CN114650234A

CN114650234A - Data processing method and device and server

Info

Publication number: CN114650234A
Application number: CN202210252778.6A
Authority: CN
Inventors: 郑丹阳; 沈一春; 沈纲祥; 揭水平; 符小东; 房洪莲
Original assignee: Zhongtian Communication Technology Co ltd; Jiangsu Zhongtian Technology Co Ltd; Zhongtian Broadband Technology Co Ltd
Current assignee: Zhongtian Communication Technology Co ltd; Jiangsu Zhongtian Technology Co Ltd; Zhongtian Broadband Technology Co Ltd
Priority date: 2022-03-14
Filing date: 2022-03-14
Publication date: 2022-06-21
Anticipated expiration: 2042-03-14
Also published as: CN114650234B

Abstract

The specification provides a data processing method, a data processing device and a server. Based on the method, when a target service function chain needs to be deployed in a target network system, parameter data of the target service function chain, network topology data and operation parameters of the target network system are obtained; then, according to a preset construction rule, constructing a target weighted energy consumption level auxiliary graph aiming at the target service function chain by using the parameter data of the target service function chain, and the network topology data and the operation parameters of the target network system; and determining a corresponding target deployment strategy according to the target weighted energy consumption level auxiliary graph. Therefore, the target deployment strategy with low energy consumption and good effect can be accurately determined by constructing and based on the target weighted energy consumption level auxiliary graph and fully utilizing the calculation resources of the existing function instance of the target network system, and the target service function chain can be deployed and operated in the target network system with low processing cost according to the target deployment strategy.

Description

Data processing method and device and server

Technical Field

The present specification belongs to the field of network technologies, and in particular, to a data processing method, apparatus, and server.

Background

Based on the network function virtualization framework, a service requested by a demand party is usually split into a plurality of service functions connected in a chain form to obtain a corresponding service function chain. In order to implement the business service, the service function chain needs to be deployed into a corresponding network system (e.g., a network system of a micro data center) for instantiation processing.

Based on the existing method, the optimal strategy is often found by focusing on reducing the number of newly started exchanger servers; and according to the strategy, deploying and operating a corresponding service function chain in the network system to provide the requested service for the demand party.

However, when the service function chain is deployed and operated according to the policy determined by the method, the technical problems of high energy consumption, high processing cost and incapability of fully and reasonably utilizing available computing resources in the network system often exist.

In view of the above problems, no effective solution has been proposed.

Disclosure of Invention

The specification provides a data processing method, a data processing device and a server, which can make full use of the computing resources of the existing function instance of a target network system by constructing and based on a target weighted energy consumption level auxiliary graph, accurately determine a target deployment strategy with low energy consumption and good effect, and further deploy and operate a target service function chain in the target network system with low processing cost according to the target deployment strategy.

An embodiment of the present specification provides a data processing method, including:

acquiring parameter data of a target service function chain, and network topology data and operation parameters of a target network system;

according to a preset construction rule, constructing a target weighted energy consumption level auxiliary graph aiming at the target service function chain by utilizing the parameter data of the target service function chain, and the network topology data and the operation parameters of a target network system;

and determining a target deployment strategy aiming at the target service function chain according to the target weighted energy consumption level auxiliary graph.

In one embodiment, the operating parameters include: server operating parameters and switch operating parameters; wherein the server operating parameters include at least: the current on-off state of the server and the current existing function instance of the server; the switch operating parameters include at least: the current switch state of the switch.

In one embodiment, constructing a target weighted energy consumption level auxiliary graph for a target service function chain according to a preset construction rule by using the parameter data of the target service function chain and the network topology data and the operation parameters of a target network system, includes:

determining a plurality of target service functions and a connection relation between the target service functions according to the parameter data of the target service function chain;

constructing an initial energy consumption level auxiliary graph according to the plurality of target service functions, the connection relation among the target service functions, and the network topology data and the operation parameters of the target network system; wherein the initial energy consumption level auxiliary graph comprises a plurality of structural layers; the structural layer corresponds to a target service function; the structural layer comprises server nodes supporting the corresponding target service function;

determining energy consumption weight parameters of server nodes and energy consumption weight parameters of connecting edges between the server nodes in adjacent structural layers according to network topology data and operation parameters of a target network system;

and processing the initial energy consumption hierarchy auxiliary graph according to the energy consumption weight parameters of the server nodes and the energy consumption weight parameters of the connecting edges to obtain a corresponding target weighted energy consumption hierarchy auxiliary graph.

