Computing resource allocation method and device, electronic device, and storage medium

Abstract

The present disclosure relates to the field of computing resource allocation, and provides a computing resource allocation method and device, electronic device, and storage medium. The method includes: determining the utility function and its constraints corresponding to the terminal device based on the edge server selection strategy according to the communication energy consumption between the terminal device and each edge server, and the computing energy consumption corresponding to the terminal device, the privacy leakage risk assessment index and its coefficient; determining the edge server selection strategy of each terminal device when its utility is maximum based on the utility function; determining the utility increment of each terminal device under the edge server selection strategy when its utility is maximum relative to the utility increment under the current edge server selection strategy; updating the current edge server selection strategy of the terminal device with the largest utility increment to the edge server selection strategy when its utility is maximum. The present disclosure can reduce the overall energy consumption of the health monitoring system, improve energy efficiency and response speed, enhance privacy protection, and improve user experience.

Description

Technical Field

[0001] The present disclosure relates to the technical field of computing resource allocation, and in particular to a computing resource allocation method and device, electronic equipment, and storage medium. Background Art

[0002] The Medical Internet of Things has become a transformative force in the healthcare sector, combining medical devices and applications to create a highly personalized and efficient medical ecosystem. The health monitoring system is a key application of the Medical Internet of Things. In the health monitoring system, wearable devices on the patient continuously track the patient's biomedical data and transmit the data to the patient's terminal device. The terminal device acts as a gateway to forward the relevant data to the medical service provider, enabling the medical service provider to continuously monitor the patient's health status at minimal cost. In addition, the health monitoring system can also expand the patient's range of activities and significantly improve the patient's quality of life.

[0003] Due to the limitation of communication resources and the insufficient computing power of patients' own devices, the existing health monitoring system faces major challenges, and the efficiency and responsiveness of the system are limited to a certain extent. The introduction of Mobile Edge Computing (MEC) and the sixth generation of wireless networks provides a solution to overcome these obstacles. The integration of mobile edge computing and the sixth generation of wireless networks in the medical Internet of Things ecosystem may become the cornerstone of the next generation of medical technology.

[0004] In the health monitoring system based on the medical Internet of Things, the allocation of computing resources is a key issue, which significantly affects the overall performance of the system. The prior art proposes a module placement method using a classification regression tree algorithm, which can effectively allocate computing tasks and optimize authentication, confidentiality, integrity, availability, capacity, speed and cost at the same time, but ignores the standby time of users' mobile devices and the energy consumption of devices. The prior art also uses the Stackelberg game as an incentive mechanism to influence users' offloading decisions to balance the relationship between minimizing the energy consumption of mobile edge computing servers and ensuring the quality of user experience, but ignores the needs of medical monitoring systems. The prior art also proposes a cooperative bargaining game for optimizing the allocation of wireless channel resources, and makes computing offloading decisions through a decentralized non-cooperative game to minimize the energy consumption of the entire system. When allocating resources, it considers aspects such as medical urgency, information timeliness and the overall energy consumption of the system including the server, but ignores the risk of user privacy leakage and the standby time of mobile devices. Therefore, how to balance the optimization of user device energy efficiency and strict privacy protection when allocating computing resources has become an urgent problem to be solved in this field. Summary of the invention

[0005] The present disclosure aims to solve at least one of the problems existing in the prior art and provides a computing resource allocation method and device, an electronic device, and a storage medium.

[0006] In one aspect of the present disclosure, a computing resource allocation method is provided, which is applied to a health monitoring system, wherein the health monitoring system includes a plurality of edge servers, a plurality of terminal devices respectively communicatively connected to each of the edge servers, and a plurality of wearable devices respectively communicatively connected to each of the terminal devices;

[0007] The computing resource allocation method comprises:

[0008] According to the communication energy consumption between the terminal device and each of the edge servers, as well as the computing energy consumption corresponding to the terminal device, the privacy leakage risk assessment index and its coefficient, the utility function and its constraint conditions corresponding to the terminal device are determined based on the edge server selection strategy;

[0009] Based on the utility function, determining the edge server selection strategy corresponding to each terminal device when its utility is maximum;

[0010] Determine respectively the utility increment of each terminal device under the edge server selection strategy corresponding to the maximum utility of the terminal device relative to the utility increment under the current edge server selection strategy;

[0011] The current edge server selection policy where the terminal device with the largest utility increment is located is updated to the edge server selection policy corresponding to when its utility is the largest, so as to allocate computing resources of the edge server to the terminal device.

[0012] Optionally, determining the utility function and its constraint conditions corresponding to the terminal device based on the edge server selection strategy according to the communication energy consumption between the terminal device and each edge server, as well as the computing energy consumption corresponding to the terminal device, the privacy leakage risk assessment index and its coefficient, includes:

[0013] According to formula 1, the utility function is constructed:

[0014] Formula 1;

[0015] in, represents the edge server selection strategy corresponding to the i-th terminal device; Indicates the edge server selection strategy corresponding to other terminal devices other than the i-th terminal device; represents the utility function corresponding to the i-th terminal device; represents the cost function corresponding to the i-th terminal device and is expressed as:

[0016] ;

[0017] in, Represents a set of edge server selection strategies corresponding to all terminal devices; represents the privacy leakage risk assessment index corresponding to the i-th terminal device; express The coefficient of Indicates an indicator function. When the expression in the brackets is true The function value is 1, and the expression in the brackets is false. The function value of is 0; represents the communication energy consumption between the i-th terminal device and the edge server; represents the total energy consumed by the i-th terminal device to process computing tasks;

[0018] According to Formula 2, the constraints of the utility function are determined:

[0019] Formula 2;

[0020] Wherein, N represents the number of terminal devices; Represents the set of all edge servers.

