Computing resource allocation method, device and mobile edge computing integrated device

Abstract

The present disclosure relates to a method, device and mobile edge computing integrated device for allocating computing power resources. The method is applied to the mobile edge computing integrated device, including: obtaining the computing power requirements corresponding to each software in the mobile edge computing integrated device under a preset performance level; calculating the total computing power requirements of the software corresponding to the preset performance level according to the computing power requirements corresponding to each software; comparing the total computing power requirements of the software with the total computing power supported by the hardware of the mobile edge computing integrated device; when the total computing power requirements of the software are less than the total computing power supported by the hardware, determining that the performance level of the mobile edge computing integrated device is the preset performance level; and allocating computing power resources to each software according to the computing power requirements corresponding to each software under the preset performance level. By adopting the above technical solution, computing power resources that meet the performance level can be adaptively allocated to each software deployed on the mobile edge computing integrated device, thereby improving the flexibility of computing power resource allocation.

Description

Technical Field

[0001] The present disclosure relates to the field of 5G mobile edge computing technology, and in particular to a computing resource allocation method, apparatus, and integrated mobile edge computing device. Background Art

[0002] With the application of 5G in various industries and fields, more and more requirements are being placed on the deployment methods, costs, and operation and maintenance of wireless communication equipment. For example, enterprise campus deployments must ensure the security and real-time availability of sensitive business data. Therefore, a hybrid networking model can be adopted, combining an internal enterprise mobile communications private network with the operator's public mobile communications network. Security-sensitive and latency-sensitive business services can be transmitted using the internal mobile communications private network, while public business services and internal employee online activities can be transmitted using the operator's public mobile communications network. An internal mobile communications private network can include four communication network elements: wireless base stations, mobile edge computing platforms, 5G core networks, and edge network management systems. To facilitate enterprise deployment, save costs, and reduce subsequent operation and maintenance, integrated equipment deployment is often used.

[0003] In related technologies, integrated devices are limited by their internal components and are usually large in size, high in power consumption, high in cost, and have poor performance. In particular, they can only achieve a fixed computing power configuration based on existing or set conditions in a relatively rigid manner, resulting in poor energy efficiency of each component in the integrated device. Summary of the Invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a computing resource allocation method, device and mobile edge computing integrated device.

[0005] In a first aspect, the present disclosure provides a computing resource allocation method applied to a mobile edge computing integrated device, the method comprising:

[0006] Obtaining the computing power requirements corresponding to each software in the mobile edge computing integrated device at a preset performance level;

[0007] Calculate the total computing power requirement of the software corresponding to the preset performance level based on the computing power requirement corresponding to each software;

[0008] Comparing the total computing power requirement of the software with the total computing power supported by the hardware of the mobile edge computing integrated device;

[0009] When the total computing power requirement of the software is less than the total computing power supported by the hardware, determining the performance level of the mobile edge computing integrated device to be the preset performance level;

[0010] Allocate computing power resources to each software according to the computing power requirements corresponding to each software at the preset performance level.

[0011] In a second aspect, the present disclosure provides a computing resource allocation device, which is applied to a mobile edge computing integrated device, and the device includes:

[0012] An acquisition module, configured to obtain the computing power requirements corresponding to each software in the mobile edge computing integrated device at a preset performance level;

[0013] A calculation module, configured to calculate the total computing power requirement of the software corresponding to the preset performance level based on the computing power requirement corresponding to each software;

[0014] A comparison module, configured to compare the total computing power requirement of the software with the total computing power supported by the hardware of the mobile edge computing integrated device;

[0015] a determination module, configured to determine that the performance level of the mobile edge computing integrated device is the preset performance level when the total computing power requirement of the software is less than the total computing power supported by the hardware;

[0016] The resource allocation module is used to allocate computing power resources to each software according to the computing power requirements corresponding to each software at the preset performance level.

[0017] In the third aspect, the present disclosure provides a mobile edge computing integrated device, including: a mobile edge computing management platform, 5G core network software, 5G network management software, 5G wireless base station protocol software, N2 interface adapter and N3 interface adapter deployed on the same server device, for executing a computing power resource allocation method, wherein the computing power resource allocation method determines the total computing power requirement of the software corresponding to the preset performance level according to the computing power requirement corresponding to each software in the mobile edge computing integrated device under the preset performance level, determines the performance level of the mobile edge computing integrated device according to the difference between the total computing power requirement of the software and the total computing power supported by the hardware of the mobile edge computing integrated device, and allocates computing power resources to each software.

[0018] The technical solution provided by the embodiments of the present disclosure has the following advantages over the prior art:

[0019] By obtaining the computing power requirements corresponding to each software in the mobile edge computing integrated device under a preset performance level, the total computing power requirements of the software corresponding to the preset performance level are calculated based on the computing power requirements corresponding to each software, and the total computing power requirements of the software are compared with the total computing power supported by the hardware of the mobile edge computing integrated device. When the total computing power requirements of the software are less than the total computing power supported by the hardware, the performance level of the mobile edge computing integrated device is determined to be the preset performance level, and computing power resources are allocated to each software according to the computing power requirements corresponding to each software under the preset performance level. A mobile edge computing integrated device is provided, comprising: a mobile edge computing management platform, 5G core network software, 5G network management software, 5G wireless base station protocol software, an N2 interface adapter, and an N3 interface adapter deployed on the same server device, for executing a computing power resource allocation method. By adopting the above technical solution, computing power resources that meet the performance level can be adaptively allocated to each software deployed on the mobile edge computing integrated device, thereby improving the flexibility of computing power resource allocation. In addition, by deploying 5G wireless base stations, 5G core networks, 5G network management software and mobile edge computing management platforms on the same server device to build an end-to-end 5G mobile communication network, the size of the integrated device is reduced, making it easy to carry and deploy. By adding N2 interface adapters and N3 interface adapters to replace the original N2 interface and N3 interface protocol stack processing methods, the software system's consumption of hardware resources is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0021] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1A This is a schematic diagram of the deployment structure of existing 5G end-to-end integrated equipment;

[0023] Figure 1B This is a block diagram of the structure of connecting 5G wireless base station equipment and 5G core network equipment through the standard N2 interface;

[0024] Figure 1C This is a block diagram of the structure of connecting 5G wireless base station equipment and 5G core network equipment through the standard N3 interface;

[0025] Figure 2 A flowchart of a computing resource allocation method provided in one embodiment of the present disclosure;

[0026] Figure 3 A schematic diagram of the structure of a mobile edge computing integrated device provided in an embodiment of the present disclosure;

[0027] Figure 4 A flowchart of a computing resource allocation method provided in another embodiment of the present disclosure;

[0028] Figure 5 A schematic diagram of the structure of a computing resource allocation device provided in one embodiment of the present disclosure;

[0029] Figure 6 A schematic diagram of the structure of a mobile edge computing integrated device provided in one embodiment of the present disclosure;

[0030] Figure 7 This is a schematic diagram of the structure of the communication between the 5G wireless base station and the 5G core network through the N2 interface adapter;

[0031] Figure 8 This is a structural diagram of the communication between the 5G wireless base station and the 5G core network through the N3 interface adapter. DETAILED DESCRIPTION

[0032] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features therein can be combined with each other in the absence of conflict.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0034] In order to facilitate enterprise deployment, save costs and facilitate subsequent operation and maintenance, the mobile communication private network built within the enterprise is usually deployed using integrated equipment.

