CN113438678B - A method and device for allocating cloud resources for network slicing - Google Patents

Abstract

The application provides a method and a device for distributing cloud resources for a network slice. According to the method, the central cloud node and the edge cloud nodes in the cloud resources are reasonably distributed to each virtual network in the network slice according to the communication delay between each edge cloud node and the central cloud node, the data processing delay between each edge cloud node and the central cloud node and the computing capacity, balance between the computing efficiency and delay of each virtual network when the virtual network runs on the distributed cloud nodes can be guaranteed, and the utilization rate of the cloud resources can be improved.

Description

A method and device for allocating cloud resources for network slicing

technical field

The present application relates to the field of communication technologies, and in particular to a method and device for allocating cloud resources for network slices.

Background technique

In order to meet the technical requirements of 5G ( ^5th generation, 5G) communication network with fast transmission rate, low delay and high network efficiency, the communication field proposes to deploy network slicing communication technology in the network infrastructure on which 5G depends.

At present, in the 5G communication network, the available cloud resources are dynamically distributed to network slices mainly according to the instantaneous user demand and the current load of the network.

A network slice consists of a series of virtual networks, and the virtual networks on each network slice can be split. Therefore, in the field of communication, it is further proposed that each virtual network in the network slice can be deployed on a suitable cloud unit, that is, the virtual network of the network slice can be used as a unit to allocate cloud resources for the virtual network of the network slice, so as to improve the utilization of cloud resources. utilization rate. However, the field of communication has not specifically proposed how to allocate cloud resources for the virtual network of network slicing.

Therefore, how to allocate cloud resources for the virtual network of network slicing has become an urgent technical problem to be solved.

Contents of the invention

The embodiment of the present application provides a method and device for allocating cloud resources for network slices, which fully considers the delay requirements of the virtual network, the requirements of computing resources, and the distribution of nodes, determines the optimal solution for virtual network deployment, and realizes The balance between delay and computing efficiency makes full use of cloud resources and improves the utilization of cloud resources.

In a first aspect, the present application provides a method for allocating cloud resources for network slices, the network slices include multiple virtual networks, and the cloud resources include central cloud nodes and multiple edge cloud nodes, the method includes: obtaining the The first communication delay between each edge cloud node in a plurality of edge cloud nodes and the central cloud node; obtaining the first communication delay when each virtual network in the plurality of virtual networks is deployed on each edge cloud node A data processing delay; obtaining a second data processing delay when each virtual network in the plurality of virtual networks is deployed on the central cloud node; according to the first communication delay, the first data processing time delay, the second data processing delay, the computing capability of each edge cloud node and the computing capability of the central cloud node, determine the target cloud node assigned to each virtual network, and the target cloud node For a cloud node in the cloud resources, when each virtual network in the multiple virtual networks runs on a corresponding target cloud node, the total computing resources required by the multiple virtual networks are the smallest.

With reference to the first aspect, in a first possible implementation manner, the total computing resources required by the multiple virtual networks satisfy the following relationship:

表示l_a的边缘云节点的计算速率，

表示所述中心云节点的计算速率，E表示所述多个边缘云节点所能承载的最大计算量之和，F表示所述中心云节点所能承载的最大计算量之和，

表示l_a的边缘云节点对应的所述第一数据处理时延，

表示所述中心云节点对应的所述第二数据处理时延，

表示l_a对应的所述第一通信延迟，β为预设值，

的取值为1或0，

表示所述第i个虚拟网络部署在l_a的边缘云节点上，

表示所述第i个虚拟网络部署在所述中心云节点上。Among them, l _a represents the a-th link in the link set A, which includes the link between each edge node and the central node, and I represents the plurality of virtual networks , i represents the i-th virtual network in the plurality of virtual networks,

Indicates the computing rate of the edge cloud node of _la ,

Represents the computing rate of the central cloud node, E represents the sum of the maximum amount of computation that the multiple edge cloud nodes can carry, and F represents the sum of the maximum computation that the central cloud node can carry,

Indicates the first data processing delay corresponding to the edge cloud node of _la ,

Indicates the second data processing delay corresponding to the central cloud node,

Indicates the first communication delay corresponding to _la , β is a preset value,

The value of is 1 or 0,

Indicates that the i-th virtual network is deployed on the edge cloud node of _la ,

Indicates that the i-th virtual network is deployed on the central cloud node.