[0021] Optionally, the utility increment of the i-th terminal device under the edge server selection strategy corresponding to the maximum utility of the i-th terminal device relative to the utility increment under the current edge server selection strategy is expressed as:

[0022] ;

[0023] in, represents the utility increment corresponding to the i-th terminal device at time t; The edge server selection strategy corresponding to the i-th terminal device when its utility is maximum is: At the same time, the utility function of other terminal devices keeps their edge server selection strategy unchanged; Indicates that the i-th terminal device selects the corresponding edge server strategy as At the same time, the utility function of other terminal devices keeps their edge server selection strategy unchanged.

[0024] Optionally, the privacy leakage risk assessment index corresponding to the i-th terminal device It is expressed as:

[0025] ;

[0026] Where j represents the number of the wearable device that is connected to the i-th terminal device, j=1,2,…, M , MRepresents the total number of wearable devices that are connected to the i-th terminal device; Indicates the calculated position corresponding to the jth wearable device that is connected to the ith terminal device. Its value 0 indicates that the calculated position is local to the terminal device, and its value 1 indicates that the calculated position follows the terminal device selection. Represents the computing task corresponding to the jth wearable device that is connected to the i-th terminal device Privacy leakage risk assessment index.

[0027] Optionally, the communication energy consumption between the i-th terminal device and the edge server is It is expressed as:

[0028] ;

[0029] in, represents the transmission power of the i-th terminal device; represents the transmission delay corresponding to the i-th terminal device and is expressed as:

[0030] ;

[0031] in, represents the total task size uploaded by the i-th terminal device to the edge server and is expressed as:

[0032] ;

[0033] in, Represents a computing task The data size; Represents a computing task The calculation result data size of

[0034] represents the transmission rate between the i-th terminal device and its selected edge server and is expressed as:

[0035] ;

[0036] Where k represents the number of the edge server and k=1,2,…, K , K Indicates the total number of edge servers; represents the channel gain between the i-th terminal device and the k-th edge server; Indicates the terminal device number and The value of is 1 to N, which is different from i; Indicates The edge server selection strategy corresponding to each terminal device; Indicates The transmission power of each terminal device; represents the channel gain between the i-th terminal device and the k-th edge server; B represents the channel bandwidth; Represents the background noise power.

[0037] Optionally, the total energy consumed by the i-th terminal device to process the computing task It is expressed as:

[0038] ;

[0039] in, represents the computing power of the i-th terminal device; Indicates the computing power of the i-th terminal device; represents the number of CPU cycles required for the i-th terminal device to perform the computing task locally on its terminal device, and is expressed as:

[0040] ;

[0041] in, Represents processing computing tasks The number of CPU cycles required.

[0042] Optionally, the computing resource allocation method further includes:

[0043] For each of the terminal devices, determine whether the number of times it selects the terminal device to perform computing tasks locally exceeds a preset threshold. If so, set the computing location corresponding to the wearable device with the highest security level of its communication connection to the local terminal device.

[0044] Another aspect of the present disclosure provides a computing resource allocation device, which is applied to a health monitoring system, wherein the health monitoring system includes a plurality of edge servers, a plurality of terminal devices respectively communicatively connected to each of the edge servers, and a plurality of wearable devices respectively communicatively connected to each of the terminal devices;

[0045] The computing resource allocation device comprises:

[0046] A construction module is used to determine the utility function and its constraint conditions corresponding to the terminal device based on the edge server selection strategy according to the communication energy consumption between the terminal device and each edge server, as well as the computing energy consumption corresponding to the terminal device, the privacy leakage risk assessment index and its coefficient;

[0047] A first determination module, configured to determine, based on the utility function, the edge server selection strategy corresponding to each of the terminal devices when the utility of the terminal device is maximum;

[0048] A second determination module is used to respectively determine the utility increment of each terminal device under the edge server selection strategy corresponding to the maximum utility of the terminal device relative to the utility increment under the current edge server selection strategy;

[0049] The updating module is used to update the current edge server selection policy where the terminal device with the largest utility increment is located to the edge server selection policy corresponding to the maximum utility, so as to allocate the computing resources of the edge server to the terminal device.

[0050] Another aspect of the present disclosure provides an electronic device, including:

[0051] at least one processor; and,

[0052] a memory communicatively connected to at least one processor; wherein,

[0053] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the computing resource allocation method described above.

[0054] Another aspect of the present disclosure provides a computer-readable storage medium storing a computer program, which implements the computing resource allocation method described above when executed by a processor.

