiving user RU includes: a memory, a transceiver, and a processor.

[0048] A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations:

[0049] The network device receives the optimized computing resource value of the RU sent by the network device. The optimized computing resource value of the RU is the optimized value determined by the network device based on the D2D collaborative computing task transmitted from the TU to the RU.

[0050] Receive the D2D collaborative computing task transmitted by the TU;

[0051] The D2D collaborative computing task is processed collaboratively based on the RU's optimized computing resource value to obtain the collaborative computing result.

[0052] In a seventh aspect, embodiments of this application also provide a resource optimization device, which is used in a network device of a device-to-device (D2D) collaborative computing system, wherein the D2D collaborative computing system further includes a transmitting user (TU) and a receiving user (RU); the resource optimization device includes:

[0053] A signal receiving unit is configured to receive a charging indication signal sent by the TU for a D2D collaborative computing task, wherein the charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task;

[0054] The model determination unit is used to determine the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal.

[0055] The optimization value determination unit is used to determine the resource optimization value corresponding to the D2D collaborative computing task according to the resource optimization model. The resource optimization value includes the transmit power optimization value of the TU, the transmit power optimization value of the network device, and the computing resource optimization value of the RU.

[0056] An optimization value sending unit is used to send the optimized transmit power value of the TU and the optimized computing resource value of the RU to the TU and the RU, respectively.

[0057] Eighthly, embodiments of this application also provide a resource optimization device, which is used for a transmitting user (TU) in a device-to-device (D2D) collaborative computing system, wherein the D2D collaborative computing system further includes a network device and a receiving user (RU); the resource optimization device includes:

[0058] Task determination unit, used to determine D2D collaborative computing tasks;

[0059] A signal transmitting unit is configured to send a charging indication signal to the network device for the D2D collaborative computing task, the charging indication signal being used to instruct the network device to charge the RU according to the D2D collaborative computing task;

[0060] The first optimization value receiving unit is used to receive the TU transmit power optimization value sent by the network device;

[0061] The task transmission unit is used to transmit the D2D collaborative computing task to the RU according to the optimized transmit power value of the TU.

[0062] Ninthly, embodiments of this application also provide a resource optimization device, the resource optimization device being used for a receiving user RU in a device-to-device (D2D) collaborative computing system, the D2D collaborative computing system further including a network device and a transmitting user TU; the resource optimization device includes:

[0063] The second optimization value receiving unit is used to receive the computational resource optimization value of the RU sent by the network device. The computational resource optimization value of the RU is the optimization value determined by the network device based on the D2D collaborative computing task transmitted from the TU to the RU.

[0064] A task receiving unit is used to receive the D2D collaborative computing task transmitted by the TU.

[0065] The collaborative computing unit is used to perform collaborative computing processing on the D2D collaborative computing task based on the computing resource optimization value of RU, and obtain the collaborative computing result.

[0066] In a tenth aspect, embodiments of this application also provide a device-to-device (D2D) collaborative computing system, the D2D collaborative computing system including a network device, a transmitting user (TU), and a receiving user (RU);

[0067] The network device is used to perform the steps of the resource optimization method described in the first aspect as described above; the TU is used to perform the steps of the resource optimization method described in the second aspect as described above; and the RU is used to perform the steps of the resource optimization method described in the third aspect as described above.

[0068] Eleventhly, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing the processor to perform the steps of the resource optimization method described in the first aspect, the second aspect, or the third aspect.

[0069] The resource optimization method, device, apparatus, system, and storage medium provided in this application can receive a charging indication signal sent by a TU (Transmitter Unit) for a D2D collaborative computing task. The charging indication signal instructs the network device to charge the RU (Resource Utility Unit) according to the D2D collaborative computing task. Based on the charging indication signal, a resource optimization model corresponding to the D2D collaborative computing task is determined. Based on the resource optimization model, resource optimization values ​​corresponding to the D2D collaborative computing task are determined, including the optimized transmit power value of the TU, the optimized transmit power value of the network device, and the optimized computational resource value of the RU. The optimized transmit power value of the TU and the optimized computational resource value of the RU are then sent to the TU and RU, respectively. This application embodiment can enhance the enthusiasm of D2D receiving users to participate in collaborative computing. By solving for the optimized transmit power value of the TU, the optimized transmit power value of the network device, and the optimized computational resource value of the RU through a model, the latency and energy consumption of collaborative computing in the D2D system are further reduced, and resource optimization efficiency is improved. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 This is one of the flowcharts illustrating the resource optimization method provided in the embodiments of this application;

[0072] Figure 2 This is a schematic diagram illustrating an application scenario of the resource optimization method provided in the embodiments of this application;

[0073] Figure 3 This is a schematic diagram of the resource optimization algorithm in an embodiment of this application;

[0074] Figure 4 This is a schematic diagram of the simulation results of the resource optimization method in the embodiments of this application;

[0075] Figure 5 This is a second schematic flowchart of the resource optimization method provided in the embodiments of this application;

[0076] Figure 6 This is the third flowchart illustrating the resource optimization method provided in the embodiments of this application;

[0077] Figure 7 This is one of the structural schematic diagrams of the resource optimization device provided in the embodiments of this application;

[0078] Figure 8This is a second schematic diagram of the structure of the resource optimization device provided in the embodiments of this application;

[0079] Figure 9 This is the third schematic diagram of the resource optimization device provided in the embodiments of this application;

[0080] Figure 10 This is a schematic diagram of the network device provided in the embodiments of this application;

[0081] Figure 11 This is one of the structural schematic diagrams of the terminal device provided in the embodiments of this application;

[0082] Figure 12 This is the second structural schematic diagram of the terminal device provided in the embodiments of this application. Detailed Implementation

[0083] In the embodiments of this application, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0084] In the embodiments of this application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0085] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0086] In a D2D communication network, when a TU and an RU have the same computational task, and the TU is unable to complete the task independently due to resource constraints or insufficient energy, the TU will transfer the task to the RU, which has more abundant resources. The RU will then complete the computational task alone and share the results with the TU. Network devices and the TU will provide wireless charging to the RU as an incentive. The established D2D collaborative computing system ensures the security of collaborative computing and the effectiveness of D2D devices.

[0087] RU (Radio Unit) wireless energy harvesting requires energy harvesting technology, which involves Wireless Power Transmission (WPT) nodes charging batteries via electromagnetic radiation. In WPT, energy can be obtained from ambient signals or from a dedicated power source in a fully controlled manner; this application embodiment employs the latter. A beamforming technique is used at the network device to transmit an energy beam, and the receiving end is equipped with a wireless power receiver, including a receiver antenna or antenna array, a matching network, an RF-to-DC converter or rectifier, a power management unit, and an energy storage unit.

[0088] This application provides a resource optimization method, device, apparatus, system, and storage medium to optimize the transmit power of the TU, the computing resource allocation of the RU, and the transmit power for charging network devices, thereby reducing the latency and energy consumption of collaborative computing in D2D systems and improving the efficiency of resource optimization.

[0089] The method and apparatus are based on the same concept of the application. Since the methods and apparatus solve problems in similar ways, the implementation of the apparatus and methods can refer to each other, and the repeated parts will not be described again.

[0090] The technical solutions provided in this application can be applied to various systems, especially 5G systems. For example, applicable systems may include Global System for Mobile Communication (GSM), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA) General Packet Radio Service (GPRS), Long Term Evolution (LTE), LTE Frequency Division Duplex (FDD), LTE Time Division Duplex (TDD), Long Term Evolution Advanced (LTE-A), Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), and 5G New Radio (NR). All of these systems include terminal equipment and network equipment. The systems may also include a core network component, such as Evolved Packet System (EPS) and 5G system (5GS).