In this method, by obtaining the communication delay between each edge cloud node and the central cloud node in the multiple edge cloud nodes, the data processing delay and the computing power of the node, the central cloud node and multiple cloud nodes allocated for the virtual network are determined. The edge cloud node improves the delay limitation of the existing mobile communication system, realizes the balance between computing efficiency and transmission delay, and improves the utilization rate of cloud resources.

With reference to the first aspect, in a second possible implementation manner, the obtaining the first communication delay between each edge cloud node in the plurality of edge cloud nodes and the central cloud node includes: obtaining the A first distance between each edge cloud node in a plurality of edge cloud nodes and the central cloud node; calculate the first communication delay according to the first distance, and the first communication delay is equal to the first distance The ratio of the transmission rate of the optical fiber link between each edge cloud node and the central cloud node.

With reference to the first aspect, in a third possible implementation manner, the method further includes: calculating the calculation rate of each cloud node in the cloud resources according to the actual calculation rate of each cloud node and the maximum amount of calculation that can be carried by each cloud node The computing power of each cloud node, the actual computing rate of each cloud node is determined by the computing rate of the floating-point calculation of each cloud node and the frequency of the CPU, and the maximum amount of calculation that can be carried by each cloud node is determined by the Determine the computing resources occupied by each cloud node.

With reference to the first aspect or any of the above possible implementation manners, in a fourth possible implementation manner, according to the first communication delay, the first data processing delay and the second data processing time Determining the target cloud node assigned to each virtual network includes: using a heuristic algorithm to determine according to the first communication delay, the first data processing delay and the second data processing delay as The target cloud node assigned by each virtual network.

In a second aspect, the present application provides an apparatus for allocating cloud resources for a network slice, the network slice includes multiple virtual networks, the cloud resources include a central cloud node and a plurality of edge cloud nodes, the apparatus includes: an acquisition module, Obtain the first communication delay between each edge cloud node in the plurality of edge cloud nodes and the central cloud node; the acquisition module is also used to acquire the deployment of each virtual network in the plurality of virtual networks The first data processing time delay of each edge cloud node; the obtaining module is also used to obtain the second data processing time when each virtual network in the plurality of virtual networks is deployed on the central cloud node Delay; a determining module, configured to determine according to the first communication delay, the first data processing delay, the second data processing delay, the computing capability of each edge cloud node and the central cloud node Computing capability, determining a target cloud node assigned to each virtual network, where the target cloud node is a cloud node in the cloud resource, and each virtual network in the plurality of virtual networks is at the corresponding target cloud node When running on , the total computing resources required by the multiple virtual networks are minimal.

With reference to the second aspect, in a first possible implementation manner, the total computing resources required by the multiple virtual networks satisfy the following relationship:

表示l_a的边缘云节点的计算速率，

表示所述中心云节点的计算速率，E表示所述多个边缘云节点所能承载的最大计算量之和，F表示所述中心云节点所能承载的最大计算量之和，

表示l_a的边缘云节点对应的所述第一数据处理时延，

表示所述中心云节点对应的所述第二数据处理时延，

表示l_a对应的所述第一通信延迟，β为预设值，

的取值为1或0，

表示所述第i个虚拟网络部署在l_a的边缘云节点上，

表示所述第i个虚拟网络部署在所述中心云节点上。Among them, l _a represents the a-th link in the link set A, which includes the link between each edge node and the central node, and I represents the plurality of virtual networks , i represents the i-th virtual network in the plurality of virtual networks,

Indicates the computing rate of the edge cloud node of _la ,

Represents the computing rate of the central cloud node, E represents the sum of the maximum amount of computation that the multiple edge cloud nodes can carry, and F represents the sum of the maximum computation that the central cloud node can carry,

Indicates the first data processing delay corresponding to the edge cloud node of _la ,

Indicates the second data processing delay corresponding to the central cloud node,

Indicates the first communication delay corresponding to _la , β is a preset value,

The value of is 1 or 0,

Indicates that the i-th virtual network is deployed on the edge cloud node of _la ,

Indicates that the i-th virtual network is deployed on the central cloud node.