[0035] In related technologies, the deployment of 5G end-to-end integrated equipment is as follows: Figure 1A As shown, from Figure 1A As can be seen, 5G wireless base station equipment, mobile edge computing equipment, 5G core network equipment, and 5G network management equipment are interconnected through switches and then placed in a portable device box to form a large integrated device. The disadvantages of this deployment method are large size, high power consumption, high cost, and difficulty in portability.

[0036] In the existing 5G end-to-end integrated equipment deployment mode, 5G wireless base station equipment and 5G core network equipment need to be interconnected through standard N2 and N3 interfaces. Figure 1BThis is a block diagram of the structure of connecting 5G wireless base station equipment and 5G core network equipment through the standard N2 interface. Figure 1C The following is a structural diagram of connecting 5G wireless base station equipment and 5G core network equipment through the standard N3 interface. Figure 1B As shown, N2 interface processors need to be set on both the wireless side (i.e., 5G wireless base station equipment side) and the AMF (Access and Mobility Management Function) side of the 5G core network equipment. The N2 interface processor on the wireless side and the N2 interface processor on the AMF side communicate through standard communication protocols such as NGAP (Next Generation Application Protocol), SCTP (Stream Control Transmission Protocol) and IP (Internet Protocol). Figure 1C As shown, both the wireless side and the UPF (User Plane Function) side of the 5G core network equipment require an N3 interface processor. The wireless side N3 interface processor and the UPF side N3 interface processor communicate with each other through standard communication protocols such as GTP-U (User Plane Part of GTP, GPRS user plane part) protocol, UDP (User Datagram Protocol, User Datagram Protocol) protocol, and IP protocol. It can be seen that the 5G wireless base station equipment and the 5G core network equipment are connected through the N2 interface and the N3 interface. The communication between the wireless side and the 5G core network requires heavy protocol processing, which will consume a large amount of hardware resources of the integrated equipment.

[0037] In addition, when deploying software on 5G end-to-end integrated devices, fixed computing resources are usually manually configured for the software, without considering the performance balance of each software in the integrated device, resulting in poor flexibility.

[0038] In response to the above problems, the present disclosure provides a computing power resource allocation method, device and mobile edge computing integrated device. By pre-configuring computing power requirements corresponding to different performance levels for each software in the mobile edge computing integrated device, users can adaptively select the performance level of each software system and allocate computing power resources based on the computing power supported by the hardware, the computing power requirements of the mobile edge computing applications integrated on the integrated device, etc., thereby improving the flexibility of computing power resource allocation and ensuring the performance balance of each software system in the integrated device.

[0039] Figure 2This is a flow chart of a computing resource allocation method provided in an embodiment of the present disclosure. The computing resource allocation method can be executed by a computing resource allocation device provided in an embodiment of the present disclosure. The computing resource allocation device can be implemented using software and / or hardware and can be integrated into the mobile edge computing integrated device provided in an embodiment of the present disclosure. Figure 3 A schematic diagram of the structure of a mobile edge computing integrated device provided in an embodiment of the present disclosure.

[0040] like Figure 3 As shown, general server hardware can be used to centrally deploy mobile edge applications, mobile edge computing management platform, 5G base station protocol software, N2 interface adapter, N3 interface adapter, 5G core network software and 5G network management software on a unified server device to form an end-to-end integrated device for the 5G mobile communication network, which is easy to carry and deploy, saves deployment space and saves costs. Figure 3 In the server, the operating system and hardware infrastructure are resources provided by the server itself. The software deployed on the server can communicate with each other through the internal loopback network of the server, and communication between different software can be achieved without the help of network ports and switches.

[0041] like Figure 2 As shown, the computing power resource allocation method may include the following steps:

[0042] Step 101: Obtain the computing power requirements corresponding to each software in the mobile edge computing integrated device at a preset performance level.

[0043] Among them, each software in the mobile edge computing integrated device can include 5G base station protocol software, 5G core network software, 5G network management software, mobile edge computing management platform, mobile edge applications, etc.

[0044] In the disclosed embodiments, the computing power requirements corresponding to each software in the mobile edge computing integrated device at different performance levels can be pre-set. Generally, the higher the performance level, the higher the computing power requirements corresponding to each software. When computing power resources need to be allocated, the computing power requirements corresponding to each software in the mobile edge computing integrated device at the preset performance level are obtained.

[0045] Among them, the preset performance level can be the highest level among multiple pre-configured performance levels, or the preset performance level can also be the current performance level of the mobile edge computing integrated device.

[0046] Step 102: Calculate the total computing power requirement of the software corresponding to the preset performance level based on the computing power requirement corresponding to each software.

[0047] In the embodiment of the present disclosure, after obtaining the computing power requirements corresponding to each software at the preset performance level, the total computing power requirements of the software corresponding to the preset performance level can be calculated.

[0048] For example, assuming that each software corresponds to only one computing power requirement, the computing power requirements corresponding to each software can be summed to obtain the total computing power requirement of the software corresponding to the preset performance level.

[0049] For example, assuming that the computing power requirements corresponding to some software include a minimum computing power requirement and a maximum computing power requirement, the maximum computing power requirement corresponding to this part of the software can be summed with the computing power requirements corresponding to the remaining software to obtain the total computing power requirement of the software corresponding to the preset performance level. Alternatively, the average value of the corresponding maximum computing power requirement and minimum computing power requirement can be calculated for this part of the software, and then the average value corresponding to each part of the software can be summed with the computing power requirements corresponding to the remaining software to obtain the total computing power requirement of the software corresponding to the preset performance level.

[0050] Step 103: Compare the total computing power requirement of the software with the total computing power supported by the hardware of the mobile edge computing integrated device.

[0051] Among them, the total computing power supported by the hardware of the mobile edge computing integrated device is provided by the hardware infrastructure of the server device itself. The total computing power supported by the hardware can be pre-stored by technicians in the local storage space of the mobile edge computing integrated device and can be directly obtained when needed.

[0052] Step 104: When the total computing power requirement of the software is less than the total computing power supported by the hardware, determine the performance level of the mobile edge computing integrated device as a preset performance level.