In one embodiment, determining the energy consumption weighting parameter of the server node according to the network topology data and the operation parameter of the target network system comprises:

determining an energy consumption weight parameter of the server node according to the following formula:

wherein ,GH_sRepresents an energy consumption weight parameter for a server node corresponding to server s, represents the on-off state of server s,

representing the energy consumption when the server s is switched on,

indicating the currently used computing resources of the server s, theta indicating whether the server s currently has a function instance of the target service function SF,

representing the computational resources used when the function instance of the target service function SF is first created in case the server s does not currently have a function instance of the target service function SF,

representing the computational resources used when extending the function instance of the target service function SF in case the server s currently has a function instance of the target service function SF, P_s ^peakRepresenting the energy consumption of the server s at full load, C_sRepresenting the total amount of computing resources of the server s.

In one embodiment, determining an energy consumption weight parameter of a transit path between server nodes in adjacent structural layers according to network topology data and operation parameters of a target network system includes:

determining energy consumption weight parameters of connecting edges between server nodes in adjacent structural layers according to the following formula:

wherein ,GH_m,nEnergy consumption weight parameters of connecting edges between the server nodes m and the server nodes n, wherein the server nodes m are positioned in the previous structural layer of the adjacent structural layers, the server nodes n are positioned in the next structural layer of the adjacent structural layers, r represents a transfer path between the servers corresponding to the server nodes m and the servers corresponding to the server nodes n

Exchange in (1) | gamma_mL represents the number of switches in the transit path,

indicating the switch state of the switch r,

representing the energy consumption when the switch r is turned on,

representing the energy consumption of the ports of the switch r in transferring data.

In one embodiment, the method further comprises:

and searching the lowest energy consumption path between the server nodes in the adjacent structural layers through a Dijkstra algorithm according to the initial energy consumption level auxiliary graph, the network topology data and the operation parameters of the target network system, and taking the lowest energy consumption path as the transfer path.

In one embodiment, processing the initial energy consumption hierarchy auxiliary graph according to the energy consumption weight parameter of the server node and the energy consumption weight parameter of the connecting edge to obtain a corresponding target weighted energy consumption hierarchy auxiliary graph includes:

marking energy consumption weight parameters of corresponding server nodes at the server nodes in the initial energy consumption level auxiliary graph; and marking energy consumption weight parameters of corresponding connecting edges between server nodes in adjacent structural layers in the initial energy consumption level auxiliary graph to obtain the target weighted energy consumption level auxiliary graph.

In one embodiment, determining a target deployment strategy for a target service function chain according to the target weighted energy consumption level auxiliary graph includes:

searching a minimum weight path between a starting point and an end point according to the target weighted energy consumption level auxiliary graph to serve as a target link path;

and determining a target deployment strategy aiming at the target service function chain according to the target link path.

In one embodiment, searching for a minimum weight path between a start point and an end point as a target link path according to a target weighted energy consumption level auxiliary graph comprises:

superposing energy consumption weight parameters of connecting edges marked by the connecting edges between the server nodes in each adjacent structural layer in the target weighted energy consumption level auxiliary graph on the energy consumption weight parameters of the initial server nodes of the connecting edges to obtain a target weighted energy consumption level auxiliary graph after superposition operation;

and searching a minimum weight path between a starting point and an end point through a Dijkstra algorithm according to the target weighted energy consumption level auxiliary graph after the superposition operation, and taking the minimum weight path as a target link path.

In one embodiment, after determining a target deployment policy for a target service function chain according to the target weighted energy consumption level assistance map, the method further comprises:

and deploying a target service function chain in the target network system according to the target deployment strategy.

In one embodiment, the target network system comprises at least one of: a network system of a micro data center, a network system of edge computing and a network system of the Internet of things.

In one embodiment, the network topology data and operational parameters of the target network system are determined from a weighted energy consumption level assistance map of existing functional service chains previously deployed to the target network system.