[0055] Compared with the prior art, the present invention can automatically adjust the terminal device's demand for edge server resources, reduce the overall energy consumption of the health monitoring system, and focus on reducing the energy consumption of the terminal device as one of the main goals, effectively extending the standby time of the terminal device; for sensitive user health and medical information, the risk of privacy leakage is taken into account, effectively reducing the risk of user privacy leakage and ensuring the security of user information; the task allocation between the local computing of the terminal device and the edge server can be dynamically adjusted, and the edge server selection strategy can be refined to each task to achieve the optimal balance between reducing energy consumption and protecting privacy, thereby improving the overall performance of the system and user experience, improving the response speed of medical services, and increasing user trust and satisfaction with the health monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0057] Figure 1 A schematic diagram of a communication connection relationship of a health monitoring system provided in one embodiment of the present disclosure;

[0058] Figure 2 A flowchart of a computing resource allocation method provided in another embodiment of the present disclosure;

[0059] Figure 3 A schematic diagram of a strategy selection result in Example 1 provided in another embodiment of the present disclosure;

[0060] Figure 4 A schematic diagram of dynamic changes in the utility of a terminal device in Example 2 provided in another embodiment of the present disclosure;

[0061] Figure 5 A schematic diagram of the structure of a computing resource allocation device provided in another embodiment of the present disclosure;

[0062] Figure 6 A schematic structural diagram of an electronic device provided in another embodiment of the present disclosure. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the embodiments of the present disclosure will be described in detail below in conjunction with the accompanying drawings. However, it can be understood by those skilled in the art that in each embodiment of the present disclosure, many technical details are proposed in order to enable readers to better understand the present disclosure. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed for protection in the present disclosure can also be implemented. The division of the following embodiments is for the convenience of description and should not constitute any limitation on the specific implementation of the present disclosure. The various embodiments can be combined and referenced with each other without contradiction.

[0064] One embodiment of the present disclosure relates to a computing resource allocation method, which is applied to a health monitoring system. Figure 1 As shown, the health monitoring system includes multiple edge servers, multiple terminal devices that are respectively connected to each edge server, and several wearable devices that are respectively connected to each terminal device. Among them, each wearable device can be connected to the terminal device through a wireless link, so that each wearable device and the terminal device form an internal wireless body area network (intra-WBANs). Multiple terminal devices can also form an external wireless body area network (extra-WBANs) with the corresponding edge server, so that the edge server sends the wearable device monitoring data collected by the terminal device to the medical service provider through the cloud server. Among them, multiple terminal devices can be connected to the corresponding edge server through a wireless link, each edge server can be connected to the cloud server through a wired link, and the cloud server can be connected to the medical service provider through a wired link.

[0065] Each wearable device is used to continuously monitor the user's key health indicators such as heart rate, blood pressure, blood sugar and other biomarkers. The terminal device is used to store information, process data, and act as a gateway. The terminal device can decide whether to perform computing tasks locally or offload computing tasks to the edge server. In other words, the terminal device can aggregate and process the monitoring data collected by the user's wearable device, and send the relevant medical report or data required by the edge server to the edge server. The edge server then sends the relevant medical report or monitoring data to the cloud server and medical service providers, thereby enabling medical service providers to continuously monitor the user's biomedical data at the lowest cost.

[0066] Utilizing the high speed and low latency capabilities of the 6th generation mobile networks, terminal devices are able to efficiently transmit the collected user health data to nearby edge servers. The edge servers are strategically located to perform real-time data processing, significantly reducing latency compared to traditional cloud-based health monitoring systems. The edge servers can also immediately use the processed data to gain health insights, or send the data to medical providers for further analysis and action. The health monitoring system provided by this embodiment can not only quickly respond to the user's medical service needs, but also improve the overall response speed and accuracy of the health monitoring system, and enhance the responsiveness and accuracy of medical intervention.

[0067] Resource allocation for edge servers presents significant challenges. Therefore, the computational resource allocation method provided by the present embodiment recognizes the acute energy sensitivity of end devices by focusing on minimizing energy usage at the end device level. In addition, biomedical data collected by wearable devices raises significant privacy issues, requiring a strategy that cleverly integrates privacy risk management. Therefore, when allocating resources for edge servers, it is necessary to cleverly balance optimizing energy efficiency of end devices with strict privacy protection.

[0068] like Figure 2 As shown, the computing resource allocation method includes:

[0069] Step S210, according to the communication energy consumption between the terminal device and each edge server, as well as the computing energy consumption corresponding to the terminal device, the privacy leakage risk assessment index and its coefficient, the utility function corresponding to the terminal device and its constraints are determined based on the edge server selection strategy.

[0070] Specifically, the information processed and transmitted by the health monitoring system is medical information in nature, which raises significant concerns about user privacy. The data collected by wearable devices provides deep insights into personal health status. From the perspective of privacy security alone, users prefer to process tasks locally on terminal devices rather than uploading the collected data to edge servers. Local computing on terminal devices allows users to reduce the risk of leakage of data collected by wearable devices while transmitting necessary medical reports directly to medical service providers. Therefore, this embodiment establishes a privacy leakage risk assessment indicator to systematically evaluate the possibility of privacy leakage during task transmission.

[0071] According to IEEE standard 802.15.6, wearable devices will select a security level when transmitting messages. The security levels include three levels: Level 0 (unencrypted), Level 1 (authenticated but not encrypted), and Level 2 (authenticated and encrypted). All tasks of wearable devices can be classified into the above three security levels.