With reference to the second aspect, in a second possible implementation manner, the obtaining module is specifically configured to: obtain a first distance between each edge cloud node in the plurality of edge cloud nodes and the central cloud node; Calculate the first communication delay according to the first distance, the first communication delay is equal to the difference between the first distance and the transmission rate of the optical fiber link between each edge cloud node and the central cloud node Compare.

With reference to the second aspect, in a third possible implementation manner, the device further includes a computing module, configured to calculate the actual computing rate and maximum capacity of each cloud node in the cloud resource Quantitative calculation of the computing power of each cloud node, the actual computing rate of each cloud node is determined by the computing rate of the floating point calculation of each cloud node and the frequency of the CPU, the maximum of each cloud node The amount of computing that can be carried is determined by the computing resources occupied by each cloud node.

With reference to the second aspect or any one of the above possible implementation manners, in a fourth possible implementation manner, the determining module is specifically configured to: the determining module uses a heuristic algorithm according to the first communication delay, the The first data processing delay and the second data processing delay determine the target cloud node assigned to each virtual network.

In a third aspect, the present application provides a device for allocating cloud resources for network slices, including: multiple memories and multiple processors; the memories are used to store program instructions; the processors are used to call programs in the memories The instruction executes the method described in the first aspect or any one of the possible implementation manners.

When the system is a computing device, in some implementations the system can also include a transceiver or communication interface for communicating with other devices.

When the system is a chip for a computing device, in some implementations, the system may further include a communication interface for communicating with other devices in the computing device, such as for communicating with a transceiver of the computing device.

In a fourth aspect, the present application provides a computer-readable medium, where the computer-readable medium stores program code for execution by a computer, and the program code includes a program code for executing the program described in the first aspect or any one of the possible implementation manners. instructions for the method described above.

In a fifth aspect, the present application provides a computer program product including instructions, and when the computer program product is run on a processor, the processor is made to implement the method in the first aspect or any one of the implementation manners.

Description of drawings

Fig. 1 is the cloud resource distribution schematic diagram of an embodiment of the present application;

FIG. 2 is a flowchart of a method for allocating cloud resources for network slices according to an embodiment of the present application;

FIG. 3 is a schematic diagram of an apparatus for allocating cloud resources for network slices according to an embodiment of the present application;

FIG. 4 is a schematic structural diagram of allocating cloud resources for network slices according to an embodiment of the present application.

Detailed ways

For ease of understanding, relevant terms involved in this application are firstly described.

1. Network slicing

Network slicing is an on-demand networking method that allows operators to separate multiple virtual end-to-end networks on a unified infrastructure. Each network slice performs logic from the wireless access network bearer network to the core network. isolated to suit a wide variety of applications. A network slice can be divided into at least three parts: wireless network sub-slice, bearer network sub-slice and core network sub-slice.

The core of network slicing technology is network functions virtualization (network functions virtualization, NFV). NFV separates the hardware and software parts from the traditional network. The hardware is deployed by a unified server, and the software is undertaken by different network functions (network functions, NF). , so as to meet the needs of flexible assembly business.

Network slicing is a concept based on logic, which is the reorganization of resources. Reorganization is to select the required virtual machines and physical resources for a specific communication service type according to the service level agreement (SLA).

2. Edge cloud

The widely accepted definition of cloud computing at this stage is: Cloud computing is a mode in which scalable, elastic, and shared physical and virtual resource pools are provisioned and managed in an on-demand self-service manner, and network access is provided. The cloud computing model consists of key characteristics, cloud computing roles and activities, capability types and cloud service categories, cloud deployment models, and cloud computing common concerns. The current concept of cloud computing is based on centralized resource management and control. Even if multiple data centers are interconnected, all software and hardware resources are still managed, scheduled and sold as unified resources. With the advent of 5G, the Internet of Things era, and the gradual increase in cloud computing applications, centralized clouds can no longer meet the cloud resource requirements of "large connections, low latency, and large bandwidth" on the terminal side. Combined with the concept of edge computing, cloud computing will inevitably develop to the next technical stage, which is to expand the capabilities of cloud computing to the edge side closer to the terminal, and realize the sinking of cloud computing services through the unified management and control of the cloud edge terminal. To end-to-end cloud services, the concept of edge cloud computing has also emerged.