[0053] In an embodiment of the present disclosure, after calculating the total software computing power requirement corresponding to the preset performance level, the total software computing power requirement can be compared with the total hardware supported computing power that the mobile edge computing integrated device can provide. If the total software computing power requirement is less than the total hardware supported computing power, the preset performance level is determined as the performance level of the mobile edge computing integrated device.

[0054] Step 105: Allocate computing resources to each software according to the computing power requirements corresponding to each software at a preset performance level.

[0055] In the embodiment of the present disclosure, when the total computing power requirement of the software corresponding to the preset performance level is less than the total computing power supported by the hardware, computing power resources can also be allocated to each software according to the computing power requirement corresponding to each software at the preset performance level.

[0056] For example, assuming that the performance level of the mobile edge computing integrated device is determined to be level 3, and the computing power requirement of application A corresponding to the pre-set level 3 is 0.5 cores, then when allocating CPU (Central Processing Unit) resources to application A, a CPU with 0.5 cores or a CPU slightly higher than 0.5 cores can be allocated to application A.

[0057] The computing power resource allocation method provided in this embodiment obtains the computing power requirements corresponding to each software in the mobile edge computing integrated device under the preset performance level, calculates the total computing power requirements of the software corresponding to the preset performance level based on the computing power requirements corresponding to each software, and compares the total computing power requirements of the software with the total computing power supported by the hardware of the mobile edge computing integrated device. When the total computing power requirements of the software are less than the total computing power supported by the hardware, the performance level of the mobile edge computing integrated device is determined to be the preset performance level, and computing power resources are allocated to each software based on the computing power requirements corresponding to each software under the preset performance level. The above technical solution can adaptively allocate computing power resources that meet the performance level to each software deployed on the mobile edge computing integrated device, thereby improving the flexibility of computing power resource allocation and ensuring the performance balance of each software system of the integrated device.

[0058] Furthermore, in an optional implementation of the embodiment of the present disclosure, when the total computing power requirement of the software is greater than or equal to the total computing power supported by the hardware, the preset performance level is lowered by one level, and the computing power requirement corresponding to each software in the mobile edge computing integrated device at the lowered performance level is obtained.

[0059] That is to say, when the total computing power supported by the hardware provided by the mobile edge computing integrated device cannot meet the computing power requirements of each software at the preset performance level, the preset performance level can be lowered by one level, and the computing power requirements corresponding to each software in the mobile edge computing integrated device at the lowered performance level are obtained, and then the total computing power requirements of the software corresponding to the lowered performance level are calculated and compared with the total computing power supported by the hardware. If the total computing power requirements of the software corresponding to the lowered performance level are less than the total computing power supported by the hardware, the lowered performance level is determined as the performance level of the mobile edge computing integrated device, and computing power resources are allocated to each software according to the computing power requirements of each software corresponding to the lowered performance level. If the total computing power requirements of the software corresponding to the lowered performance level are still greater than or equal to the total computing power supported by the hardware, the performance level is further lowered by one level, and the above process is repeated until the total computing power requirements of the software corresponding to the lowered performance level are less than the total computing power supported by the hardware, or until the performance is equal to or less than 1.

[0060] The solution of the embodiment of the present disclosure lowers the preset performance level by one level when the total computing power requirement of the software is greater than or equal to the total computing power supported by the hardware, and obtains the computing power requirement corresponding to each software in the mobile edge computing integrated device at the lowered performance level. As a result, the performance level of the integrated device can be adaptively adjusted according to the difference between the total computing power supported by the hardware provided by the mobile edge computing integrated device and the total computing power required by the software, and then the computing power allocation calculation is performed according to the computing power requirement corresponding to each software at the adjusted performance level, thereby improving the flexibility of computing power allocation and facilitating improving the utilization of system resources.

[0061] In an optional implementation of the disclosed embodiment, the computing power requirements corresponding to each software are obtained for subsequent operations only when the preset performance level is greater than or equal to 1 and the performance level after the adjustment is greater than or equal to 1. If the preset performance level or the performance level after the adjustment is less than 1, the user is prompted that the computing power resource allocation has failed.

[0062] For example, when the preset performance level is less than 1, the computing power allocation fails. The user can be prompted that the computing power resource allocation has failed through text display, voice broadcast, etc., and the reason for the failure can be given, such as "the computing power supported by the hardware cannot meet the computing power requirements of the system's minimum performance level. It is recommended to delete some applications."

[0063] The solution of the embodiment of the present disclosure can ensure that the computing power requirements corresponding to each software are obtained by determining that the preset performance is equal to or greater than 1, thereby providing conditions for subsequent operations; by prompting the user that the computing power resource allocation fails when the preset performance level is less than 1, the user can be notified of the allocation results of the resource allocation failure in a timely manner, so that the user can take corresponding measures in a timely manner.

[0064] Mobile edge computing management platform, mobile edge applications, 5G wireless base station protocol software, 5G core network software and 5G network management software are software systems built into mobile edge computing integrated devices. These software can be classified according to the level of latency requirements into sensitive software and non-sensitive software. Sensitive software requires sufficient hardware resources to ensure a certain level of performance requirements. It is an exclusively allocated hardware resource and will not always maintain efficient resource utilization. Non-sensitive software needs to ensure its minimum hardware resource requirements and can share resources with other non-sensitive software. 5G wireless base station protocol software and 5G core network software have high latency requirements and belong to the time-sensitive software set; mobile edge computing management platform and 5G network management software belong to the time-insensitive software set. For sensitive software and non-sensitive software, different calculation methods can be used to calculate the total computing power required for sensitive software and the total computing power required for non-sensitive software. The following is combined with the attached Figure 4 Provide detailed explanation.

[0065] Figure 4 A flowchart of a computing resource allocation method provided in another embodiment of the present disclosure.

[0066] like Figure 4 As shown, the computing power resource allocation method may include the following steps:

[0067] Step 201: Determine a preset performance level i.

[0068] Among them, the preset performance level i can be the highest level among multiple pre-set performance levels, or it can be the performance level currently corresponding to the mobile edge computing integrated device, and the performance level currently corresponding to the mobile edge computing integrated device is not higher than the pre-set highest level.

[0069] In an optional implementation of the embodiment of the present disclosure, the mobile edge computing integrated device may include a visualization interface, through which the user can pre-configure the computing power requirements corresponding to each software at different performance levels, wherein each software can be determined based on the actual application scenario of the mobile edge computing integrated device, that is, the user can determine all software that may be deployed in the mobile edge computing integrated device in combination with the actual application scenario of the mobile edge computing integrated device, and configure the computing power requirements corresponding to each software at different performance levels through the visualization interface provided by the mobile edge computing integrated device. Thus, the computing power resource allocation method provided by the embodiment of the present disclosure may also include: receiving multiple performance levels configured by the user through the visualization interface and the computing power requirements corresponding to different software at each performance level; and storing the corresponding relationship between each performance level and the computing power requirements of the corresponding different software.