[0072] Defining indicator functions , when the expression in the brackets is true, The function value is 1 when the expression in the brackets is false The function value of is 0. Let the computing task corresponding to the jth wearable device connected to the i-th terminal device be The privacy leakage risk assessment index is ,but It can be calculated by the following formula: ,in, s =0,1,2 indicates the security level, ∈[0 , ∞) indicates the security level s The coefficient of Represents a computing task The amount of information. The value of s j represents the number of the wearable device connected to the i-th terminal device, j=1,2,…, M , M Represents the total number of wearable devices that are connected to the i-th terminal device.

[0073] In the external wireless body area network, each terminal device can only communicate with one edge server at a time. The edge server selection strategy corresponding to the i-th terminal device is recorded as , then when the number of terminal devices is N (N is a positive integer), the set of edge server selection strategies corresponding to all terminal devices is It can be expressed as .gather It contains the strategic choices made by each end device in selecting an edge server for communication purposes. = k It means that the i-th terminal device selects the k-th edge server for task offloading. Where k represents the number of the edge server and k=1,2,…, K , K represents the total number of edge servers and K Is a positive integer. =0 means that the i-th terminal device prefers to process tasks locally. The transmission rate between the i-th terminal device and its selected edge server It can be calculated as follows: .in, Represents the transmission power of the i-th terminal device. represents the channel gain between the i-th terminal device and the k-th edge server. Indicates the terminal device number and The value of is a value different from i from 1 to N. Indicates The edge server selection strategy corresponding to each terminal device. Indicates The transmission power of each terminal device. represents the channel gain between the i-th terminal device and the k-th edge server. B represents the channel bandwidth. Represents the background noise power.

[0074] In practical applications, low-power operation of wearable devices is critical, which requires a design that can accommodate both minimal battery size and infrequent charging cycles. Low energy consumption is critical to ensuring the long-term operation and effectiveness of wearable devices deployed in various medical monitoring scenarios. In the health monitoring system provided in this embodiment, although the energy consumption of the wearable device for information collection and task generation remains unchanged, when it sends data to the terminal device, the transmission energy consumption generated makes the overall energy consumption of the wearable device variable. The energy consumption within the external wireless body area network is mainly characterized by two key elements: the communication energy consumption between the terminal device and the edge server, and the computing energy consumption.

[0075] The total task size uploaded by the i-th terminal device to the edge server It can be calculated by the following formula: .in, Represents a computing task The data size. Represents a computing task The calculation result data size. It is a binary variable, which indicates the calculated position corresponding to the jth wearable device communicating with the i-th terminal device. Its value is 0, which indicates that the calculated position is local to the terminal device, and its value is 1, which indicates that the calculated position follows the terminal device selection.

[0076] Transmission delay corresponding to the i-th terminal device It can be calculated by the following formula: The communication energy consumption between the i-th terminal device and the edge server is It can be calculated by the following formula: .

[0077] In the health monitoring system provided in this embodiment, computing tasks can be processed by terminal devices or edge servers. After the computing tasks are processed, the result data can be transmitted to the cloud server and forwarded to the medical service provider through the cloud server. This workflow ensures that computing tasks that are critical to health monitoring and analysis can be effectively executed, making full use of the distributed computing resources available in the network.

[0078] Will handle computational tasks The number of CPU cycles required is recorded as , then the number of CPU cycles required for the i-th terminal device to perform the computing task locally on its terminal device is It can be calculated by the following formula: .in, Represents a computing task The number of CPU cycles required. The number of CPU cycles required by the i-th terminal device to offload the computing task to the edge server for processing It can be calculated by the following formula: .

[0079] The computing power of the i-th terminal device and the computing power of the k-th edge server can be respectively expressed as and Assuming that the edge server allocates equal computing resources to each terminal device, the available computing power obtained by the i-th terminal device from the edge server is It can be calculated by the following formula: ,in, The value of is 1, 2,…, N.

[0080] The total energy consumed by the i-th terminal device to process computing tasks It can be calculated by the following formula: .in, Represents the computing power of the i-th terminal device.

[0081] The total energy consumed by the edge server to process the computing task from the i-th terminal device It can be calculated by the following formula: .in, Indicates the computing power of the edge server.

[0082] After collecting all relevant data, the terminal device faces a key decision: whether to offload the task to the edge server or process it locally on the terminal device. Most existing technologies focus on reducing the total energy consumption of the system, but the computing resource allocation method provided by this embodiment turns to reducing the operating expenses of the terminal device and taking into account the risk of privacy leakage. This embodiment has two considerations for this: first, given the popularity of terminal devices carried by users, the energy consumption of the terminal device itself is prioritized over the energy demand of the edge server; second, the user's health and medical data is inherently confidential, and each task processes a large amount of user personal information, which makes processing tasks directly on the edge server a less desirable option. In contrast, tasks are processed locally on the terminal device and then the relevant reports are transmitted to the medical service provider, which can effectively enhance user privacy protection. Therefore, this embodiment must focus on user-related privacy impacts in the decision-making process regarding task processing.

[0083] Since one terminal device corresponds to one user, the cost function corresponding to the i-th terminal device is It can be expressed as .in, It represents the privacy leakage risk assessment index corresponding to the i-th terminal device, which can be expressed as . express The coefficient of .

[0084] Based on the above content, illustratively, step S210 includes: constructing a utility function according to formula 1, and determining the constraint conditions of the utility function according to formula 2.

[0085] Formula 1.

[0086] in, Represents the utility function corresponding to the i-th terminal device. Indicates the edge server selection strategy corresponding to other terminal devices except the i-th terminal device.