[0091] The terminal devices involved in the embodiments of this application can be devices that provide voice and / or data connectivity to users, handheld devices with wireless connectivity, or other processing devices connected to a wireless modem. The names of the terminal devices may differ in different systems; for example, in a 5G system, a terminal device can be called User Equipment (UE). Wireless terminal devices can communicate with one or more core networks (CNs) via a Radio Access Network (RAN). Wireless terminal devices can be mobile terminal devices, such as mobile phones (or "cellular" phones) and computers with mobile terminal devices, for example, portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and / or data with the RAN. Examples include Personal Communication Service (PCS) phones, cordless phones, Session Initiated Protocol (SIP) phones, Wireless Local Loop (WLL) stations, and Personal Digital Assistants (PDAs). Wireless terminal equipment can also be referred to as a system, subscriber unit, subscriber station, mobile station, mobile station, remote station, access point, remote terminal, access terminal, user terminal, user agent, or user device, but this application does not limit the terminology.

[0092] The network device involved in this application embodiment can be a base station, which may include multiple cells providing services to terminals. Depending on the specific application, a base station may also be called an access point, or a device in an access network that communicates with a wireless terminal device through one or more sectors on the air interface, or other names. The network device can be used to exchange received air frames with Internet Protocol (IP) packets, acting as a router between the wireless terminal device and the rest of the access network, where the rest of the access network may include an Internet Protocol (IP) communication network. The network device can also coordinate the attribute management of the air interface. For example, the network equipment involved in the embodiments of this application can be a base transceiver station (BTS) in a Global System for Mobile communications (GSM) or Code Division Multiple Access (CDMA), a NodeB in a Wide-band Code Division Multiple Access (WCDMA) system, an evolved Node B (eNB or e-NodeB) in a long term evolution (LTE) system, a 5G base station (gNB) in a next generation system, a Home evolved Node B (HeNB), a relay node, a femto, a pico, etc., and is not limited in the embodiments of this application. In some network structures, the network equipment may include centralized unit (CU) nodes and distributed unit (DU) nodes, and the centralized unit and distributed unit may be geographically separated.

[0093] Network devices and terminal devices can each use one or more antennas for multiple-input multiple-output (MIMO) transmission. MIMO transmission can be single-user MIMO (SU-MIMO) or multiple-user MIMO (MU-MIMO). Depending on the configuration and number of antenna combinations, MIMO transmission can be 2D-MIMO, 3D-MIMO, FD-MIMO, or massive-MIMO, and can also be diversity transmission, precoding transmission, or beamforming transmission, etc.

[0094] Figure 1 This is one of the flowcharts illustrating the resource optimization method provided in the embodiments of this application. This resource optimization method can be used in network devices of a D2D collaborative computing system, such as a base station. The D2D collaborative computing system also includes TUs and RUs; for example... Figure 1 As shown, this resource optimization method may include the following steps:

[0095] Step 101: Receive the charging indication signal sent by the TU for the D2D collaborative computing task. The charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task.

[0096] Specifically, such as Figure 2 As shown, the D2D collaborative computing system of this application embodiment includes a network device, a TU, and an RU. Assuming each TU is aware of its neighboring RUs and the network device, before transmitting a task, the TU sends a charging indication signal for the D2D collaborative computing task to the network device, and the network device receives the charging indication signal for the D2D collaborative computing task sent by the TU. The charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task.

[0097] Step 102: Determine the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal.

[0098] Specifically, after receiving the charging indication signal sent by the TU for the D2D collaborative computing task, the network device determines the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal.

[0099] The resource optimization model can be a pre-set optimization model or an optimization model determined based on the following models:

[0100] (1) System Model

[0101] Figure 2 This is a schematic diagram illustrating an application scenario of the resource optimization method provided in the embodiments of this application; for example... Figure 2As shown, the application scenario may include a network device, a TU, and a RU. p represents the transmission power of the TU, h1 represents the D2D channel impulse response parameter, q represents the transmission power of the network device, and h2 represents the cellular channel impulse response parameter.

[0102] (2) Communication model

[0103] Assume that each TU knows the adjacent RU and network device. Before transmitting a task, the TU sends a charging prompt signal to the network device to charge the RU. The TU transmits the task to the RU for execution. The task is represented by the binary tuple <D, c>, where D represents the input data size (bits) of the D2D collaborative computing task, and c represents the computing resources (cycles) required for the D2D collaborative computing task. The transmission rate of the D2D collaborative computing task is:

[0104]

[0105] where w represents the channel bandwidth, p represents the transmission power of the TU, h1 represents the D2D channel impulse response parameter, and n represents Gaussian white noise.

[0106] (3) Charging model

[0107] Using cooperative energy beamforming technology, the distributed multi-antenna energy transmitter can charge the RU by the network device, and the charging process will be completed within the task transmission time. Compared with task transmission and calculation, the time for returning the task calculation result is too short and can be ignored. The task transmission time can be written as:

[0108]

[0109] where D represents the input data size of the D2D collaborative computing task, and R represents the transmission rate of the D2D collaborative computing task.

[0110] The charging energy can be expressed as:

[0111]

[0112] where p represents the transmission power of the TU, h1 represents the D2D channel impulse response parameter, q represents the transmission power of the network device, h2 represents the cellular channel impulse response parameter, D represents the input data size of the D2D collaborative computing task, and R represents the transmission rate of the D2D collaborative computing task.

[0113] (4) Delay, energy consumption, and cost utility model [[ID=​​​​Considering the time, energy, and cost of charging network equipment, and assuming the TU computing resource f1 is known, the time consumed by the TU computing is:

[0116]

[0117] Where c represents the computing resources required for the D2D collaborative computing task, and f1 represents the computing resources of the TU.

[0118] Assume the RU's computing resources f d It is known that the time consumed by collaborative computing is:

[0119]

[0120] Where D represents the input data size of the D2D collaborative computing task, c represents the computing resources required for the D2D collaborative computing task, R represents the transmission rate of the D2D collaborative computing task, and f d This represents the computing resources of RU.

[0121] The energy consumed by the task in TU is:

[0122]

[0123] Where k represents the energy factor, f1 represents the computational resources of the TU, and c represents the computational resources required for the D2D collaborative computing task.

[0124] During collaborative computing, the energy consumed by the TU transmission task is as follows:

[0125]

[0126] Where p represents the transmit power of the TU, R represents the transmission rate of the D2D collaborative computing task, and D represents the input data size of the D2D collaborative computing task.

[0127] b) TU Billing Model

[0128] The RU (Radio Unit) is a distributed multi-antenna energy transmitter of a network device that uses cooperative energy beamforming technology for wireless charging during mission transmission. The fee paid by the TU (Radio Unit) to the network device is...

[0129]

[0130] in, q represents the fee paid by the TU to the network device, i.e., the energy consumed by the network device for charging; q represents the transmit power of the network device; D represents the input data size of the D2D collaborative computing task; and R represents the transmission rate of the D2D collaborative computing task.

[0131] c) Energy utility model of RU

[0132] The utility of the RU consists solely of energy, which can be divided into the energy that the network devices and TUs jointly charge the RU, and the energy consumed by the RU in performing tasks. The charging energy and task energy consumption are as follows:

[0133]

[0134]

[0135] Where p represents the transmit power of the TU, h1 represents the D2D channel impulse response parameter, q represents the transmit power of the network device, h2 represents the cellular channel impulse response parameter, D represents the input data size of the D2D collaborative computing task, R represents the transmission rate of the D2D collaborative computing task, k represents the energy factor, and f d represents the computing resources of RU, and c represents the computing resources required for the D2D collaborative computing task.

[0136] Step 103: Determine the resource optimization values ​​corresponding to the D2D collaborative computing task based on the resource optimization model. The resource optimization values ​​include the transmit power optimization value of the TU, the transmit power optimization value of the network device, and the computing resource optimization value of the RU.