[0070] Among them, the higher the performance level, the higher the computing power requirements of each software.

[0071] Exemplarily, different performance levels can be represented by numbers. For example, the user pre-configures three levels of performance levels, which are recorded as level 1, level 2 and level 3, among which level 3 is the highest level. For the same software A, the computing power requirement at performance level 3 is higher than the computing power requirement at performance level 2, and the computing power requirement at performance level 2 is higher than the computing power requirement at performance level 1.

[0072] In an embodiment of the present disclosure, a visual interface is set up on a mobile edge computing integrated device to receive multiple performance levels configured by the user and the computing power requirements corresponding to different software at each performance level, and the correspondence between each performance level and the corresponding computing power requirements of different software is stored, so that users can perform interactive operations based on the visual interface of the mobile edge computing integrated device, thereby improving the flexibility of the system.

[0073] In the embodiment of the present disclosure, the preset performance level i can be determined based on the pre-configured performance level or the current performance level of the mobile edge computing integrated device.

[0074] For example, assuming that the pre-configured maximum performance level is 5, when the computing power resources are allocated for the first time to the software deployed on the mobile edge computing integrated device, it can be determined that the preset performance level i is the highest level 5.

[0075] For example, assuming that the pre-configured highest performance level is 5, some software has been deployed on the mobile edge computing integrated device, and the current performance level of the mobile edge computing integrated device is level 4, then when new software needs to be deployed on the mobile edge computing integrated device, computing power resources need to be reallocated, and the preset performance level i can be determined to be the current performance level 4.

[0076] Step 202: Determine whether the preset performance level i is less than 1.

[0077] Step 203: Obtain the maximum computing power requirement corresponding to each sensitive software under the preset performance level i.

[0078] Step 204: Obtain the minimum computing power requirement and the maximum computing power requirement corresponding to each non-sensitive software under the preset performance level i.

[0079] In the embodiment of the present disclosure, after determining the preset performance level i, it is possible to first determine whether i is less than 1. If i is greater than or equal to 1, the maximum computing power requirement corresponding to each sensitive software under the preset performance level i can be obtained, as well as the minimum computing power requirement and maximum computing power requirement corresponding to each non-sensitive software under the preset performance level i can be obtained.

[0080] Software can be categorized as sensitive or non-sensitive based on its latency requirements. For example, 5G wireless base station protocol software and 5G core network software have high latency requirements and are considered sensitive software, while mobile edge computing platforms and 5G network management software have low latency requirements and are therefore considered non-sensitive software. For sensitive software, users can configure only the maximum computing power requirements corresponding to different performance levels. For non-sensitive software, users need to configure both the minimum and maximum computing power requirements corresponding to different performance levels.

[0081] It should be noted that when the computing power requirements of a third-party application to be deployed are not found in the pre-configured correspondence between different performance levels and the computing power requirements corresponding to each software, the user can be prompted to configure the computing power requirements of the third-party application at different performance levels through a visual interface, and then obtain the computing power requirements of the third-party application at the preset performance level i based on the determined preset performance level i.

[0082] Step 205: Calculate the total computing power requirement of the sensitive software corresponding to the preset performance level i based on the maximum computing power requirement corresponding to each sensitive software.

[0083] For example, the total computing power requirement of the sensitive software corresponding to the preset performance level i can be calculated by the following formula (1).

[0084]

[0085] Among them, S_SW_CP total (i) represents the total computing power requirement of sensitive software corresponding to the preset performance level i; BBU_CP max (i) indicates the maximum computing power requirement of the 5G base station protocol software when the preset performance level is i; 5GC_CP max (i) indicates the maximum computing power requirement of the 5G core network software when the preset performance level is i; APP_CP max,j (i) represents the maximum computing power requirement corresponding to the jth application software when the preset performance level is i, j = (1, 2, ..., X) represents the number of sensitive software, and X is a positive integer.

[0086] Step 206: Calculate the shared computing power requirement corresponding to each non-sensitive software based on the minimum computing power requirement and the maximum computing power requirement corresponding to each non-sensitive software.

[0087] For example, the shared computing power requirement corresponding to each non-sensitive software can be calculated using the following formula (2).

[0088]

[0089] Among them, MEC_CP share (i) indicates the shared computing power requirement of the mobile edge computing management platform when the preset performance level is i; MEC_CP max (i) indicates the maximum computing power requirement of the mobile edge computing management platform when the preset performance level is i; MEC_CP min (i) indicates the minimum computing power requirement of the mobile edge computing management platform when the preset performance level is i; HMS_CP share (i) indicates the shared computing power requirement of the 5G network management software when the preset performance level is i; HMS_CP max (i) indicates the maximum computing power requirement of the 5G network management software when the preset performance level is i; HMS_CP min (i) indicates the minimum computing power requirement of the 5G network management software when the preset performance level is i; APP_CP share,j (i) represents the shared computing power requirement corresponding to the jth application software when the preset performance level is i; APP_CPmax,j (i) indicates the maximum computing power requirement corresponding to the jth application software when the preset performance level is i; APP_CP min,j (i) represents the minimum computing power requirement corresponding to the jth application software when the preset performance level is i; where j = (X+1, X+2, ..., Y) represents the number of non-sensitive software, and Y is greater than X.

[0090] Step 207: Calculate the total computing power requirement of the non-sensitive software corresponding to the preset performance level i based on the shared computing power requirement corresponding to each non-sensitive software and the minimum computing power requirement corresponding to each non-sensitive software.

[0091] For example, the total computing power requirement of the non-sensitive software corresponding to the preset performance level i can be calculated by the following formula (3).

[0092]

[0093] Among them, NS_SW_CP toatal (i) represents the total computing power requirement of non-sensitive software corresponding to the preset performance level i.

[0094] Step 208, calculate the total computing power requirement of the software corresponding to the preset performance level i based on the total computing power requirement of the sensitive software corresponding to the preset performance level i, the total computing power requirement of the non-sensitive software corresponding to the preset performance level i, and the computing power requirement corresponding to the underlying software of the operating system.

[0095] For example, the total computing power requirement of the software corresponding to the preset performance level i can be calculated using the following formula (4).

[0096] SW_CP toatal (i) = OS_CP required +S_SW_CP total (i)+NS_SW_CP total (i) (4)

[0097] Among them, SW_CP toatal (i) represents the total computing power requirement of the software corresponding to the preset performance level i; OS_CP required Indicates the computing power requirements of the underlying software related to the operating system of the mobile edge computing integrated device.

[0098] Step 209: Determine whether the total computing power requirement of the software is less than the total computing power supported by the hardware of the mobile edge computing integrated device.

[0099] Among them, the total computing power supported by the hardware of the mobile edge computing integrated device can be recorded as HW_CP totalThe total computing power supported by the hardware can be configured by the user according to the resources that can be provided by the hardware facilities of the mobile edge computing integrated device when configuring the computing power requirements of each software corresponding to different performance levels. The total computing power supported by the hardware can be written into the local storage space of the mobile edge computing integrated device and can be directly obtained when needed.