[0087] Formula 2.

[0088] in, Represents the set of all edge servers.

[0089] Step S220: Based on the utility function, determine the edge server selection strategy corresponding to each terminal device when its utility is maximum.

[0090] Specifically, the utility function corresponding to the i-th terminal device expressed in Formula 1 is obtained: Afterwards, according to , determine the edge server selection strategy corresponding to the i-th terminal device when its utility is maximum, and then obtain the edge server selection strategy corresponding to each terminal device when its utility is maximum.

[0091] Step S230 , respectively determining the utility increment of each terminal device under the edge server selection strategy corresponding to the maximum utility of the terminal device relative to the utility increment under the current edge server selection strategy.

[0092] Specifically, in order to solve the optimization challenges related to the allocation of edge server resources, the computing resource allocation method provided in this embodiment is actually an edge server resource dynamic allocation algorithm. The algorithm first sets a utility function for each terminal device, assuming that all terminal devices initially perform all tasks locally, thereby establishing a baseline for system operation. Then, the algorithm starts an iterative process until the system reaches a Nash equilibrium point. During the algorithm iteration process, each iteration includes a policy evaluation, in which the incremental utility benefits of potential edge server resource reallocation are calculated and compared with the existing edge server selection strategy. In this iteration, the terminal device that obtains the maximum utility benefit updates its edge server selection strategy accordingly. Once the incremental utility benefit of the terminal device stabilizes to zero, that is, the Nash equilibrium point is reached, the algorithm will converge. Therefore, step S230 needs to determine the utility increment of each terminal device under the edge server selection strategy corresponding to its maximum utility relative to its current edge server selection strategy, so as to determine the terminal device with the largest utility increment.

[0093] Exemplarily, the utility increment of the i-th terminal device under the edge server selection strategy corresponding to the maximum utility of the i-th terminal device relative to the current edge server selection strategy is expressed as:

[0094] .

[0095] in, Represents the utility increment corresponding to the i-th terminal device at time t. It represents the edge server selection strategy corresponding to the i-th terminal device when its utility is maximum. The edge server selection strategy corresponding to the i-th terminal device when its utility is maximum is: At the same time, the utility function of other terminal devices keeps their edge server selection strategy unchanged. Indicates that the i-th terminal device selects the corresponding edge server strategy as At the same time, the utility function of other terminal devices keeps their edge server selection strategy unchanged.

[0096] Step S240 , updating the current edge server selection policy of the terminal device with the largest utility increment to the edge server selection policy corresponding to the maximum utility, so as to allocate computing resources of the edge server to the terminal device.

[0097] By updating the current edge server selection policy of the terminal device with the largest utility increment to the corresponding edge server selection policy when its utility is the largest, the resource utilization of the edge server can be effectively improved, thereby effectively improving the computing efficiency.

[0098] Exemplarily, the computing resource allocation method also includes: for each terminal device, determining whether the number of times it selects the terminal device locally to perform computing tasks exceeds a preset threshold; if so, setting the computing location corresponding to the wearable device with the highest security level of its communication connection to the local terminal device.

[0099] Specifically, in order to optimize the local computing cost of the terminal device while protecting user privacy, the computing resource allocation method provided in this embodiment also includes a dynamic adjustment mechanism. When the number of times the terminal device executes computing tasks locally exceeds a preset threshold, the dynamic adjustment mechanism will automatically modify the terminal device's request for edge server computing, and isolate the tasks with higher privacy leakage risks to be processed locally on the terminal device, which is manifested as setting the computing location corresponding to the wearable device with the highest security level that is connected to the terminal device for communication to the local terminal device, and excluding it from the decision-making process of whether edge computing is needed. Conversely, when the terminal device continues to obtain computing resources from the edge server within the preset threshold, the dynamic adjustment mechanism encourages more tasks to be included in the scope of potential outsourcing to the edge server. That is, this strategic algorithm strikes a balance between privacy protection and computing efficiency, guiding the system to achieve the best operating balance.

[0100] Compared with the prior art, the computing resource allocation method provided by the embodiment of the present disclosure can automatically adjust the terminal device's demand for edge server resources, reduce the overall energy consumption of the health monitoring system, and focus on reducing the energy consumption of the terminal device as one of the main goals, effectively extending the standby time of the terminal device. For sensitive user health and medical information, the computing resource allocation method provided by the embodiment of the present disclosure takes the risk of privacy leakage into account, effectively reduces the risk of user privacy leakage, and ensures the security of user information. In addition, the computing resource allocation method provided by the embodiment of the present disclosure can dynamically adjust the task allocation between the local computing of the terminal device and the edge server, and refine the edge server selection strategy to each task to achieve the optimal balance between reducing energy consumption and protecting privacy, thereby improving the overall performance and user experience of the system, improving the response speed of medical services, and increasing users' trust and satisfaction with the health monitoring system.

[0101] In order to enable those skilled in the art to better understand the above implementation, the edge server resource dynamic allocation algorithm involved in the computing resource allocation method is described below with a specific example.

[0102] The algorithm for dynamic allocation of edge server resources is as follows:

[0103] Input: a set of N terminal devices, a set of wearable devices corresponding to each terminal device, and a set of edge servers.

[0104] Output: The set of edge server selection policies for each end device.