Edge cloud computing, referred to as edge cloud, is a cloud computing platform built on edge infrastructure based on the core of cloud computing technology and edge computing capabilities. Form an elastic cloud platform with comprehensive computing, network, storage, and security capabilities at the edge, and form an end-to-end technical architecture of "cloud, edge, and terminal collaboration" with the central cloud and IoT terminals. Computing, intelligent data analysis and other tasks are processed at the edge, reducing response delay, reducing cloud pressure, reducing bandwidth costs, and providing cloud services such as network-wide scheduling and computing power distribution. The infrastructure of edge cloud computing includes but is not limited to: distributed IDC, edge infrastructure of operator communication network, edge devices such as edge client nodes (such as edge gateways, home gateways, etc.) and their corresponding network environments.

Edge cloud computing is essentially based on cloud computing technology, providing low-latency, self-organizing, definable, schedulable, high-security, and open-standard distributed cloud services for terminals of the "Internet of Everything". The edge cloud can adopt a unified architecture, unified interface, and unified management with the central cloud to the greatest extent, which can minimize user development and operation and maintenance costs, and truly expand the scope of cloud computing to a place closer to the data source. Make up for the deficiencies of cloud computing with traditional architecture in some application scenarios.

The implementation of the embodiment of the present application will be described in detail below with reference to the accompanying drawings.

FIG. 1 is a schematic diagram of cloud resource distribution according to an embodiment of the present application. As shown in Figure 1, a central cloud can be extended to multiple edge clouds. The central cloud has functions such as storage, computing, network, artificial intelligence (AI), big data, and security. The edge cloud is an extension of the central cloud. Extend some services or capabilities of the cloud (including but not limited to storage, computing, network, AI, big data, security, etc.) to the edge infrastructure, including but not limited to: distributed Internet data center (internet data center, IDC), operator communication network edge infrastructure, and edge-side customer nodes (such as edge gateways, home gateways, etc.) and other edge devices and their corresponding network environments. The central cloud and the edge cloud cooperate with each other to realize the capabilities of center and edge collaboration, network-wide computing power scheduling, and network-wide unified management and control, and truly realize the "ubiquitous" cloud.

Edge cloud, also known as edge cloud computing, is essentially based on cloud computing technology and provides low-latency, self-organizing, definable, schedulable, high-security, and open-standard distributed cloud services for terminals of the "Internet of Everything". The edge cloud can adopt a unified architecture, unified interface, and unified management with the central cloud to the greatest extent, which can minimize user development and operation and maintenance costs, and truly expand the scope of cloud computing to a place closer to the data source. Make up for the deficiencies of cloud computing with traditional architecture in some application scenarios.

In the embodiment of the present application, the nodes in the central cloud are called central cloud nodes, and the nodes in the edge cloud are called edge cloud nodes.

FIG. 2 is a flowchart of a method for allocating cloud resources for network slices according to an embodiment of the present application. As shown in FIG. 2, the method may include S201, S202, S203 and S204.

S201. Obtain a first communication delay between each edge cloud node in the plurality of edge cloud nodes and the central cloud node.

As an example, the distribution positions of the edge cloud nodes and the central cloud nodes can be acquired, the distribution positions include distances and spatial positions between nodes; and then according to the acquired distribution positions, determine the The first distance between each edge cloud node and the central cloud node; calculate the first communication delay according to the obtained first distance, and the first communication delay is equal to the first distance and each edge cloud node and The transmission rate ratio of the optical fiber links between the central cloud nodes.

Optionally, in this embodiment, the azimuth map between the edge cloud node and the central cloud node can also be simulated and drawn according to the obtained distribution positions of the edge cloud node and the central cloud node, and after calculating the first communication delay, it can be On the drawn azimuth map, the first communication delay is marked to make the data more intuitive.