An embodiment of the present specification further provides a data processing apparatus, including:

the acquisition module is used for acquiring parameter data of the target service function chain, and network topology data and operation parameters of the target network system;

the construction module is used for constructing a target weighted energy consumption level auxiliary graph aiming at the target service function chain by utilizing the parameter data of the target service function chain, the network topology data and the operation parameters of the target network system according to a preset construction rule;

and the determining module is used for determining a target deployment strategy aiming at the target service function chain according to the target weighted energy consumption level auxiliary graph.

Embodiments of the present specification also provide a server, which includes a processor and a memory for storing processor-executable instructions, where the processor executes the instructions to implement the relevant steps of the data processing method.

Embodiments of the present specification also provide a computer readable storage medium, on which computer instructions are stored, and when executed by a processor, the instructions implement the relevant steps of the data processing method.

Based on the data processing method, the data processing device and the server provided by the specification, when a target service function chain needs to be deployed in a target network system, parameter data of the target service function chain, network topology data and operation parameters of the target network system can be obtained firstly; according to a preset construction rule, constructing and obtaining a target weighted energy consumption level auxiliary graph for the target service function chain by using the parameter data of the target service function chain, the network topology data and the operation parameters of the target network system; and determining a target deployment strategy aiming at the target service function chain according to the target weighted energy consumption level auxiliary graph. Therefore, a target weighted energy consumption level auxiliary graph of the existing service function chain which is previously deployed is constructed and considered, computing resources of the existing function instance of the target network system are fully utilized, a target deployment strategy with low energy consumption and good effect is accurately determined, and then the target service function chain can be deployed and operated in the target network system with low processing cost according to the target deployment strategy, so that corresponding service is provided for a demand party.

Drawings

In order to more clearly illustrate the embodiments of the present specification, the drawings needed to be used in the embodiments will be briefly described below, and the drawings in the following description are only some of the embodiments described in the specification, and it is obvious to those skilled in the art that other drawings can be obtained based on the drawings without any inventive work.

FIG. 1 is a flow diagram of a data processing method provided by one embodiment of the present description;

FIG. 2 is a diagram illustrating an embodiment of a data processing method according to an embodiment of the present disclosure;

FIG. 3 is a diagram illustrating an embodiment of a data processing method according to an embodiment of the present disclosure;

FIG. 4 is a diagram illustrating an embodiment of a data processing method according to an embodiment of the present disclosure;

FIG. 5 is a schematic diagram of a server according to an embodiment of the present disclosure;

fig. 6 is a schematic structural component diagram of a data processing apparatus according to an embodiment of the present specification.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step should fall within the scope of protection of the present specification.

Referring to fig. 1, an embodiment of the present disclosure provides a data processing method. When the method is implemented, the following contents may be included.

S101: and acquiring parameter data of the target service function chain, and network topology data and operation parameters of the target network system.

S102: and according to a preset construction rule, constructing a target weighted energy consumption level auxiliary graph aiming at the target service function chain by utilizing the parameter data of the target service function chain, and the network topology data and the operation parameters of the target network system.

S103: and determining a target deployment strategy aiming at the target service function chain according to the target weighted energy consumption level auxiliary graph.

In an embodiment, the Service Function Chain (SFC) may be specifically understood as a Chain structure obtained by connecting a plurality of related Service functions (Service functions) in a Chain manner according to a certain sequence in order to implement a corresponding Service. In specific implementation, a plurality of connected Service Function Instances (SFI) can be obtained by deploying the Service Function chain in a network system for instantiation; and then, the corresponding business service is realized by operating the plurality of connected service function instances.

The target service function chain may be specifically understood as a service function chain to be deployed and run. Specifically, the target service function chain may be specifically understood as a service function chain corresponding to a target business service requested by a demand side.

The target service may include business services with different contents according to different application scenarios and processing requirements. Specifically, for example, the target service may include: a federated computing service, an information query service, or a settlement service, among others.

Further, the target service may be split into a plurality of target service functions. The target service function may also include a plurality of service functions with different contents. For example, the target service function may specifically include: firewall functions, big data processing functions, or authentication functions, etc.

Of course, it should be noted that the target service and the target service function listed above are only schematic illustrations. The specific content of the target service and the target service function is not limited in the present specification.

In one embodiment, in implementation, the server may receive a business service request from a demander; determining a target business service required by a demand party according to the business service request; determining a plurality of related target service functions according to the target service; and connecting a plurality of target service functions in a chain manner to obtain the target service function chain.