[0105] initialization:

[0106] 1. Set the initial time slot t=0.

[0107] 2. For each terminal device i:

[0108] —Initialize edge server selection strategy is 0.

[0109] — Initialize counter is 0.

[0110] —For each end device i, the jth wearable device will calculate the location Initialized to 1.

[0111] Policy Update:

[0112] 3. For each time slot t:

[0113] —Initialize the edge server selection strategy candidate set Ω to be an empty set.

[0114] - For each terminal device:

[0115] —Find the utility function that maximizes the terminal device i Edge server selection strategy , .

[0116] —Calculate the incremental utility of a policy change , .

[0117] -Will Added to the edge server selection strategy candidate set Ω, .

[0118] —When Ω is not empty:

[0119] —Find the function with the largest utility increment The terminal device n, .

[0120] — Update the edge server selection strategy of terminal device n, .

[0121] —Remove from the edge server selection strategy candidate set Ω , .

[0122] Mission strategy update:

[0123] 4. For each time slot t:

[0124] - For each terminal device i:

[0125] —If the edge server selection strategy of terminal device i =0:

[0126] -counter Increase by 1.

[0127] —If the counter Exceeding the preset threshold ,but:

[0128] —Find the index with the largest privacy leakage risk assessment index Wearable devices n.

[0129] -Will Set to 0.

[0130] Otherwise, the counter Reset to 0.

[0131] 5. Time slot t increases by 1.

[0132] The beneficial effects of the computing resource allocation method provided by the embodiment of the present disclosure are explained below through examples.

[0133] For example, 30 terminal devices are equipped with 1 to 10 wearable devices through a random selection process. The task data size generated by the wearable device is 100KB to 300KB, and the specific data size is determined randomly. The CPU cycles required to process the task are between 10 megacycles and 100 megacycles. The health monitoring system is operated on a bandwidth of 5 MHz, and the standard transmission power of data transmission is 0.1 W. The edge servers are randomly distributed, and the radio coverage range is 50 meters. The channel gain is set to ,in, represents the distance between the i-th terminal device and the j-th wearable device it is communicating with, and 3 represents the path loss factor. The computing power of the edge server and the terminal device are configured as 30,000 MHz and 2,000 MHz, respectively. Both the terminal device and the edge server are assigned a uniform transmission and computing power of 0.1W. The medical criticality and privacy leakage risk assessment indicators are both set in the range of 0 to 3. Privacy leakage risk assessment indicator corresponding to the i-th terminal device The coefficient of The value is 0.012.

[0134] Example 1: A simulation experiment with 30 terminal devices and 5 edge servers was conducted to observe the dynamic changes of the edge server selection strategy of the terminal devices over time. Initially, all terminal devices choose local computing. Figure 3 The strategy selection results for this experiment are shown, which show a clear trend towards equilibrium over time.

[0135] Example 2: A simulation experiment with 10 terminal devices and 5 edge servers was conducted to observe the dynamic changes of the utility of the terminal devices over time. Figure 4 The figure shows the dynamic changes of the terminal device utilities in this experiment. The results show that the utilities of the 10 terminal devices converge and stop changing within a limited number of iterations, and the utilities of all terminal devices increase over time.

[0136] Another embodiment of the present disclosure relates to a computing resource allocation device, which is applied to a health monitoring system, wherein the health monitoring system includes a plurality of edge servers, a plurality of terminal devices respectively connected to the edge servers for communication, and a plurality of wearable devices respectively connected to the terminal devices for communication. Figure 5 As shown, the computing resource allocation device includes a construction module 510 , a first determination module 520 , a second determination module 530 , and an update module 540 .

[0137] The construction module 510 is used to determine the utility function and its constraints corresponding to the terminal device based on the edge server selection strategy according to the communication energy consumption between the terminal device and each edge server, as well as the computing energy consumption corresponding to the terminal device, the privacy leakage risk assessment index and its coefficient.

[0138] The first determination module 520 is used to determine, based on the utility function, the edge server selection strategy corresponding to each terminal device when its utility is maximum.

[0139] The second determination module 530 is used to respectively determine the utility increment of each terminal device under the edge server selection strategy corresponding to the maximum utility of the terminal device relative to the utility increment under the current edge server selection strategy.

[0140] The updating module 540 is used to update the current edge server selection policy of the terminal device with the largest utility increment to the edge server selection policy corresponding to the maximum utility, so as to allocate computing resources of the edge server to the terminal device.

[0141] Exemplarily, the update module 540 is also used to: for each terminal device, determine whether the number of times it selects the terminal device locally to perform computing tasks exceeds a preset threshold; if so, set the computing location corresponding to the wearable device with the highest security level of its communication connection to the local terminal device.

[0142] The specific implementation method of the computing resource allocation device provided in the embodiment of the present disclosure can be found in the computing resource allocation method provided in the embodiment of the present disclosure, and will not be repeated here.

[0143] Compared with the prior art, the computing resource allocation device provided in the embodiment of the present disclosure can automatically adjust the terminal device's demand for edge server resources, reduce the overall energy consumption of the health monitoring system, and focus on reducing the energy consumption of the terminal device as one of the main goals, effectively extending the standby time of the terminal device; for sensitive user health and medical information, the risk of privacy leakage is taken into account, effectively reducing the risk of user privacy leakage and ensuring the security of user information; the task allocation between the local computing of the terminal device and the edge server can be dynamically adjusted, and the edge server selection strategy can be refined to each task to achieve the optimal balance between reducing energy consumption and protecting privacy, thereby improving the overall performance of the system and user experience, improving the response speed of medical services, and increasing user trust and satisfaction with the health monitoring system.