[0100] Step 210: Determine that the performance level of the mobile edge computing integrated device is a preset performance level i.

[0101] Step 211 , allocate computing resources to each software according to the computing power requirements corresponding to each software at a preset performance level i.

[0102] In an embodiment of the present disclosure, when the determined total computing power requirement of the software is less than the total computing power supported by the hardware of the mobile edge computing integrated device, the preset performance level i can be determined as the performance level corresponding to the mobile edge computing integrated device, and computing power resources can be allocated to each software based on the computing power requirement corresponding to each software under the preset performance level i.

[0103] For example, for sensitive software, computing power resources with the highest computing power requirement corresponding to the sensitive software at the preset performance level i can be allocated; for non-sensitive software, computing power resources slightly higher than the minimum computing power requirement corresponding to the non-sensitive software at the preset performance level i can be allocated.

[0104] Step 212, set i=i-1.

[0105] In an embodiment of the present disclosure, when the determined total software computing power requirement is greater than or equal to the total computing power supported by the hardware of the mobile edge computing integrated device, i=i-1 can be set, that is, the preset performance level i is lowered by one level, and then return to step 202 to determine whether the lowered i is less than 1, and continue to execute subsequent steps if it is not less than 1.

[0106] Step 213: prompt the user that the computing power resource allocation has failed.

[0107] In the disclosed embodiment, when i is less than 1, computing power resource allocation fails, and a prompt message can be sent to the user to inform the user of the computing power resource allocation failure. Furthermore, the reason for the failure can be provided, such as "The computing power supported by the hardware cannot meet the system's minimum performance level computing power requirements. It is recommended to delete some applications."

[0108] The computing power resource allocation method of the embodiment of the present disclosure calculates the shared computing power requirement corresponding to each non-sensitive software according to the minimum computing power requirement and the maximum computing power requirement corresponding to each non-sensitive software, and then calculates the total computing power requirement of the non-sensitive software corresponding to the preset performance level according to the shared computing power requirement and the minimum computing power requirement of each non-sensitive software, and then calculates the total computing power requirement of the software corresponding to the preset performance level according to the total computing power requirement of the sensitive software corresponding to the preset performance level, the total computing power requirement of the non-sensitive software corresponding to the preset performance level and the computing power requirement corresponding to the underlying software of the operating system, so that when calculating the total computing power requirement of the software, the total computing power requirement of the software is taken into consideration. It allows non-sensitive software to share computing resources, ensuring the accuracy of the calculation of the software's total computing power requirements, while also enabling the integrated device to operate at a higher performance level as much as possible, ensuring the operating efficiency of each software deployed in the integrated device; by defining different performance levels and corresponding computing power requirements for mobile edge computing software systems, 5G base station protocol software systems, 5G core network software systems, 5G network management software systems and third-party application software systems, it is convenient for users to adaptively select the performance level of each software system based on the computing power supported by the hardware, the computing power requirements of the mobile edge computing applications integrated on the integrated device, etc., so as to achieve the purpose of performance balance of each software system in the integrated device.

[0109] In order to implement the above embodiments, the present disclosure also provides a computing power resource allocation device, which can be implemented using software and / or hardware and can be integrated on the mobile edge computing integrated device provided in the embodiments of the present disclosure.

[0110] Figure 5 This is a schematic diagram of the structure of a computing resource allocation device provided in one embodiment of the present disclosure. Figure 5 As shown, the computing power resource allocation device 30 may include: an acquisition module 310, a calculation module 320, a comparison module 330, a determination module 340 and a resource allocation module 350.

[0111] The acquisition module 310 is configured to obtain the computing power requirements corresponding to each software in the mobile edge computing integrated device at a preset performance level;

[0112] A calculation module 320 is configured to calculate the total computing power requirement of the software corresponding to the preset performance level based on the computing power requirement corresponding to each software;

[0113] A comparison module 330 is configured to compare the software total computing power requirement with the hardware supported total computing power of the mobile edge computing integrated device;

[0114] A determination module 340 is configured to determine that the performance level of the mobile edge computing integrated device is the preset performance level when the total computing power requirement of the software is less than the total computing power supported by the hardware;

[0115] The resource allocation module 350 is used to allocate computing resources to each software according to the computing power requirements corresponding to each software at the preset performance level.

[0116] Optionally, the computing resource allocation device 30 further includes:

[0117] The level adjustment module is used to lower the preset performance level by one level when the total computing power requirement of the software is greater than or equal to the total computing power supported by the hardware, and obtain the computing power requirement corresponding to each software in the mobile edge computing integrated device at the lowered performance level.

[0118] Optionally, the software in the mobile edge computing integrated device includes sensitive software and non-sensitive software; the acquisition module 310 is used to:

[0119] Obtain the maximum computing power required for each sensitive software under the preset performance level;

[0120] Obtain the minimum and maximum computing power requirements for each non-sensitive software at the preset performance level.

[0121] Optionally, the calculation module 320 is further configured to:

[0122] Calculate the total computing power requirement of the sensitive software corresponding to the preset performance level based on the maximum computing power requirement corresponding to each sensitive software;

[0123] Calculate the shared computing power requirement corresponding to each non-sensitive software based on the minimum computing power requirement and the maximum computing power requirement corresponding to each non-sensitive software;

[0124] Calculate the total computing power requirement of the non-sensitive software corresponding to the preset performance level based on the shared computing power requirement corresponding to each non-sensitive software and the minimum computing power requirement corresponding to each non-sensitive software;

[0125] Based on the total computing power requirements of sensitive software corresponding to the preset performance level, the total computing power requirements of non-sensitive software corresponding to the preset performance level, and the computing power requirements corresponding to the underlying software of the operating system, the total computing power requirements of the software corresponding to the preset performance level are calculated.

[0126] Optionally, the mobile edge computing integrated device includes a visual interface, and the computing resource allocation device 30 further includes:

[0127] A receiving module, configured to receive multiple performance levels configured by a user through the visual interface and computing power requirements corresponding to different software at each performance level;

[0128] The storage module is used to store the corresponding relationship between each performance level and the computing power requirements of the corresponding different software.

[0129] Optionally, the computing resource allocation device 30 further includes:

[0130] A preprocessing module is used to determine whether the preset performance level is greater than or equal to 1.

[0131] Optionally, the computing resource allocation device 30 further includes:

[0132] The prompt module is used to prompt the user that the computing power resource allocation has failed when the preset performance level is less than 1.

[0133] The computing power resource allocation device provided in the embodiments of the present disclosure can execute the computing power resource allocation method provided in the embodiments of the present disclosure, which can be applied to the integrated mobile edge computing device, and has the corresponding functional modules and beneficial effects of the execution method. For any content not fully described in the embodiments of the present disclosure, please refer to the description of any method embodiment of the present disclosure.