[0137] Specifically, after determining the resource optimization model, the resource optimization values ​​corresponding to the D2D collaborative computing task are further determined based on the resource optimization model. That is, the optimal transmit power values ​​of the TU, the optimal transmit power values ​​of the network devices, and the optimal computational resource values ​​of the RU are further solved using the resource optimization model. Since the resource optimization model is a non-convex function, the optimal transmit power values ​​of the TU, the optimal transmit power values ​​of the network devices, and the optimal computational resource values ​​of the RU can be determined using the Block Coordinate Descent (BCD) algorithm.

[0138] Step 104: Send the optimized transmit power value of TU and the optimized computational resource value of RU to TU and RU respectively.

[0139] Specifically, after determining the resource optimization value corresponding to the D2D collaborative computing task through the resource optimization model, the network device sends the TU's transmit power optimization value and the RU's computing resource optimization value to the TU and RU respectively.

[0140] As can be seen from the above embodiments, by receiving a charging indication signal sent by the TU for the D2D collaborative computing task, the charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task; the resource optimization model corresponding to the D2D collaborative computing task is determined based on the charging indication signal; the resource optimization value corresponding to the D2D collaborative computing task is determined based on the resource optimization model, the resource optimization value includes the TU's transmit power optimization value, the network device's transmit power optimization value, and the RU's computing resource optimization value; the TU's transmit power optimization value and the RU's computing resource optimization value are sent to the TU and RU respectively. This embodiment of the application can enhance the enthusiasm of D2D receiving users to participate in collaborative computing, and by solving the TU's transmit power optimization value, the network device's transmit power optimization value, and the RU's computing resource optimization value through the model, further reduce the latency and energy consumption of D2D system collaborative computing, and improve resource optimization efficiency.

[0141] Optionally, the resource optimization method also includes charging the RU according to the optimized transmit power value of the network device.

[0142] Specifically, after determining the resource optimization value corresponding to the D2D collaborative computing task through the resource optimization model, the network device charges the RU according to the network device's transmit power optimization value.

[0143] As can be seen from the above embodiments, by charging the RU according to the optimized transmit power value of the network device, the energy loss of the RU due to collaborative computing can be compensated, thereby increasing the RU's enthusiasm for participating in collaborative computing.

[0144] Optionally, the charging indication signal includes task information for characterizing the D2D collaborative computing task, including the size of the D2D input data and the computing resources required for the D2D collaborative computing task.

[0145] The resource optimization model corresponding to the D2D collaborative computing task is determined based on the charging indication signal, including:

[0146] The resource optimization model is determined based on the size of the input data and computing resources.

[0147] Specifically, the charging indication signal sent by the TU for the D2D collaborative computing task includes task information characterizing the D2D collaborative computing task, including the D2D input data size (bits) and the computing resources (cycles) required for the D2D collaborative computing task. Further, the resource optimization model corresponding to the D2D collaborative computing task is determined based on the charging indication signal, that is, the resource optimization model is determined based on the input data size (bits) and computing resources (cycles).

[0148] As can be seen from the above embodiments, by determining the resource optimization model based on the input data size (bits) and computing resources (cycles), computing efficiency can be further improved and the latency and energy consumption of collaborative computing can be reduced.

[0149] Optionally, a resource optimization model is determined based on the input data size and computational resources, including:

[0150] Based on the input data size and computing resources, a first utility model is determined to characterize the computation time saved by the TU, a second utility model is determined to characterize the energy saved by the TU, a third utility model is determined to characterize the fees paid by the TU to network devices, and a fourth utility model is determined to characterize the energy collected by the RU.

[0151] Based on the first utility model, the second utility model, the third utility model, and the fourth utility model, a resource optimization model is determined to characterize the total utility of network devices, TUs, and RUs.

[0152] Specifically, based on the input data size and computing resources, a first utility model is determined to characterize the computation time saved by TU. A second utility model for characterizing the energy saved by TU The third utility model used to characterize the fees paid by the TU to network equipment. and a fourth utility model for characterizing the energy collected by the RU

[0153] According to the first utility model Second Utility Model Third Utility Model and the fourth utility model Determine a resource optimization model to characterize the total utility of network devices, TUs, and RUs.

[0154] In one embodiment, a resource optimization model can be determined according to a first formula; wherein the first formula includes:

[0155]

[0156] in,

[0157]

[0158] s.t0≤p≤p max

[0159] 0≤f d ≤f d max

[0160] 0≤q≤q max

[0161] U represents the total utility of network devices, TU, and RU. Used to characterize the computation time saved by TU Used to characterize the energy saved by TU Used to characterize the fees paid by the TU to network equipment. Used to characterize the energy collected by the RU, β1, β2, β3, and β4 represent, respectively and The corresponding weighting factor, This represents maximizing the total profit of the D2D collaborative computing system; the constraint functions represent the ranges of values ​​for p, f, and q under the constraints; p max q represents the maximum transmit power of the TU. max f represents the maximum transmit power of the network device. d max Let f1 represent the maximum computational resources of the RU, c represent the computational resources required for the D2D collaborative computing task, f1 represent the computational resources of the TU, D represent the input data size, w represent the channel bandwidth, n represent Gaussian white noise, h1 represent the D2D channel impulse response parameters, h2 represent the cellular channel impulse response parameters, k represent the energy factor, η represent the energy transmission efficiency, p represent the transmit power of the TU, q represent the transmit power of the network device, and f1 represent the maximum computational resources of the RU, c represent the computational resources required for the D2D collaborative computing task, f2 represent the computational resources of the TU, D represent the input data size, w represent the channel bandwidth, n represent Gaussian white noise, h1 represent the D2D channel impulse response parameters, h2 represent the cellular channel impulse response parameters, k represent the energy factor, η represent the energy transmission efficiency, p represent the transmit power of the TU, q represent the transmit power of the network device, and f1 represent the transmit power of the TU. d This represents the computing resources of RU.

[0162] As can be seen from the above embodiments, by determining the resource optimization model through the first utility model, the second utility model, the third utility model and the fourth utility model, the problems of optimization latency, energy and network equipment charging costs can be taken into account in an overall manner, so as to achieve the goal of reducing the latency and energy consumption of collaborative computing in D2D system.

[0163] Optionally, the resource optimization value corresponding to the D2D collaborative computing task is determined according to the resource optimization model, including:

[0164] Determine the first value range, which is the range of values ​​for the TU's transmit power;

[0165] Within the first range of values, the first maximum value of total utility is determined by the resource optimization model, and the transmit power of the TU corresponding to the first maximum value is the optimized transmit power value of the TU.

[0166] Specifically, the first range of values ​​can be expressed as:

[0167] 0≤p≤p max

[0168] Where, p max This indicates the maximum transmit power of the TU.

[0169] In one embodiment, the optimized transmit power value of the TU can be determined according to a second formula; wherein the second formula includes:

[0170] maxU 11 +U 12

[0171]

[0172] Where z is obtained by substitution of variables in the first formula above, and after substitution, we get:

[0173]

[0174] maxU 11 +U 12 U 11 +U 12 The maximum value operation, where:

[0175]

[0176]

[0177] U represents the total utility of network devices, TU, and RU. Used to characterize the computation time saved by TU Used to characterize the energy saved by TU Used to characterize the fees paid by the TU to network equipment Used to characterize the energy collected by the RU, β1, β2, β3, and β4 represent, respectively and The corresponding weighting factors are: c represents the computing resources required for the D2D collaborative computing task; f1 represents the computing resources of the TU; D represents the input data size of the D2D collaborative computing task; w represents the channel bandwidth; n represents Gaussian white noise; h1 represents the D2D channel impulse response parameter; h2 represents the cellular channel impulse response parameter; k represents the energy factor; η represents the energy transmission efficiency; p represents the transmit power of the TU; q represents the transmit power of the network device; and f d This refers to the computing resources of the RU.