S202. Obtain a first data processing delay when each virtual network in the plurality of virtual networks is deployed on each edge cloud node.

As an example, the first data processing delay when each virtual network is deployed on each edge cloud node may be determined according to resource requirements of each virtual network and resource conditions of each edge cloud node.

S203. Obtain a second data processing delay when each virtual network in the plurality of virtual networks is deployed on the central cloud node.

As an example, the second data processing delay when each virtual network is deployed on the central cloud node may be determined according to the resource requirements of each virtual network and the resource conditions of the central cloud node.

S204. According to the first communication delay, the first data processing delay, the second data processing delay, the computing capability of each edge cloud node and the computing capability of the central cloud node, determine as A target cloud node assigned by each virtual network, where the target cloud node is a cloud node in the cloud resource.

In some implementations, the cloud resources include a central cloud node and a plurality of edge cloud nodes, and the computing capability of each cloud node is calculated according to the actual computing rate of each cloud node in the cloud resource and the maximum amount of computing that can be carried, wherein, The actual calculation rate of each cloud node is determined by the calculation rate of each cloud node's floating-point calculation and the frequency of the CPU; the maximum amount of calculation that each cloud node can carry is determined by the computing resources occupied by each cloud node. For example, if there are many computing resources, the amount of calculations that can be tolerated is large. The amount of calculations can be carried out by stress testing the nodes, that is, by continuously increasing the pressure, it is determined that the nodes meet their own performance conditions. The maximum pressure that can be tolerated is the maximum calculation amount.

In some implementations of this embodiment, according to the first communication delay, the first data processing delay, the second data processing delay, the computing capability of each edge cloud node and the computing capability of the central cloud node, it is determined The target cloud nodes assigned by the network are central cloud nodes and multiple edge cloud nodes. When each virtual network in multiple virtual networks runs on the corresponding target cloud node, the total computing resources required by multiple virtual networks can meet the following example Sexual relationship formula:

表示l_a的边缘云节点的计算速率，

表示所述中心云节点的计算速率，E表示所述多个边缘云节点所能承载的最大计算量之和，F表示所述中心云节点所能承载的最大计算量之和，

表示l_a的边缘云节点对应的所述第一数据处理时延，

表示所述中心云节点对应的所述第二数据处理时延，

表示l_a对应的所述第一通信延迟，β为预设值，

的取值为1或0，

表示所述第i个虚拟网络部署在l_a的边缘云节点上，

表示所述第i个虚拟网络部署在所述中心云节点上。Among them, l _a represents the a-th link in the link set A, which includes the link between each edge node and the central node, and I represents the plurality of virtual networks , i represents the i-th virtual network in the plurality of virtual networks,

Indicates the computing rate of the edge cloud node of _la ,

Represents the computing rate of the central cloud node, E represents the sum of the maximum amount of computation that the multiple edge cloud nodes can carry, and F represents the sum of the maximum computation that the central cloud node can carry,

Indicates the first data processing delay corresponding to the edge cloud node of _la ,

Indicates the second data processing delay corresponding to the central cloud node,

Indicates the first communication delay corresponding to _la , β is a preset value,

The value of is 1 or 0,

Indicates that the i-th virtual network is deployed on the edge cloud node of _la ,

Indicates that the i-th virtual network is deployed on the central cloud node.

可以称为目标函数，

可以称为边缘云节点的资源分配约束函数，

可以称为中心云节点的资源分配预设函数。In this example,

can be called the objective function,

can be called the resource allocation constraint function of edge cloud nodes,

It can be called the resource allocation preset function of the central cloud node.

That is to say, the method of this embodiment is to use the optimal solver to determine the objective The optimal deployment scheme of the virtual network with the smallest value of the function.

Optionally, the optimal solver can be based on the particle swarm algorithm, heuristic algorithm, genetic algorithm, etc. For example, when the heuristic algorithm is specifically solved, the virtual network is deployed on some of the cloud nodes when the constraints are met , is regarded as the optimal solution, wherein the selection of the cloud node can be selected after sorting in ascending or descending order according to the delay time of the cloud nodes.