[0134] In order to implement the above embodiments, the present disclosure also provides an integrated mobile edge computing device.

[0135] Figure 6 This is a structural diagram of a mobile edge computing integrated device provided by an embodiment of the present disclosure, such as Figure 6 As shown, the mobile edge computing integrated device 40 includes: a mobile edge computing management platform 420, 5G core network software 430, 5G network management software 440, 5G wireless base station protocol software 450, N2 interface adapter 460 and N3 interface adapter 470 deployed on the same server device 410, for executing the computing power resource allocation method. The computing power resource allocation method can be the computing power resource allocation method described in the previous embodiment.

[0136] Among them, the mobile edge computing management platform 420, 5G core network software 430, 5G network management software 440 and 5G wireless base station protocol software 450 communicate through the internal loop network of the server device 410, without the need for switch equipment for communication connection, reducing the size of the integrated device.

[0137] In the embodiment of the present disclosure, the 5G wireless base station, 5G core network, 5G network management software and mobile edge computing management platform used to build an end-to-end 5G mobile communication network are all deployed in the form of software in the same server device. Compared with the existing technology, the wireless base station equipment, mobile edge computing equipment and 5G core network equipment are interconnected with the switch equipment as independent devices and then placed as a whole in a movable equipment box to form a large device. This reduces the size of the integrated equipment, makes it easy to carry and deploy, makes network management more convenient, saves deployment space, and greatly reduces costs.

[0138] In an optional implementation of the embodiment of the present disclosure, the 5G wireless base station protocol software may include a first processor, and the 5G core network software may include a second processor. In this embodiment, the N2 interface adapter is used to establish a signaling plane adaptation information mapping relationship, the signaling plane adaptation information mapping relationship including a mapping relationship between wireless base station information and AMF information, a mapping relationship between wireless base station message types and AMF message types, and a mapping relationship between a user identifier within the wireless base station and a user identifier within the AMF; the first processor sends an uplink signaling message through a first internal interface; the N2 interface adapter obtains the wireless base station information, the wireless base station message type, and the user identifier within the wireless base station from the uplink signaling message, and queries the signaling plane adaptation information mapping relationship to determine the corresponding target AMF information, target AMF message type, and target AMF user identifier, and constructs an uplink NAS adaptation message based on the target AMF information, target AMF message type, and target AMF user identifier, and sends the uplink NAS adaptation message to the second processor through a second internal interface.

[0139] Furthermore, in an optional implementation of the embodiment of the present disclosure, the second processor sends a downlink NAS message to the N2 interface adapter through the second internal interface; the N2 interface adapter obtains AMF information, AMF message type and AMF internal user identifier from the downlink NAS message, and queries the signaling plane adaptation information mapping relationship, determines the corresponding target wireless base station information, target wireless base station message type and target wireless base station internal user identifier, and constructs a downlink NAS adaptation message based on the target wireless base station information, target wireless base station message type and target wireless base station internal user identifier, and sends the downlink NAS adaptation message to the first processor through the first internal interface.

[0140] For example, Figure 7 This is a schematic diagram of the structure of the communication between the 5G wireless base station and the 5G core network through the N2 interface adapter. Figure 7In the example, the wireless side L3 processor is the first processor of the 5G wireless base station protocol software, and the AMF side NAS (Network Attached Storage) processor is the second processor of the 5G core network software. The two are adapted to communicate through the N2 interface adapter. The N2 interface adapter establishes a signaling plane adaptation information mapping relationship, including a mapping relationship between wireless base station information and AMF information (denoted as Table [Wireless Base Station Information, AMF Information]), a mapping relationship between wireless base station message types and AMF message types (denoted as Table [Wireless Base Station Message Type, AMF Message Type]), and a mapping relationship between a user identifier within the wireless base station and a user identifier within the AMF (denoted as Table [User Identifier Within the Wireless Base Station, User Identifier Within the AMF]). The mapping relationship between the user identifier within the wireless base station and the user identifier within the AMF is dynamically created when the user accesses. Wireless base station information includes the global gNB (5G base station) node identifier, supported global cell identifiers, supported TA (Tracking Area) list, each TA list item includes TAC (Tracking Area Code), and supported slice identifier list; AMF information includes AMF name, GUAMI and supported PLMN (Public Land Mobile Network) list, each PLMN list item contains a PLMN identifier and a supported slice identifier list.

[0141] During uplink communication, the wireless-side L3 processor sends an uplink signaling message via "Internal Interface_1" (i.e., the first internal interface). "Internal Interface_1" can be a message queue, socket, or other communication method, and interface adaptation can be performed based on actual conditions. The N2 interface adapter obtains the internally defined wireless base station message type, wireless base station user identifier, and wireless base station information from the uplink signaling message. It obtains the AMF internally defined message type by looking up the wireless base station message type table [Wireless Base Station Message Type, AMF Message Type]; obtains the AMF user identifier by looking up the wireless base station user identifier table [Wireless Base Station User Identifier, AMF User Identifier]; and obtains the AMF information by looking up the wireless base station information table [Wireless Base Station Information, AMF Information]. Based on the above information obtained from the table lookup, it constructs an uplink NAS adaptation message and sends the adapted uplink NAS adaptation message to the AMF-side NAS processor via "Internal Interface_2" (i.e., the second internal interface). "Internal Interface_2" can be a message queue, socket, or other communication method. The NAS processor on the AMF side may process the uplink NAS adaptation message in a traditional manner to complete reception of the uplink message.

[0142] During downlink communication, the AMF-side NAS processor sends a downlink NAS message to the N2 interface adapter via "Internal Interface_2." The N2 interface adapter retrieves the AMF information, AMF internal user identifier, and AMF message type from the downlink NAS message. It then uses the AMF information lookup table [Radio Base Station Information, AMF Information] to obtain the radio base station information. It also uses the AMF message type lookup table [Radio Base Station Message Type, AMF Message Type] to obtain the message type defined within the radio base station. It also uses the AMF internal user identifier lookup table [Radio Base Station Internal User Identifier, AMF Internal User Identifier] to obtain the radio base station internal user identifier. Based on the information retrieved from the lookup table, the N2 interface adapter constructs a downlink NAS adaptation message and sends it to the wireless-side L3 processor via "Internal Interface_1." The wireless-side L3 processor processes the downlink NAS adaptation message using traditional methods, completing reception of the downlink message.