[0178] Specifically, first, variable substitution is performed to make...

[0179]

[0180] The objective function is rewritten as

[0181]

[0182] Since the objective function for p is non-convex, the convex-concave procedure (CCCP) algorithm is used to decompose the objective function:

[0183]

[0184] Among them, U 11 and U 12 Regarding whether z is a convex or concave function, the resource optimization model is rewritten as follows:

[0185] P2: maxU 11 +U 12

[0186]

[0187] For the optimization model P2, rewriting the expression for the difference of convex (DC), we get:

[0188]

[0189] The two parts in the above equation have similar structures and are both convex functions. The expression representing the positive term can be expanded using a first-order Taylor series. Linearization is performed at this point, where the value is the current point generated in the previous iteration, resulting in:

[0190]

[0191] For the optimization model P2, it can be reformulated as a continuously iterative convex optimization model P3, given by the following equation:

[0192] P3:

[0193]

[0194] Among them, the optimization model P3 is a concave function. It is the optimal value of z in the previous iteration optimization. The optimized value of TU's transmit power can be obtained by continuous convex approximation.

[0195] As can be seen from the above embodiments, by determining the optimal transmit power value of the TU through the resource optimization model within the first value range, the transmit power of D2D transmitting users can be reasonably allocated, further improving resource optimization efficiency.

[0196] Optionally, the resource optimization value corresponding to the D2D collaborative computing task is determined according to the resource optimization model, including:

[0197] Determine the second value range, which is the range of values ​​for the transmission power of the network device;

[0198] Within the second value range, the second maximum value of total utility is determined according to the resource optimization model. The transmit power of the network device corresponding to the second maximum value is the optimized transmit power value of the network device.

[0199] Specifically, the second range of values ​​can be expressed as:

[0200] s.t0≤q≤q max

[0201] Where, q max This indicates the maximum transmit power of the network device.

[0202] In one embodiment, the optimized transmit power value of the network device can be determined according to a third formula; wherein the third formula includes:

[0203]

[0204] s.t0≤q≤q max

[0205] Where, max q U(q) represents the maximum value of U in the first formula, s.t0≤q≤q max This represents the range of values ​​for q under the constraints. max U represents the maximum transmit power of the network device, and U represents the total utility of the network device, TU, and RU.

[0206] As can be seen from the above embodiments, by determining the optimal transmit power value of the network device through the resource optimization model within the second value range, the transmit power of the network device can be reasonably allocated, thereby further improving the efficiency of resource optimization.

[0207] Optionally, the resource optimization value corresponding to the D2D collaborative computing task is determined according to the resource optimization model, including:

[0208] Determine the third value range, which is the value range of the computing resources of RU;

[0209] Within the third value range, the third maximum value of total utility is determined according to the resource optimization model. The computational resources of RU corresponding to the third maximum value are the optimized computational resources of RU.

[0210] Specifically, the third range of values ​​can be expressed as:

[0211]

[0212] in, This represents the maximum computing resources of RU.

[0213] In one embodiment, the optimized transmit power value of the network device can be determined according to a fourth formula; wherein the fourth formula includes:

[0214]

[0215] in, β1 and β4 represent the optimal computational resource values ​​for RU. and The corresponding weighting factors, where:

[0216]

[0217]

[0218] and Let represent the fees paid by the D2D collaborative computing task TU to the network device and the energy received by the RU collaborative computing task, respectively; let c represent the computing resources required by the D2D collaborative computing task; let D represent the input data size of the D2D collaborative computing task; let w represent the channel bandwidth; let n represent Gaussian white noise; let h1 represent the D2D channel impulse response parameters; let h2 represent the cellular channel impulse response parameters; let k represent the energy factor; let η represent the energy transmission efficiency; let p represent the transmit power of the TU; let q represent the transmit power of the network device; and let f represent the energy transfer efficiency. d This represents the computing resources of the RU.

[0219] Specifically, the optimal computational resource value for RU. This can be represented by the optimization model P4:

[0220]

[0221]

[0222] The optimization model P4 is a concave function problem, and we get:

[0223]

[0224] Among them, f d This represents the computing resources of RU. denoted as the computational resource optimization value of RU, β1 and β4 represent the corresponding weight factors, k represents the energy factor, and η represents the energy transmission efficiency.

[0225] As can be seen from the above embodiments, by determining the optimal computational resource value of RU through the resource optimization model within the third value range, the computational resources of RU can be reasonably allocated, further improving the efficiency of resource optimization.

[0226] The implementation process of the above resource optimization method will be illustrated below through two examples.

[0227] Example 1

[0228] Figure 3 This is a schematic diagram of the resource optimization algorithm in an embodiment of this application, as shown below. Figure 3 As shown:

[0229] (1) The original non-convex variable coupling problem P1.

[0230] (2) Initialize p, q, f d .

[0231] (3) Variable substitution.

[0232] (4) Use CCCP technology.

[0233] (5) Continuous convex approximation.

[0234] (6) Use Taylor expressions.

[0235] (7) Quasi-convex problems.

[0236] (8) Determine whether the termination criteria are met; if they are met, proceed to step (9); if they are not met, return to step (5).

[0237] Among them, steps (2) to (8) use the CCCP iterative algorithm.

[0238] (9) Update p.

[0239] (10) Update q using convex optimization methods.

[0240] (11) Update f using linear programming. d .

[0241] (12) Determine if the termination criteria are met; if they are met, proceed to step (13). If not, return to step (3).

[0242] (13) Output.

[0243] Steps (2) to (12) use a BCD-based algorithm.

[0244] Example 2

[0245] Figure 4 This is a schematic diagram of the simulation processing results of the resource optimization method in the embodiments of this application; as shown... Figure 4 As shown:

[0246] In the simulation, this embodiment uses a system with a radius of 300m as an example, where D2D and network devices are randomly distributed. The maximum distance for D2D association is set to 100m, the energy conversion efficiency is set to 0.6, Monte Carlo simulation is used, and the simulation accuracy and maximum number of cycles are set. Table 1 lists the important parameters as shown in Table 1:

[0247] Table 1

[0248]

[0249]

[0250] Figure 5 This is a second schematic flowchart of the resource optimization method provided in the embodiments of this application. This resource optimization method can be used in a device-to-device (D2D) collaborative computing system where the transmitting user (TU) also includes network devices and receiving users (RUs). Figure 5 As shown, the method includes:

[0251] Step 501: Determine the D2D collaborative computing task.

[0252] Specifically, such as Figure 2 As shown, the D2D collaborative computing system of this application embodiment includes network devices (such as base stations), TUs, and RUs. It is assumed that each TU is aware of its neighboring RUs and network devices. First, the TU determines the D2D collaborative computing task, which includes the D2D input data size and the computing resources required for the D2D collaborative computing task.

[0253] Step 502: Send a charging indication signal to the network device for the D2D collaborative computing task. The charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task.

[0254] Specifically, before transmitting a task, the TU sends a charging indication signal for the D2D collaborative computing task to the network device, and the network device receives the charging indication signal for the D2D collaborative computing task sent by the TU. The charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task.

[0255] Step 503: Receive the optimized transmit power value of the TU sent by the network device.

[0256] Specifically, after receiving a charging indication signal from the TU for the D2D collaborative computing task, the network device determines the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal. The resource optimization values ​​corresponding to the D2D collaborative computing task are determined according to the resource optimization model. These resource optimization values ​​include the TU's transmit power optimization value, the network device's transmit power optimization value, and the RU's computing resource optimization value. Further, the TU receives its transmit power optimization value from the network device.