In some implementations, the objective function value of the total computing resources required by multiple virtual networks in the scheme is regarded as the optimal value, all possible schemes are traversed, and other possible schemes are calculated one by one for the objective function value, if any If the objective function value of the scheme is lower than the optimal value, use this scheme to replace the previously determined optimal scheme until the traversal time reaches the preset time, then terminate the traversal; use the determined optimal deployment scheme to deploy multiple virtual networks , that is, the virtual network can be divided into each edge node as much as possible, so that the available computing resources can be optimally utilized.

In this embodiment, by obtaining the communication delay between the central cloud node and each edge cloud node, the data processing delay and the computing power of the node, the central cloud node and multiple edge cloud nodes allocated for the virtual network are determined to improve The delay limitation existing in the existing mobile communication system is overcome, the balance between computing efficiency and transmission delay is realized, and the utilization rate of cloud resources is improved.

As an implementation, in the embodiment of the present application, the second communication delay between each edge cloud node in the plurality of edge cloud nodes and each other edge cloud node in the plurality of edge cloud nodes can also be obtained .

As an example, the distribution positions of edge cloud nodes and other edge cloud nodes can be obtained, and the distribution positions include distances and spatial positions between nodes; according to the obtained distribution positions, among the plurality of edge cloud nodes A second distance between each edge cloud node and each other edge cloud node in the plurality of edge cloud nodes; calculate a second communication delay according to the obtained second distance, and the second communication delay is equal to the second distance The ratio of the transmission rate of the optical fiber link between each edge cloud node and each other edge cloud node.

Optionally, in this embodiment, according to the obtained distribution positions of the edge cloud node and other cloud edge nodes, the azimuth map between the edge cloud node and other edge cloud nodes can be simulated and drawn, and after calculating the second communication delay, The second communication delay can be marked on the drawn azimuth map to make the data more intuitive.

In this implementation manner, the link set may also include links between each edge cloud node and each other edge cloud node.

FIG. 3 is a schematic diagram of an apparatus for allocating cloud resources for network slices according to an embodiment of the present application. The device shown in FIG. 3 may be used to execute the method described in any one of the foregoing embodiments. As shown in FIG. 3 , the apparatus 300 of this embodiment may include: an acquisition module 301 , a determination module 302 and a calculation module 303 .

In an example, the apparatus 300 may be used to execute the method described in FIG. 2 . For example, the acquisition module 301 may be used to execute S201, S202 and S203, the determination module 302 may be used to execute S204, and the calculation module 303 may be used to execute S201 and S204.

FIG. 4 is a schematic structural diagram of allocating cloud resources for network slices according to an embodiment of the present application. The device shown in FIG. 4 may be used to execute the method described in any one of the foregoing embodiments.

As shown in FIG. 4 , the apparatus 400 of this embodiment includes: a memory 401 , a processor 402 , a communication interface 403 and a bus 404 . Wherein, the memory 401 , the processor 402 , and the communication interface 403 are connected to each other through the bus 404 .

The memory 401 may be a read only memory (read only memory, ROM), a static storage device, a dynamic storage device or a random access memory (random access memory, RAM). The memory 401 may store a program, and when the program stored in the memory 401 is executed by the processor 402, the processor 402 is configured to execute each step of the method shown in FIG. 2 .

The processor 402 may adopt a general-purpose central processing unit (central processing unit, CPU), a microprocessor, an application specific integrated circuit (application specific integrated circuit, ASIC), or one or more integrated circuits for executing related programs to Implement the methods in the various embodiments of the present application.

The processor 402 may also be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the method in each embodiment of the present application may be completed by an integrated logic circuit of hardware in the processor 402 or instructions in the form of software.

The above-mentioned processor 402 can also be a general-purpose processor, a digital signal processor (digital signal processing, DSP), an application-specific integrated circuit (ASIC), a ready-made programmable gate array (field programmable gate array, FPGA) or other programmable logic devices, discrete gates Or transistor logic devices, discrete hardware components. Various methods, steps, and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register. The storage medium is located in the memory 401, and the processor 402 reads the information in the memory 401, and combines its hardware to complete the functions required by the units included in the device of the present application. For example, it can perform various steps/functions of the embodiment shown in FIG. 2 .