[0143] In an optional implementation of the disclosed embodiment, the 5G wireless base station protocol software includes a third processor, and the 5G core network software includes a fourth processor. In this embodiment, the N3 interface adapter is used to establish a user plane adaptation information mapping relationship, where the user plane adaptation information mapping relationship includes a mapping relationship between a wireless base station message type and a UPF message type; the third processor sends uplink service data to the N3 interface adapter via a third internal interface; the N3 interface adapter obtains the wireless base station message type from the uplink service data, queries the user plane adaptation information mapping relationship based on the wireless base station message type, determines the corresponding target UPF message type, and constructs an uplink service adaptation message based on the target UPF message type, and sends the uplink service adaptation message to the fourth processor via a fourth internal interface.

[0144] Furthermore, the fourth processor sends downlink service data to the N3 interface adapter through the fourth internal interface; the N3 interface adapter obtains the UPF message type from the downlink service data, and queries the user plane adaptation information mapping relationship based on the UPF message type, determines the corresponding target wireless base station message type, and constructs a downlink service adaptation message based on the target wireless base station message type, and sends the downlink service adaptation message to the third processor through the third internal interface.

[0145] For example, Figure 8 This is a schematic diagram of the structure of the communication between the 5G wireless base station and the 5G core network through the N3 interface adapter. Figure 8In the 5G core network, the wireless-side L2 processor, the third processor of the 5G wireless base station protocol software, and the UPF-side N6 processor, the fourth processor of the 5G core network software, communicate with each other through the N3 interface adapter. The N3 interface adapter establishes a user plane adaptation information mapping relationship, including the mapping relationship between wireless base station message types and UPF message types (denoted as Table [Wireless Base Station Message Type, UPF Message Type]).

[0146] During uplink communication, the L2 processor on the wireless side sends uplink service data through the "internal interface_3" (i.e., the third internal interface), where "internal interface_3" can be a communication method such as a message queue or a socket. The N3 interface adapter obtains the wireless base station message type from the uplink service data, and through the wireless base station message type lookup table [wireless base station message type, UPF message type], obtains the UPF message type sent to the N6 processor on the UPF side, and constructs an uplink service adaptation message based on the obtained UPF message type. The N3 interface adapter sends the adapted uplink service adaptation message to the N6 processor on the UPF side through the "internal interface_4" (i.e., the fourth internal interface), where "internal interface_4" can be a communication method such as a message queue or a socket. The N6 processor on the UPF side can use traditional processing methods to process the uplink service adaptation message and complete the reception of the uplink service data.

[0147] During downlink communications, the UPF-side N6 processor sends downlink service data to the N3 interface adapter via "Internal Interface_4." The N3 interface adapter retrieves the UPF message type from the downlink service data, looks up the UPF message type table [Radio Base Station Message Type, UPF Message Type], obtains the corresponding radio base station message type, constructs a downlink service adaptation message to be sent to the radio base station, and sends the adapted downlink service adaptation message to the radio-side L2 processor via "Internal Interface_3." The radio-side L2 processor can process the downlink service adaptation message using traditional methods to complete the reception of the downlink service data.

[0148] contrast Figure 7 、 Figure 8 and Figure 1B 、 Figure 1CIt can be seen that in the embodiment of the present disclosure, by removing the complex protocol processing of the original N2 interface and N3 interface, adding N2 interface adapters and N3 interface adapters to replace the processing method of the original N2 interface and N3 interface protocol stack, the N2 interface adapter and N3 interface adapter are mainly used for internal conversion of the original N2 interface and N3 interface of the wireless access network and the 5G core network, reducing the software system's consumption of hardware resources, reducing the transmission delay of control plane and user plane data, and improving the service experience quality. In addition, by adding N2 interface adapters and N3 interface adapters, modifications to the original software system are avoided, thereby improving system flexibility.

[0149] In one possible implementation of the disclosed embodiments, the integrated mobile edge computing device may further include a visualization interface for receiving multiple user-configured performance levels and the computing power requirements corresponding to different software at each performance level. In this embodiment, the server device is configured to store the corresponding relationship between each performance level and the computing power requirements of different software.

[0150] Exemplarily, the server device can store the correspondence between each performance level configured by the user through the visual interface and the computing power requirements of the corresponding different software in the local storage space of the server device. When the computing power resource allocation method described in the aforementioned embodiment is executed, the computing power requirements corresponding to each software under the preset performance level can be obtained from this storage space.

[0151] In the embodiment of the present disclosure, a visual interface is set up to receive multiple performance levels configured by the user and the computing power requirements corresponding to different software at each performance level, thereby realizing interactive operations based on the visual interface of the mobile edge computing integrated device and improving the flexibility of the system.

[0152] The embodiments of the present disclosure also provide a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of each embodiment of the computing power resource allocation method as described in the above embodiments are implemented. To avoid repeated description, they are not repeated here.

[0153] The embodiments of the present disclosure also provide a computer program product, which is used to execute the steps of each embodiment of the computing power resource allocation method as described in the above embodiments.

[0154] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0155] The foregoing description is intended only to provide specific embodiments of the present disclosure, intended to enable those skilled in the art to understand and implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments described herein, but rather to be construed in the broadest manner consistent with the principles and novel features disclosed herein.

Claims

1. A computing resource allocation method, characterized in that: Applied to a mobile edge computing integrated device, the method includes: Obtaining the computing power requirements corresponding to each software in the mobile edge computing integrated device at a preset performance level; Calculate the total computing power requirement of the software corresponding to the preset performance level based on the computing power requirement corresponding to each software; Comparing the total computing power requirement of the software with the total computing power supported by the hardware of the mobile edge computing integrated device; When the total computing power requirement of the software is less than the total computing power supported by the hardware, determining the performance level of the mobile edge computing integrated device to be the preset performance level; Allocate computing resources to each software according to the computing power requirements corresponding to each software at the preset performance level; The software in the mobile edge computing integrated device includes sensitive software and non-sensitive software, and the software is divided into the sensitive software and the non-sensitive software according to the delay requirements; The obtaining of the computing power requirements corresponding to each software in the mobile edge computing integrated device at a preset performance level includes: Obtain the maximum computing power required for each sensitive software under the preset performance level; Obtain the minimum and maximum computing power requirements for each non-sensitive software at the preset performance level; Calculating the total computing power requirement of the software corresponding to the preset performance level according to the computing power requirement corresponding to each software includes: Calculate the total computing power requirement of the sensitive software corresponding to the preset performance level based on the maximum computing power requirement corresponding to each sensitive software; Calculate the shared computing power requirement corresponding to each non-sensitive software based on the minimum computing power requirement and the maximum computing power requirement corresponding to each non-sensitive software, where the shared computing power requirement is obtained based on the difference between the maximum computing power requirement and the minimum computing power requirement corresponding to the same non-sensitive software; Calculate the total computing power requirement of the non-sensitive software corresponding to the preset performance level based on the shared computing power requirement corresponding to each non-sensitive software and the minimum computing power requirement corresponding to each non-sensitive software; Based on the total computing power requirements of sensitive software corresponding to the preset performance level, the total computing power requirements of non-sensitive software corresponding to the preset performance level, and the computing power requirements corresponding to the underlying software of the operating system, the total computing power requirements of the software corresponding to the preset performance level are calculated.