[0257] Step 504: Transmit the D2D collaborative calculation task to the RU based on the TU's optimized transmit power value.

[0258] Specifically, after receiving the optimized transmit power value of the TU sent by the network device, the D2D collaborative computing task is transmitted to the RU according to the optimized transmit power value of the TU.

[0259] As can be seen from the above embodiments, by determining the D2D collaborative computing task; sending a charging indication signal for the D2D collaborative computing task to the network device, the charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task; receiving the TU's transmit power optimization value sent by the network device; and transmitting the D2D collaborative computing task to the RU according to the TU's transmit power optimization value, the transmit power of D2D transmitting users can be reasonably allocated, further improving resource optimization efficiency.

[0260] Figure 6 This is the third flowchart illustrating the resource optimization method provided in this application embodiment. This resource optimization method can be used in a device-to-device (D2D) collaborative computing system where the receiving user (RU) also includes network devices and transmitting users (TUs). Figure 6 As shown, the method includes:

[0261] Step 601: Receive the RU's optimized computing resources value sent by the network device. The RU's optimized computing resources value is the optimized value determined by the network device based on the D2D collaborative computing task transmitted from the TU to the RU.

[0262] Specifically, such as Figure 2As shown, the D2D collaborative computing system of this application embodiment includes network devices (such as base stations), TUs (Transmission Units), and RUs (Resource Utilities). Assuming each TU is aware of its neighboring RUs and network devices, before transmitting a task, the TU sends a charging indication signal for the D2D collaborative computing task to the network device. The network device receives the charging indication signal sent by the TU for the D2D collaborative computing task. The charging indication signal instructs the network device to charge the RU according to the D2D collaborative computing task. After receiving the charging indication signal from the TU for the D2D collaborative computing task, the network device determines the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal. After determining the resource optimization model, the network device further determines the resource optimization value corresponding to the D2D collaborative computing task based on the resource optimization model, that is, it uses the resource optimization model to further solve for the optimized transmit power value of the TU, the optimized transmit power value of the network device, and the optimized computational resource value of the RU. Furthermore, the RU receives the optimized computational resource value of the RU sent by the network device.

[0263] Step 602: Receive the D2D collaborative computing task transmitted by the TU.

[0264] Specifically, the D2D collaborative computing task transmitted by the TU is received. The task information includes the size of the D2D input data and the computing resources required for the D2D collaborative computing task.

[0265] Step 603: Perform collaborative computing processing on the D2D collaborative computing task based on the RU's computational resource optimization value to obtain the collaborative computing result.

[0266] Specifically, after receiving the RU's computational resource optimization value sent by the network device, the RU performs collaborative computation processing on the D2D collaborative computation task to obtain the collaborative computation result.

[0267] As can be seen from the above embodiments, by receiving the RU's computational resource optimization value sent by the network device, the RU's computational resource optimization value is the optimization value determined by the network device based on the D2D collaborative computing task transmitted from the TU to the RU; receiving the D2D collaborative computing task transmitted by the TU; and performing collaborative computing processing on the D2D collaborative computing task based on the RU's computational resource optimization value to obtain the collaborative computing result, the computational resources of the RU can be reasonably allocated, further improving the resource optimization efficiency.

[0268] Figure 7 This is one of the structural schematic diagrams of the resource optimization device provided in the embodiments of this application. The resource optimization device is used in a network device of a device-to-device (D2D) collaborative computing system. The D2D collaborative computing system also includes a transmitting user (TU) and a receiving user (RU). Figure 7 As shown, the resource optimization device includes:

[0269] The signal receiving unit 71 is used to receive a charging indication signal sent by the TU for the D2D collaborative computing task. The charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task.

[0270] Model determination unit 72 is used to determine the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal;

[0271] The optimization value determination unit 73 is used to determine the resource optimization value corresponding to the D2D collaborative computing task according to the resource optimization model. The resource optimization value includes the transmission power optimization value of the TU, the transmission power optimization value of the network device, and the computing resource optimization value of the RU.

[0272] The optimization value sending unit 74 is used to send the optimized transmit power value of the TU and the optimized computational resource value of the RU to the TU and RU respectively.

[0273] Furthermore, based on the aforementioned device, the resource optimization device also includes:

[0274] The charging unit is used to charge the RU according to the optimized transmit power value of the network device.

[0275] Furthermore, based on the aforementioned device, the charging indication signal includes task information characterizing the D2D collaborative computing task, including the D2D input data size and the computing resources required for the D2D collaborative computing task; the model determination unit 72 includes:

[0276] The model determination sub-unit is used to determine the resource optimization model based on the input data size and computing resources.

[0277] Furthermore, based on the aforementioned device, the model determines that the sub-units include:

[0278] The sub-model determination module is used to determine, based on the input data size and computing resources, a first utility model to characterize the computing time saved by the TU, a second utility model to characterize the energy saved by the TU, a third utility model to characterize the fees paid by the TU to network devices, and a fourth utility model to characterize the energy collected by the RU.

[0279] The overall model determination module is used to determine the resource optimization model for characterizing the overall utility of network devices, TUs, and RUs based on the first utility model, the second utility model, the third utility model, and the fourth utility model.

[0280] Furthermore, based on the aforementioned device, the optimization value determination unit 73 includes:

[0281] The first value range determination subunit is used to determine the first value range, which is the range of the TU's transmit power.

[0282] The first maximum value determination sub-unit is used to determine the first maximum value of total utility within a first value range through a resource optimization model. The transmit power of the TU corresponding to the first maximum value is the optimized transmit power value of the TU.

[0283] Furthermore, based on the aforementioned device, the optimization value determination unit 73 includes:

[0284] The second value range determination subunit is used to determine the second value range, which is the range of values ​​for the transmission power of the network device.

[0285] The second maximum value determination subunit is used to determine the second maximum value of total utility within the second value range according to the resource optimization model. The transmit power of the network device corresponding to the second maximum value is the optimized transmit power value of the network device.

[0286] Furthermore, based on the aforementioned device, the optimization value determination unit 73 includes:

[0287] The third value range determination subunit is used to determine the third value range, which is the value range of the computing resources of RU.

[0288] The third maximum value determination sub-unit is used to determine the third maximum value of total utility within the third value range according to the resource optimization model. The computational resources of the RU corresponding to the third maximum value are the optimized computational resources of the RU.

[0289] It should be noted that the division of units in the embodiments of this application is illustrative and only represents one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0290] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0291] It should be noted that the apparatus provided in this embodiment of the invention can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0292] Figure 8 This is a second schematic diagram of the resource optimization device provided in the embodiments of this application. This resource optimization device is used for transmitting user (TU) in a device-to-device (D2D) collaborative computing system. The D2D collaborative computing system also includes network equipment and receiving user (RU). Figure 8 As shown, the resource optimization device includes:

[0293] Task determination unit 81 is used to determine D2D collaborative computing tasks;

[0294] The signal transmitting unit 82 is used to send a charging indication signal to the network device for the D2D collaborative computing task. The charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task.

[0295] The first optimized value receiving unit 83 is used to receive the optimized transmit power value of the TU sent by the network device;

[0296] The task transmission unit 84 is used to transmit D2D collaborative computing tasks to the RU according to the TU's optimized transmit power value.

[0297] It should be noted that the division of units in the embodiments of this application is illustrative and only represents one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0298] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0299] It should be noted that the apparatus provided in this embodiment of the invention can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0300] Figure 9 This is the third schematic diagram of the resource optimization device provided in the embodiments of this application. This resource optimization device is used for receiving users (RUs) in a device-to-device (D2D) collaborative computing system. The D2D collaborative computing system also includes network devices and transmitting users (TUs). Figure 9 As shown, the resource optimization device includes:

[0301] The second optimization value receiving unit 91 is used to receive the computational resource optimization value of the RU sent by the network device. The computational resource optimization value of the RU is the optimization value determined by the network device according to the D2D collaborative computing task transmitted from the TU to the RU.