The communication interface 403 may use, but is not limited to, a transceiver device such as a transceiver to implement communication between the device 400 and other devices or communication networks.

The bus 404 may include pathways for transferring information between various components of the apparatus 400 (eg, memory 401 , processor 402 , communication interface 403 ).

It should be understood that the apparatus 400 shown in the embodiment of the present application may be a computing device, or may also be a chip configured in the computing device.

It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the non-volatile memory can be read-only memory (read-only memory, ROM), programmable read-only memory (programmable ROM, PROM), erasable programmable read-only memory (erasable PROM, EPROM), electrically programmable Erases programmable read-only memory (electrically EPROM, EEPROM) or flash memory. Volatile memory can be random access memory (RAM), which acts as external cache memory. By way of illustration and not limitation, many forms of random access memory (RAM) are available such as static random access memory (static RAM (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (RAM), Access memory (synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (double data rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchronous connection dynamic random access memory (synchlink DRAM, SLDRAM) and direct memory bus random access memory (direct rambus RAM, DR RAM).

The above-mentioned embodiments may be implemented in whole or in part by software, hardware, firmware or other arbitrary combinations. When implemented using software, the above-described embodiments may be implemented in whole or in part in the form of computer program products. The computer program product comprises one or more computer instructions or computer programs. When the computer instruction or computer program is loaded or executed on the computer, the processes or functions according to the embodiments of the present application will be generated in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website, computer, server or data center Transmission to another website site, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or a data center that includes one or more sets of available media. The available media may be magnetic media (eg, floppy disk, hard disk, magnetic tape), optical media (eg, DVD), or semiconductor media. The semiconductor medium may be a solid state drive.

It should be understood that the term "and/or" in this article is only an association relationship describing associated objects, which means that there may be three relationships, for example, A and/or B may mean: A exists alone, and A and B exist at the same time , there are three cases of B alone, where A and B can be singular or plural. In addition, the character "/" in this article generally indicates that the related objects are an "or" relationship, but it may also indicate an "and/or" relationship, which can be understood by referring to the context.

In this application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one item (piece) of a, b, or c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, c can be single or multiple .

It should be understood that, in various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the order of execution, and the execution order of the processes should be determined by their functions and internal logic, and should not be used in the embodiments of the present application. The implementation process constitutes any limitation.

Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present application.

Those skilled in the art can clearly understand that for the convenience and brevity of the description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or May be integrated into another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.

If the functions described above are realized in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media capable of storing program codes such as U disk, mobile hard disk, read-only memory, random access memory, magnetic disk or optical disk.

The above is only a specific implementation of the application, but the scope of protection of the application is not limited thereto. Anyone familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the application. Should be covered within the protection scope of this application. Therefore, the protection scope of the present application should be determined by the protection scope of the claims.

Claims (8)

1. A method of allocating cloud resources for a network slice, the network slice comprising a plurality of virtual networks, the cloud resources comprising a central cloud node and a plurality of edge cloud nodes, the method comprising:

obtaining a first communication delay between each edge cloud node of the plurality of edge cloud nodes and the center cloud node;

acquiring first data processing time delay when each virtual network in the plurality of virtual networks is deployed at each edge cloud node;

acquiring a second data processing time delay when each virtual network in the plurality of virtual networks is deployed at the central cloud node;

determining a target cloud node allocated to each virtual network according to the first communication delay, the first data processing delay, the second data processing delay, the computing capacity of each edge cloud node and the computing capacity of the center cloud node, wherein the target cloud node is a cloud node in the cloud resources, and when each virtual network in the plurality of virtual networks runs on the corresponding target cloud node, the total computing resources required by the plurality of virtual networks are minimum;

the total computing resources required by the plurality of virtual networks satisfy the following relation:

wherein l _a Representing an a-th link in a link set A, the link set A including a link between each edge cloud node and the center cloud node, I representing the plurality of virtual networks, I representing an ith virtual network in the plurality of virtual networks,

is represented by _a The computing rate of the edge cloud node of (a),

representing the computing rate of the central cloud node, E representing the sum of the maximum computing capacity which can be carried by the plurality of edge cloud nodes, F representing the sum of the maximum computing capacity which can be carried by the central cloud node,

represents the said l _a The first data processing delay corresponding to the edge cloud node of (1),

representing the second data processing latency corresponding to the central cloud node,

is represented by _a Corresponding to the first communication delay, beta is a preset value,

is a value of 1 or 0,

representing the i-th virtual network deployment at _a On the edge cloud node of (1) a,

indicating that the ith virtual network is deployed on the central cloud node, and S indicates total computing resources required by the plurality of virtual networks.