2. The computing resource allocation method according to claim 1, characterized in that: The method further comprises: When the total computing power requirement of the software is greater than or equal to the total computing power supported by the hardware, the preset performance level is lowered by one level, and the computing power requirement corresponding to each software in the mobile edge computing integrated device at the lowered performance level is obtained.

3. The computing resource allocation method according to claim 1, characterized in that: The mobile edge computing integrated device includes a visualization interface, and the method further includes: receiving a plurality of performance levels configured by a user through the visual interface and computing power requirements corresponding to different software at each performance level; The corresponding relationship between each performance level and the computing power requirements of the corresponding different software is stored.

4. The computing resource allocation method according to any one of claims 1 to 3, characterized in that: The method further comprises: Determine that the preset performance level is greater than or equal to 1.

5. The computing resource allocation method according to claim 4, characterized in that: The method further comprises: When the preset performance level is less than 1, the user is prompted that the computing power resource allocation has failed.

6. A computing resource allocation device, characterized in that: Applied to mobile edge computing integrated equipment, the device includes: An acquisition module, configured to obtain the computing power requirements corresponding to each software in the mobile edge computing integrated device at a preset performance level; A calculation module, configured to calculate the total computing power requirement of the software corresponding to the preset performance level based on the computing power requirement corresponding to each software; A comparison module, configured to compare the total computing power requirement of the software with the total computing power supported by the hardware of the mobile edge computing integrated device; a determination module, configured to determine that the performance level of the mobile edge computing integrated device is the preset performance level when the total computing power requirement of the software is less than the total computing power supported by the hardware; A resource allocation module, configured to allocate computing resources to each software according to the computing power requirements corresponding to each software at the preset performance level; The software in the mobile edge computing integrated device includes sensitive software and non-sensitive software, and the software is divided into the sensitive software and the non-sensitive software according to the delay requirements; The acquisition module is further used to: Obtain the maximum computing power required for each sensitive software under the preset performance level; Obtain the minimum and maximum computing power requirements for each non-sensitive software at the preset performance level; The computing module is further configured to: Calculate the total computing power requirement of the sensitive software corresponding to the preset performance level based on the maximum computing power requirement corresponding to each sensitive software; Calculate the shared computing power requirement corresponding to each non-sensitive software based on the minimum computing power requirement and the maximum computing power requirement corresponding to each non-sensitive software, where the shared computing power requirement is obtained based on the difference between the maximum computing power requirement and the minimum computing power requirement corresponding to the same non-sensitive software; Calculate the total computing power requirement of the non-sensitive software corresponding to the preset performance level based on the shared computing power requirement corresponding to each non-sensitive software and the minimum computing power requirement corresponding to each non-sensitive software; Based on the total computing power requirements of sensitive software corresponding to the preset performance level, the total computing power requirements of non-sensitive software corresponding to the preset performance level, and the computing power requirements corresponding to the underlying software of the operating system, the total computing power requirements of the software corresponding to the preset performance level are calculated.

7. A mobile edge computing integrated device, characterized in that: include: A mobile edge computing management platform, 5G core network software, 5G network management software, 5G wireless base station protocol software, N2 interface adapter, and N3 interface adapter deployed on the same server device are used to execute a computing power resource allocation method, wherein the computing power resource allocation method determines the total computing power requirement of the software corresponding to the preset performance level according to the computing power requirement corresponding to each software in the mobile edge computing integrated device at the preset performance level, determines the performance level of the mobile edge computing integrated device according to the difference between the total computing power requirement of the software and the total computing power supported by the hardware of the mobile edge computing integrated device, and allocates computing power resources to each software; The N2 interface adapter is used to establish a signaling plane adaptation information mapping relationship, and the N3 interface adapter is used to establish a user plane adaptation information mapping relationship, wherein the signaling plane adaptation information mapping relationship includes a mapping relationship between wireless base station information and AMF information, a mapping relationship between wireless base station message type and AMF message type, and a mapping relationship between a user identifier within a wireless base station and a user identifier within an AMF; the user plane adaptation information mapping relationship includes a mapping relationship between a wireless base station message type and a UPF message type.

8. The mobile edge computing integrated device according to claim 7, characterized in that: The 5G wireless base station protocol software includes a first processor, and the 5G core network software includes a second processor; The first processor sends an uplink signaling message through the first internal interface; The N2 interface adapter obtains the wireless base station information, the wireless base station message type and the user identifier within the wireless base station from the uplink signaling message, and queries the signaling plane adaptation information mapping relationship to determine the corresponding target AMF information, the target AMF message type and the target AMF user identifier, and constructs an uplink NAS adaptation message according to the target AMF information, the target AMF message type and the target AMF user identifier, and sends the uplink NAS adaptation message to the second processor through the second internal interface.

9. The mobile edge computing integrated device according to claim 8, characterized in that: The second processor sends a downlink NAS message to the N2 interface adapter through the second internal interface; The N2 interface adapter obtains AMF information, AMF message type, and AMF intra-user identifier from the downlink NAS message, queries the signaling plane adaptation information mapping relationship, determines the corresponding target wireless base station information, target wireless base station message type, and target wireless base station intra-user identifier, and constructs a downlink NAS adaptation message according to the target wireless base station information, target wireless base station message type, and target wireless base station intra-user identifier, and sends the downlink NAS adaptation message to the first processor through the first internal interface.

10. The mobile edge computing integrated device according to claim 7, characterized in that: The 5G wireless base station protocol software includes a third processor, and the 5G core network software includes a fourth processor; The third processor sends uplink service data to the N3 interface adapter through the third internal interface; The N3 interface adapter obtains the wireless base station message type from the uplink service data, and queries the user plane adaptation information mapping relationship according to the wireless base station message type, determines the corresponding target UPF message type, and constructs an uplink service adaptation message according to the target UPF message type, and sends the uplink service adaptation message to the fourth processor through the fourth internal interface.

11. The mobile edge computing integrated device according to claim 10, characterized in that: The fourth processor sends downlink service data to the N3 interface adapter through the fourth internal interface; The N3 interface adapter obtains the UPF message type from the downlink service data, queries the user plane adaptation information mapping relationship according to the UPF message type, determines the corresponding target wireless base station message type, and constructs a downlink service adaptation message according to the target wireless base station message type, and sends the downlink service adaptation message to the third processor through the third internal interface.

12. The mobile edge computing integrated device according to claim 7, characterized in that: Also includes: A visual interface for receiving multiple performance levels configured by the user and the computing power requirements corresponding to different software at each performance level; The server device is used to store the corresponding relationship between each performance level and the computing power requirements of the corresponding different software.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the computing power resource allocation method according to any one of claims 1 to 5.

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