[0302] Task receiving unit 92 is used to receive D2D collaborative computing tasks transmitted by TU;

[0303] The collaborative computing unit 93 is used to perform collaborative computing processing on the D2D collaborative computing task based on the computing resource optimization value of RU, and obtain the collaborative computing result.

[0304] It should be noted that the division of units in the embodiments of this application is illustrative and only represents one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0305] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0306] It should be noted that the apparatus provided in this embodiment of the invention can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0307] Figure 10 This is a schematic diagram of the network device provided in an embodiment of this application; the network device can be used in a D2D collaborative computing system, which also includes a TU and a RU; the network device can be used to execute... Figure 1 The resource optimization methods shown include:

[0308] Receive a charging indication signal sent by the TU for the D2D collaborative computing task. The charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task.

[0309] Determine the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal;

[0310] The resource optimization values ​​for the D2D collaborative computing task are determined based on the resource optimization model. These resource optimization values ​​include the transmit power optimization values ​​of the TU, the transmit power optimization values ​​of the network devices, and the computing resource optimization values ​​of the RU.

[0311] Send the optimized transmit power value of the TU and the optimized computational resource value of the RU to the TU and RU respectively.

[0312] like Figure 10 As shown, transceiver 1000 is used to receive and send data under the control of processor 1010. Among them, in Figure 10 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 1010) and memory (memory 1020). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 1000 can be multiple elements, including transmitters and receivers, providing units for communicating with various other devices over transmission media, including wireless channels, wired channels, optical fibers, etc. The processor 1010 is responsible for managing the bus architecture and general processing, and the memory 1020 can store data used by the processor 1010 during operation.

[0313] The processor 1010 can be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD). The processor can also adopt a multi-core architecture.

[0314] Figure 11 This is one of the structural schematic diagrams of the terminal device provided in the embodiments of this application. The terminal device can be a TU (Transmission Unit) and can be used in a D2D collaborative computing system. The D2D collaborative computing system also includes network devices and RUs (Resource Roots). The terminal device can be used to execute... Figure 5 The resource optimization methods shown include:

[0315] Define the D2D collaborative computing task;

[0316] Send a charging indication signal to the network device for the D2D collaborative computing task. The charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task.

[0317] The optimized transmit power value of the TU sent by the receiving network device;

[0318] The D2D collaborative computing task is transmitted to the RU based on the TU's optimized transmit power value.

[0319] like Figure 11 As shown, transceiver 1100 is used to receive and transmit data under the control of processor 1110. Among them, in Figure 11 In this application, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors represented by processor 1110 and memory represented by memory 1120 together. The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be further described herein. The bus interface provides an interface. The transceiver 1100 can be multiple components, including a transmitter and a receiver, providing a unit for communicating with various other devices over a transmission medium, including wireless channels, wired channels, optical fibers, etc. For different user equipment, the user interface 1130 can also be an interface capable of connecting external or internal devices, including but not limited to keypads, displays, speakers, microphones, joysticks, etc.

[0320] The processor 1110 is responsible for managing the bus architecture and general processing, and the memory 1120 can store the data used by the processor 1110 when performing operations.

[0321] Optionally, the processor 1110 may be a CPU (Central Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or CPLD (Complex Programmable Logic Device), and the processor may also adopt a multi-core architecture.

[0322] The processor executes any of the methods provided in the embodiments of this application by calling a computer program stored in memory, according to the obtained executable instructions. The processor and memory may also be physically separated.

[0323] Figure 12 This is a second schematic diagram of the terminal device provided in the embodiments of this application. The terminal device can be an RU (Remote Executor) and can be used in a device-to-device (D2D) collaborative computing system. The D2D collaborative computing system also includes network devices and transmitting user units (TUs). The terminal device can be used to execute... Figure 6 The resource optimization methods shown include:

[0324] The network device receives the optimized computing resource value of the RU. The optimized computing resource value of the RU is determined by the network device based on the D2D collaborative computing task transmitted from the TU to the RU.

[0325] Receive D2D collaborative computing tasks transmitted by TU;

[0326] The D2D collaborative computing task is processed collaboratively based on the RU's optimized computing resource value to obtain the collaborative computing result.

[0327] like Figure 12 As shown, transceiver 1200 is used to receive and send data under the control of processor 1210. Among them, in Figure 12 In this application, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors represented by processor 1210 and memory represented by memory 1220 together. The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be further described herein. The bus interface provides an interface. The transceiver 1200 can be multiple components, including a transmitter and a receiver, providing a unit for communicating with various other devices over a transmission medium, including wireless channels, wired channels, optical fibers, etc. For different user equipment, the user interface 1230 can also be an interface capable of connecting external or internal devices, including but not limited to keypads, displays, speakers, microphones, joysticks, etc.

[0328] The processor 1210 is responsible for managing the bus architecture and general processing, and the memory 1220 can store the data used by the processor 1210 when performing operations.

[0329] Optionally, the processor 1210 may be a CPU (Central Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or CPLD (Complex Programmable Logic Device), and the processor may also adopt a multi-core architecture.

[0330] The processor executes any of the methods provided in the embodiments of this application by calling a computer program stored in memory, according to the obtained executable instructions. The processor and memory may also be physically separated.

[0331] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing a processor to execute the methods provided in the above embodiments, including:

[0332] Receive a charging indication signal sent by the TU for the D2D collaborative computing task. The charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task.

[0333] Determine the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal;

[0334] The resource optimization values ​​for the D2D collaborative computing task are determined based on the resource optimization model. These resource optimization values ​​include the transmit power optimization values ​​of the TU, the transmit power optimization values ​​of the network devices, and the computing resource optimization values ​​of the RU.

[0335] Send the optimized transmit power value of the TU and the optimized computational resource value of the RU to the TU and RU respectively.

[0336] Processor-readable storage media can be any available medium or data storage device that the processor can access, including but not limited to magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0337] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing a processor to execute the methods provided in the above embodiments, including:

[0338] Define the D2D collaborative computing task;

[0339] Send a charging indication signal to the network device for the D2D collaborative computing task. The charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task.

[0340] The optimized transmit power value of the TU sent by the receiving network device;

[0341] The D2D collaborative computing task is transmitted to the RU based on the TU's optimized transmit power value.

[0342] Processor-readable storage media can be any available medium or data storage device that the processor can access, including but not limited to magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0343] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing a processor to execute the methods provided in the above embodiments, including:

[0344] The network device receives the optimized computing resource value of the RU. The optimized computing resource value of the RU is determined by the network device based on the D2D collaborative computing task transmitted from the TU to the RU.

[0345] Receive D2D collaborative computing tasks transmitted by TU;

[0346] The D2D collaborative computing task is processed collaboratively based on the RU's optimized computing resource value to obtain the collaborative computing result.

[0347] Processor-readable storage media can be any available medium or data storage device that the processor can access, including but not limited to magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0348] On the other hand, this application provides a computer program product, which includes instructions that, when the computer program product is run on a computer, cause the computer to perform the steps of the above method. For details, please refer to the content of the above method embodiments, which will not be repeated here.