2. The method of claim 1, wherein obtaining the first communication delay between each edge cloud node of the plurality of edge cloud nodes and the central cloud node comprises:

obtaining a first distance between each edge cloud node of the plurality of edge cloud nodes and the center cloud node;

calculating the first communication delay as a function of the first distance, the first communication delay being equal to a ratio of the first distance to a transmission rate of a fiber link between each of the edge cloud nodes and the center cloud node.

3. The method according to any one of claims 1 to 2, wherein the determining the target cloud node allocated to each virtual network according to the first communication delay, the first data processing delay and the second data processing delay comprises:

and determining a target cloud node distributed to each virtual network according to the first communication delay, the first data processing delay and the second data processing delay by using a heuristic algorithm.

4. An apparatus for allocating cloud resources for a network slice, the network slice comprising a plurality of virtual networks, the cloud resources comprising a central cloud node and a plurality of edge cloud nodes, the apparatus comprising:

an obtaining module, configured to obtain a first communication delay between each edge cloud node of the plurality of edge cloud nodes and the center cloud node;

the obtaining module is further configured to obtain a first data processing delay of each virtual network in the multiple virtual networks when the virtual network is deployed at each edge cloud node;

the obtaining module is further configured to obtain a second data processing delay of each of the multiple virtual networks when the virtual network is deployed at the central cloud node;

a determining module, configured to determine, according to the first communication delay, the first data processing delay, the second data processing delay, the computing capability of each edge cloud node, and the computing capability of the center cloud node, a target cloud node allocated to each virtual network, where the target cloud node is a cloud node in the cloud resources, and when each virtual network in the multiple virtual networks runs on the corresponding target cloud node, a total computing resource required by the multiple virtual networks is minimum;

the total computing resources required by the plurality of virtual networks satisfy the following relation:

wherein l _a Representing an a-th link in a link set A, wherein the link set A comprises links between each edge cloud node and the center cloud node, I represents the multiple virtual networks, I represents the ith virtual network in the multiple virtual networks,

represents l _a The computing rate of the edge cloud node of (c),

representing the computing rate of the central cloud node, E representing the sum of the maximum computing capacity which can be carried by the plurality of edge cloud nodes, F representing the sum of the maximum computing capacity which can be carried by the central cloud node,

is represented by _a The first data processing delay corresponding to the edge cloud node of (a),

representing the second data processing latency corresponding to the central cloud node,

represents l _a Corresponding to the first communication delay, beta is a preset value,

is a value of 1 or 0, and,

representing the deployment of the ith virtual network at _a On the edge cloud node of (1) a,

indicating that the ith virtual network is deployed on the central cloud node, and S indicates total computing resources required by the plurality of virtual networks.

5. The apparatus of claim 4, wherein the obtaining module is specifically configured to:

obtaining a first distance between each edge cloud node of the plurality of edge cloud nodes and the center cloud node;

calculating the first communication delay as a function of the first distance, the first communication delay being equal to a ratio of the first distance to a transmission rate of a fiber link between each of the edge cloud nodes and the center cloud node.

6. The apparatus according to any one of claims 4 to 5, wherein the determining module is specifically configured to:

and determining the target cloud nodes distributed to each virtual network according to the first communication delay, the first data processing delay and the second data processing delay by using a heuristic algorithm.

7. An apparatus for allocating cloud resources for a network slice, comprising: a plurality of memories and a plurality of processors;

the memory is to store program instructions;

the processor is configured to invoke program instructions in the memory to perform the method of any of claims 1 to 3.

8. A computer-readable medium, characterized in that the computer-readable medium stores program code for computer execution, the program code comprising instructions for performing the method of any of claims 1 to 3.

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