[0144] Another embodiment of the present disclosure relates to an electronic device, such as Figure 6 As shown, including:

[0145] at least one processor 601; and,

[0146] A memory 602 is communicatively connected to at least one processor 601; wherein,

[0147] The memory 602 stores instructions that can be executed by at least one processor 601. The instructions are executed by at least one processor 601 so that the at least one processor 601 can execute the computing resource allocation method described in the above embodiment.

[0148] Among them, the memory and the processor are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor.

[0149] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0150] Another embodiment of the present disclosure relates to a computer-readable storage medium storing a computer program, which implements the computing resource allocation method described in the above embodiment when executed by a processor.

[0151] That is, those skilled in the art can understand that all or part of the steps in the method described in the above embodiments can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0152] Those skilled in the art will appreciate that the above-mentioned embodiments are specific embodiments for implementing the present disclosure, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present disclosure.

Claims

1. A computing resource allocation method, characterized in that: Applied to a health monitoring system, the health monitoring system comprising a plurality of edge servers, a plurality of terminal devices respectively connected to the edge servers for communication, and a plurality of wearable devices respectively connected to the terminal devices for communication; The computing resource allocation method comprises: According to the communication energy consumption between the terminal device and each of the edge servers, as well as the computing energy consumption corresponding to the terminal device, the privacy leakage risk assessment index and its coefficient, the utility function and its constraint conditions corresponding to the terminal device are determined based on the edge server selection strategy; Based on the utility function, determining the edge server selection strategy corresponding to each terminal device when its utility is maximum; Determine respectively the utility increment of each terminal device under the edge server selection strategy corresponding to the maximum utility of the terminal device relative to the utility increment under the current edge server selection strategy; The current edge server selection policy of the terminal device with the largest utility increment is updated to the edge server selection policy corresponding to the maximum utility, so as to allocate computing resources of the edge server to the terminal device; The method of determining the utility function and its constraint conditions corresponding to the terminal device based on the edge server selection strategy according to the communication energy consumption between the terminal device and each edge server, as well as the computing energy consumption corresponding to the terminal device, the privacy leakage risk assessment index and its coefficient, includes: According to formula 1, the utility function is constructed: Formula 1; in, represents the edge server selection strategy corresponding to the i-th terminal device; Indicates the edge server selection strategy corresponding to other terminal devices other than the i-th terminal device; represents the utility function corresponding to the i-th terminal device; represents the cost function corresponding to the i-th terminal device and is expressed as: ； in, Represents a set of edge server selection strategies corresponding to all terminal devices; represents the privacy leakage risk assessment index corresponding to the i-th terminal device; express The coefficient of Indicates an indicator function. When the expression in the brackets is true The function value is 1, and the expression in the brackets is false. The function value of is 0; represents the communication energy consumption between the i-th terminal device and the edge server; represents the total energy consumed by the i-th terminal device to process computing tasks; According to Formula 2, the constraints of the utility function are determined: Formula 2; Wherein, N represents the number of terminal devices; Represents the set of all edge servers; The utility increment of the i-th terminal device under the edge server selection strategy corresponding to the maximum utility of the terminal device relative to the current edge server selection strategy is expressed as: ； in, represents the utility increment corresponding to the i-th terminal device at time t; The edge server selection strategy corresponding to the i-th terminal device when its utility is maximum is: At the same time, the utility function of other terminal devices keeps their edge server selection strategy unchanged; Indicates that the i-th terminal device selects the corresponding edge server strategy as At the same time, the utility function of other terminal devices keeps their edge server selection strategy unchanged; Privacy leakage risk assessment index corresponding to the i-th terminal device It is expressed as: ； Where j represents the number of the wearable device that is connected to the i-th terminal device, j=1,2,…, M , M Represents the total number of wearable devices that are connected to the i-th terminal device; Indicates the calculated position corresponding to the jth wearable device that is connected to the ith terminal device. Its value 0 indicates that the calculated position is local to the terminal device, and its value 1 indicates that the calculated position follows the terminal device selection. Represents the computing task corresponding to the jth wearable device that is connected to the i-th terminal device Privacy leakage risk assessment index; Communication energy consumption between the i-th terminal device and the edge server It is expressed as: ； in, represents the transmission power of the i-th terminal device; represents the transmission delay corresponding to the i-th terminal device and is expressed as: ； in, represents the total task size uploaded by the i-th terminal device to the edge server and is expressed as: ； in, Represents a computing task The data size; Represents a computing task The calculation result data size of represents the transmission rate between the i-th terminal device and its selected edge server and is expressed as: ； Where k represents the number of the edge server and k=1,2,…, K , K Indicates the total number of edge servers; represents the channel gain between the i-th terminal device and the k-th edge server; Indicates the terminal device number and The value of is 1 to N, which is different from i; Indicates The edge server selection strategy corresponding to each terminal device; Indicates The transmission power of each terminal device; represents the channel gain between the i-th terminal device and the k-th edge server; B represents the channel bandwidth; represents the background noise power; The total energy consumed by the i-th terminal device to process computing tasks It is expressed as: ； in, represents the computing power of the i-th terminal device; Indicates the computing power of the i-th terminal device; represents the number of CPU cycles required for the i-th terminal device to perform the computing task locally on its terminal device, and is expressed as: ； in, Represents processing computing tasks The number of CPU cycles required.