[0349] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0350] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable resource-optimized device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable resource-optimized device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0351] These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable resource-optimized device to operate in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including an instruction means, which is implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0352] These processors can execute instructions that can also be loaded onto a computer or other programmable resource-optimized device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0353] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A resource optimization method, characterized in that, The method is used for network devices in a device-to-device (D2D) collaborative computing system, wherein the D2D collaborative computing system further includes transmitting user (TU) and receiving user (RU); the method includes: The network device receives a charging indication signal sent by the TU for a D2D collaborative computing task, the charging indication signal being used to instruct the network device to charge the RU according to the D2D collaborative computing task; The resource optimization model corresponding to the D2D collaborative computing task is determined based on the charging indication signal. The resource optimization value corresponding to the D2D collaborative computing task is determined according to the resource optimization model. The resource optimization value includes the transmit power optimization value of the TU, the transmit power optimization value of the network device, and the computing resource optimization value of the RU. The optimized transmit power value of the TU and the optimized computational resource value of the RU are sent to the TU and the RU respectively; The charging indication signal includes task information characterizing the D2D collaborative computing task, including the input data size of the D2D collaborative computing task and the computing resources required by the D2D collaborative computing task. The step of determining the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal includes: Based on the input data size and the computing resources, a first utility model is determined to characterize the computing time saved by the TU, a second utility model is determined to characterize the energy saved by the TU, a third utility model is determined to characterize the fees paid by the TU to the network device, and a fourth utility model is determined to characterize the energy collected by the RU. Based on the first utility model, the second utility model, the third utility model, and the fourth utility model, a resource optimization model is determined to characterize the total utility of the network device, the TU, and the RU.

2. The resource optimization method according to claim 1, characterized in that, Also includes: The RU is charged according to the optimized transmit power value of the network device.

3. The resource optimization method according to claim 1, characterized in that, Determining the resource optimization value corresponding to the D2D collaborative computing task based on the resource optimization model includes: A first value range is determined, which is the range of values ​​for the transmission power of the TU; Within the first value range, the first maximum value of the total utility is determined by the resource optimization model, and the transmit power of the TU corresponding to the first maximum value is the optimized transmit power value of the TU.

4. The resource optimization method according to claim 1, characterized in that, Determining the resource optimization value corresponding to the D2D collaborative computing task based on the resource optimization model includes: A second value range is determined, which is the range of values ​​for the transmission power of the network device; Within the second value range, the second maximum value of the total utility is determined according to the resource optimization model, and the transmit power of the network device corresponding to the second maximum value is the optimized transmit power value of the network device.

5. The resource optimization method according to claim 1, characterized in that, Determining the resource optimization value corresponding to the D2D collaborative computing task based on the resource optimization model includes: A third value range is determined, wherein the third value range is the value range of the computing resources of the RU; Within the third value range, the third maximum value of the total utility is determined according to the resource optimization model, and the computational resources of the RU corresponding to the third maximum value are the computational resource optimization values ​​of the RU.

6. A resource optimization method, characterized in that, The method is used for a transmitting user (TU) in a device-to-device (D2D) collaborative computing system, wherein the D2D collaborative computing system further includes network devices and receiving user (RU); the method includes: Define the D2D collaborative computing task; Send a charging indication signal to the network device for the D2D collaborative computing task, the charging indication signal being used to instruct the network device to charge the RU according to the D2D collaborative computing task; Receive the optimized transmit power value of the TU sent by the network device; The D2D collaborative computing task is transmitted to the RU according to the optimized transmit power value of the TU; The optimized transmit power value of the TU is determined by the network device based on the following method: The resource optimization model corresponding to the D2D collaborative computing task is determined based on the charging indication signal. The resource optimization value corresponding to the D2D collaborative computing task is determined according to the resource optimization model. The resource optimization value includes the transmit power optimization value of the TU, the transmit power optimization value of the network device, and the computing resource optimization value of the RU. The charging indication signal includes task information characterizing the D2D collaborative computing task, including the input data size of the D2D collaborative computing task and the computing resources required by the D2D collaborative computing task. The step of determining the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal includes: Based on the input data size and the computing resources, a first utility model is determined to characterize the computing time saved by the TU, a second utility model is determined to characterize the energy saved by the TU, a third utility model is determined to characterize the fees paid by the TU to the network device, and a fourth utility model is determined to characterize the energy collected by the RU. Based on the first utility model, the second utility model, the third utility model, and the fourth utility model, a resource optimization model is determined to characterize the total utility of the network device, the TU, and the RU.

7. A resource optimization method, characterized in that, The method is used for a receiving user RU in a device-to-device (D2D) collaborative computing system, wherein the D2D collaborative computing system further includes network devices and transmitting user TUs; the method includes: The network device receives the optimized computing resource value of the RU sent by the network device. The optimized computing resource value of the RU is the optimized value determined by the network device based on the D2D collaborative computing task transmitted from the TU to the RU. Receive the D2D collaborative computing task transmitted by the TU; The D2D collaborative computing task is processed collaboratively based on the RU's optimized computing resource value to obtain the collaborative computing result; The optimized computational resource value of the RU is determined by the network device based on the following method: The resource optimization model corresponding to the D2D collaborative computing task is determined based on the charging indication signal sent by the TU for the D2D collaborative computing task. The resource optimization value corresponding to the D2D collaborative computing task is determined according to the resource optimization model. The resource optimization value includes the transmit power optimization value of the TU, the transmit power optimization value of the network device, and the computing resource optimization value of the RU. The charging indication signal includes task information characterizing the D2D collaborative computing task, including the input data size of the D2D collaborative computing task and the computing resources required by the D2D collaborative computing task. The step of determining the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal includes: Based on the input data size and the computing resources, a first utility model is determined to characterize the computing time saved by the TU, a second utility model is determined to characterize the energy saved by the TU, a third utility model is determined to characterize the fees paid by the TU to the network device, and a fourth utility model is determined to characterize the energy collected by the RU. Based on the first utility model, the second utility model, the third utility model, and the fourth utility model, a resource optimization model is determined to characterize the total utility of the network device, the TU, and the RU.

8. A network device, characterized in that, The network device is used for a device-to-device (D2D) collaborative computing system, which further includes a transmitting user (TU) and a receiving user (RU); the network device includes: a memory, a transceiver, and a processor. A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations: The network device receives a charging indication signal sent by the TU for a D2D collaborative computing task, the charging indication signal being used to instruct the network device to charge the RU according to the D2D collaborative computing task; The resource optimization model corresponding to the D2D collaborative computing task is determined based on the charging indication signal. The resource optimization value corresponding to the D2D collaborative computing task is determined according to the resource optimization model. The resource optimization value includes the transmit power optimization value of the TU, the transmit power optimization value of the network device, and the computing resource optimization value of the RU. The optimized transmit power value of the TU and the optimized computational resource value of the RU are sent to the TU and the RU respectively; The charging indication signal includes task information characterizing the D2D collaborative computing task, including the input data size of the D2D collaborative computing task and the computing resources required by the D2D collaborative computing task. The step of determining the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal includes: Based on the input data size and the computing resources, a first utility model is determined to characterize the computing time saved by the TU, a second utility model is determined to characterize the energy saved by the TU, a third utility model is determined to characterize the fees paid by the TU to the network device, and a fourth utility model is determined to characterize the energy collected by the RU. Based on the first utility model, the second utility model, the third utility model, and the fourth utility model, a resource optimization model is determined to characterize the total utility of the network device, the TU, and the RU.

9. A transmitting user TU, characterized in that, The transmitting user TU is used in a device-to-device (D2D) collaborative computing system, which further includes network equipment and receiving user RU; the transmitting user TU includes: a memory, a transceiver, and a processor. A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations: Define the D2D collaborative computing task; Send a charging indication signal to the network device for the D2D collaborative computing task, the charging indication signal being used to instruct the network device to charge the RU according to the D2D collaborative computing task; Receive the optimized transmit power value of the TU sent by the network device; The D2D collaborative computing task is transmitted to the RU according to the optimized transmit power value of the TU; The optimized transmit power value of the TU is determined by the network device based on the following method: The resource optimization model corresponding to the D2D collaborative computing task is determined based on the charging indication signal. The resource optimization value corresponding to the D2D collaborative computing task is determined according to the resource optimization model. The resource optimization value includes the transmit power optimization value of the TU, the transmit power optimization value of the network device, and the computing resource optimization value of the RU. The charging indication signal includes task information characterizing the D2D collaborative computing task, including the input data size of the D2D collaborative computing task and the computing resources required by the D2D collaborative computing task. The step of determining the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal includes: Based on the input data size and the computing resources, a first utility model is determined to characterize the computing time saved by the TU, a second utility model is determined to characterize the energy saved by the TU, a third utility model is determined to characterize the fees paid by the TU to the network device, and a fourth utility model is determined to characterize the energy collected by the RU. Based on the first utility model, the second utility model, the third utility model, and the fourth utility model, a resource optimization model is determined to characterize the total utility of the network device, the TU, and the RU.

10. A receiving user RU, characterized in that, The receiving user RU is used in a device-to-device (D2D) collaborative computing system, which also includes network equipment and transmitting user TUs; the receiving user RU includes: a memory, a transceiver, and a processor. A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations: The network device receives the optimized computing resource value of the RU sent by the network device. The optimized computing resource value of the RU is the optimized value determined by the network device based on the D2D collaborative computing task transmitted from the TU to the RU. Receive the D2D collaborative computing task transmitted by the TU; The D2D collaborative computing task is processed collaboratively based on the RU's optimized computing resource value to obtain the collaborative computing result; The optimized computational resource value of the RU is determined by the network device based on the following method: The resource optimization model corresponding to the D2D collaborative computing task is determined based on the charging indication signal sent by the TU for the D2D collaborative computing task. The resource optimization value corresponding to the D2D collaborative computing task is determined according to the resource optimization model. The resource optimization value includes the transmit power optimization value of the TU, the transmit power optimization value of the network device, and the computing resource optimization value of the RU. The charging indication signal includes task information characterizing the D2D collaborative computing task, including the input data size of the D2D collaborative computing task and the computing resources required by the D2D collaborative computing task. The step of determining the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal includes: Based on the input data size and the computing resources, a first utility model is determined to characterize the computing time saved by the TU, a second utility model is determined to characterize the energy saved by the TU, a third utility model is determined to characterize the fees paid by the TU to the network device, and a fourth utility model is determined to characterize the energy collected by the RU. Based on the first utility model, the second utility model, the third utility model, and the fourth utility model, a resource optimization model is determined to characterize the total utility of the network device, the TU, and the RU.

11. A resource optimization device, characterized in that, The resource optimization device is used in the network equipment of the device-to-device (D2D) collaborative computing system, which also includes a transmitting user (TU) and a receiving user (RU). The resource optimization device includes: A signal receiving unit is configured to receive a charging indication signal sent by the TU for a D2D collaborative computing task, wherein the charging indication signal is used to instruct the network device to charge the RU according to the D2D collaborative computing task; The model determination unit is used to determine the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal. The optimization value determination unit is used to determine the resource optimization value corresponding to the D2D collaborative computing task according to the resource optimization model. The resource optimization value includes the transmit power optimization value of the TU, the transmit power optimization value of the network device, and the computing resource optimization value of the RU. An optimization value sending unit is used to send the optimized transmit power value of the TU and the optimized computing resource value of the RU to the TU and the RU respectively; The charging indication signal includes task information characterizing the D2D collaborative computing task, including the input data size of the D2D collaborative computing task and the computing resources required by the D2D collaborative computing task. The step of determining the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal includes: Based on the input data size and the computing resources, a first utility model is determined to characterize the computing time saved by the TU, a second utility model is determined to characterize the energy saved by the TU, a third utility model is determined to characterize the fees paid by the TU to the network device, and a fourth utility model is determined to characterize the energy collected by the RU. Based on the first utility model, the second utility model, the third utility model, and the fourth utility model, a resource optimization model is determined to characterize the total utility of the network device, the TU, and the RU.

12. A resource optimization device, characterized in that, The resource optimization device is used for the transmitting user (TU) of the device-to-device (D2D) collaborative computing system, which also includes network devices and receiving user (RU). The resource optimization device includes: Task determination unit, used to determine D2D collaborative computing tasks; A signal transmitting unit is configured to send a charging indication signal to the network device for the D2D collaborative computing task, the charging indication signal being used to instruct the network device to charge the RU according to the D2D collaborative computing task; The first optimization value receiving unit is used to receive the TU transmit power optimization value sent by the network device; The task transmission unit is used to transmit the D2D collaborative computing task to the RU according to the optimized transmit power value of the TU; The optimized transmit power value of the TU is determined by the network device based on the following method: The resource optimization model corresponding to the D2D collaborative computing task is determined based on the charging indication signal. The resource optimization value corresponding to the D2D collaborative computing task is determined according to the resource optimization model. The resource optimization value includes the transmit power optimization value of the TU, the transmit power optimization value of the network device, and the computing resource optimization value of the RU. The charging indication signal includes task information characterizing the D2D collaborative computing task, including the input data size of the D2D collaborative computing task and the computing resources required by the D2D collaborative computing task. The step of determining the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal includes: Based on the input data size and the computing resources, a first utility model is determined to characterize the computing time saved by the TU, a second utility model is determined to characterize the energy saved by the TU, a third utility model is determined to characterize the fees paid by the TU to the network device, and a fourth utility model is determined to characterize the energy collected by the RU. Based on the first utility model, the second utility model, the third utility model, and the fourth utility model, a resource optimization model is determined to characterize the total utility of the network device, the TU, and the RU.

13. A resource optimization device, characterized in that, The resource optimization device is used in the receiving user RU of the device-to-device (D2D) collaborative computing system, which further includes network equipment and transmitting user TU; the resource optimization device includes: The second optimization value receiving unit is used to receive the computational resource optimization value of the RU sent by the network device. The computational resource optimization value of the RU is the optimization value determined by the network device based on the D2D collaborative computing task transmitted from the TU to the RU. A task receiving unit is used to receive the D2D collaborative computing task transmitted by the TU. The collaborative computing unit is used to perform collaborative computing processing on the D2D collaborative computing task according to the computing resource optimization value of RU, and obtain the collaborative computing result; The optimized computational resource value of the RU is determined by the network device based on the following method: The resource optimization model corresponding to the D2D collaborative computing task is determined based on the charging indication signal sent by the TU for the D2D collaborative computing task. The resource optimization value corresponding to the D2D collaborative computing task is determined according to the resource optimization model. The resource optimization value includes the transmit power optimization value of the TU, the transmit power optimization value of the network device, and the computing resource optimization value of the RU. The charging indication signal includes task information characterizing the D2D collaborative computing task, including the input data size of the D2D collaborative computing task and the computing resources required by the D2D collaborative computing task. The step of determining the resource optimization model corresponding to the D2D collaborative computing task based on the charging indication signal includes: Based on the input data size and the computing resources, a first utility model is determined to characterize the computing time saved by the TU, a second utility model is determined to characterize the energy saved by the TU, a third utility model is determined to characterize the fees paid by the TU to the network device, and a fourth utility model is determined to characterize the energy collected by the RU. Based on the first utility model, the second utility model, the third utility model, and the fourth utility model, a resource optimization model is determined to characterize the total utility of the network device, the TU, and the RU.

14. A device-to-device D2D collaborative computing system, characterized in that, The D2D collaborative computing system includes network equipment, transmitting user TU, and receiving user RU; Wherein, the network device is used to execute the resource optimization method according to any one of claims 1 to 5; the TU is used to execute the resource optimization method according to claim 6; and the RU is used to execute the resource optimization method according to claim 7.

15. A processor-readable storage medium, characterized in that, The processor-readable storage medium stores a computer program that causes the processor to perform the method of any one of claims 1 to 5, or the method of claim 6, or the method of claim 7.

Images