2. The computing resource allocation method according to claim 1, characterized in that: The computing resource allocation method further includes: For each of the terminal devices, determine whether the number of times it selects the terminal device to perform computing tasks locally exceeds a preset threshold. If so, set the computing location corresponding to the wearable device with the highest security level of its communication connection to the local terminal device.

3. A computing resource allocation device, characterized in that: Applied to a health monitoring system, the health monitoring system comprising a plurality of edge servers, a plurality of terminal devices respectively connected to the edge servers for communication, and a plurality of wearable devices respectively connected to the terminal devices for communication; The computing resource allocation device comprises: A construction module is used to determine the utility function and its constraint conditions corresponding to the terminal device based on the edge server selection strategy according to the communication energy consumption between the terminal device and each edge server, as well as the computing energy consumption corresponding to the terminal device, the privacy leakage risk assessment index and its coefficient; A first determination module, configured to determine, based on the utility function, the edge server selection strategy corresponding to each of the terminal devices when the utility of the terminal device is maximum; A second determination module is used to respectively determine the utility increment of each terminal device under the edge server selection strategy corresponding to the maximum utility of the terminal device relative to the utility increment under the current edge server selection strategy; An updating module, configured to update the current edge server selection policy where the terminal device with the largest utility increment is located to the edge server selection policy corresponding to the maximum utility, so as to allocate computing resources of the edge server to the terminal device; The method of determining the utility function and its constraint conditions corresponding to the terminal device based on the edge server selection strategy according to the communication energy consumption between the terminal device and each edge server, as well as the computing energy consumption corresponding to the terminal device, the privacy leakage risk assessment index and its coefficient, includes: According to formula 1, the utility function is constructed: Formula 1; in, represents the edge server selection strategy corresponding to the i-th terminal device; Indicates the edge server selection strategy corresponding to other terminal devices other than the i-th terminal device; represents the utility function corresponding to the i-th terminal device; represents the cost function corresponding to the i-th terminal device and is expressed as: ； in, Represents a set of edge server selection strategies corresponding to all terminal devices; represents the privacy leakage risk assessment index corresponding to the i-th terminal device; express The coefficient of Indicates an indicator function. When the expression in the brackets is true The function value is 1, and the expression in the brackets is false. The function value of is 0; represents the communication energy consumption between the i-th terminal device and the edge server; represents the total energy consumed by the i-th terminal device to process computing tasks; According to Formula 2, the constraints of the utility function are determined: Formula 2; Wherein, N represents the number of terminal devices; Represents the set of all edge servers; The utility increment of the i-th terminal device under the edge server selection strategy corresponding to the maximum utility of the terminal device relative to the current edge server selection strategy is expressed as: ； in, represents the utility increment corresponding to the i-th terminal device at time t; The edge server selection strategy corresponding to the i-th terminal device when its utility is maximum is: At the same time, the utility function of other terminal devices keeps their edge server selection strategy unchanged; Indicates that the i-th terminal device selects the corresponding edge server strategy as At the same time, the utility function of other terminal devices keeps their edge server selection strategy unchanged; Privacy leakage risk assessment index corresponding to the i-th terminal device It is expressed as: ； Where j represents the number of the wearable device that is connected to the i-th terminal device, j=1,2,…, M , M Represents the total number of wearable devices that are connected to the i-th terminal device; Indicates the calculated position corresponding to the jth wearable device that is connected to the ith terminal device. Its value 0 indicates that the calculated position is local to the terminal device, and its value 1 indicates that the calculated position follows the terminal device selection. Represents the computing task corresponding to the jth wearable device that is connected to the i-th terminal device Privacy leakage risk assessment index; Communication energy consumption between the i-th terminal device and the edge server It is expressed as: ； in, represents the transmission power of the i-th terminal device; represents the transmission delay corresponding to the i-th terminal device and is expressed as: ； in, represents the total task size uploaded by the i-th terminal device to the edge server and is expressed as: ； in, Represents a computing task The data size; Represents a computing task The calculation result data size of represents the transmission rate between the i-th terminal device and its selected edge server and is expressed as: ； Where k represents the number of the edge server and k=1,2,…, K , K Indicates the total number of edge servers; represents the channel gain between the i-th terminal device and the k-th edge server; Indicates the terminal device number and The value of is 1 to N, which is different from i; Indicates The edge server selection strategy corresponding to each terminal device; Indicates The transmission power of each terminal device; represents the channel gain between the i-th terminal device and the k-th edge server; B represents the channel bandwidth; represents the background noise power; The total energy consumed by the i-th terminal device to process computing tasks It is expressed as: ； in, represents the computing power of the i-th terminal device; Indicates the computing power of the i-th terminal device; represents the number of CPU cycles required for the i-th terminal device to perform the computing task locally on its terminal device, and is expressed as: ； in, Represents processing computing tasks The number of CPU cycles required.

4. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the computing resource allocation method according to claim 1 or 2.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the computing resource allocation method according to claim 1 or 2 is implemented.

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