The requesting party may specifically initiate the service request to the server through the user terminal. After receiving and responding to the service request and determining the target service function chain, the server may deploy and operate the target service function chain in a target network system (e.g., a network system of a micro data center) to provide a corresponding target service for a demanding party.

In this embodiment, the server may specifically include a background server disposed at a side of the micro data center and capable of implementing functions such as data transmission and data processing. Specifically, the server may be, for example, an electronic device having data operation, storage function and network interaction function. Alternatively, the server may be a software program running in the electronic device and providing support for data processing, storage and network interaction. In the present embodiment, the number of servers is not particularly limited. The server may specifically be one server, or may also be several servers, or a server cluster formed by several servers.

In this embodiment, the user terminal may specifically include a front end disposed on one side of the demand side, and capable of implementing functions such as data acquisition and data transmission. Specifically, the user terminal may be, for example, an electronic device such as a desktop computer, a tablet computer, and a notebook computer. Alternatively, the user terminal may be a software application capable of running in the electronic device. For example, it may be some APP running on a smartphone, etc.

In one embodiment, the parameter data of the target service function chain at least includes a data set containing identification information of a plurality of ordered target service functions.

Specifically, for example, the parameter data of the target service function chain can be represented as the following data group: SFC ═ (a, SF1, SF2, … …, SFi, … … SFn, b). Wherein, a is the starting point of the target service function chain, b is the ending point of the target service function chain, SFi is the identification information of the target service function with number i, and n is the total number of the target service functions contained in the target service function chain. The start point may specifically represent an input, and the end point may specifically represent an output.

In an embodiment, the parameter data of the target service function chain may further include associated data of the target service function, for example, a computing resource required for implementing the target service function, a type of a service supporting the target service function, and the like.

In one embodiment, the network topology data of the target network system at least includes a server data set, a switch (or route) data set, a link data set, and the like of the target network system.

In an embodiment, the network topology data of the target network system may be specifically expressed in the following form: g ═ S, R, L. Wherein, G represents a network topology data set of the target network system, S represents a server data set in the target network system, R represents a switch data set in the target network system, and L represents a link data set in the target network system. Any server S in the target network system belongs to S, any switch R in the target network system belongs to R, and any link L in the target network system belongs to L.

Further, the network topology data of the target network system may further include basic characteristic parameters of a server and a switch (or a route) in the target network system.

Specifically, the basic characteristic parameters of the server may specifically include: the energy consumption of the server when the server is started, the energy consumption and the required computing resources of the function instance of a certain service function which is created by the server for the first time, the energy consumption and the required computing resources of the server when the function instance of the certain service function is expanded based on the existing function instance of the certain service function, the energy consumption of the server when the server is fully loaded, the total amount of the computing resources of the server, the model of the server and the like.

The basic characteristic parameters of the switch may specifically include: the energy consumption of the switch when the switch is started, the port number of the switch, the energy consumption of the port of the switch when the switch transmits data, the model of the switch and the like.

It should be noted that, based on the existing method, when a new service function chain is deployed and operated, other existing service function chains that have been deployed before are often not considered, existing service function instances of a server are not effectively utilized, and most of the existing service function instances are created again on the server. And creating a new service function instance on a server usually consumes more computing resources and consumes more energy, which inevitably increases the total energy consumption when deploying and running a new service function chain.

In practice, many different service function chains often include one or more of the same service functions (e.g., firewall functions, big data processing functions, etc.). Thus, it is contemplated that service functions in other service function chains may be multiplexed by performing extensions of function instances on the basis of existing service function instances, utilizing existing other service function chains that have been previously deployed. The energy consumption required for expanding the function instance of the existing service function instance is often less than that required for recreating a new service function instance.

Therefore, in the data processing method provided by the embodiment of the present disclosure, not only the creation of a new service function instance on the server is considered, but also the existing other service function chains that have been deployed before are considered, and the existing service function instance of the server is utilized by performing the extension of the existing function instance, so that the total energy consumption for deploying and running the new service function chain can be effectively reduced.

In one embodiment, the target network system may specifically include at least one of: network systems of micro data centers, network systems of edge computing, network systems of internet of things, and the like. Of course, it should be noted that the above listed target network systems are only schematic illustrations. In specific implementation, according to specific situations and processing requirements, the data processing method provided in the embodiments of the present disclosure may also be applied to other suitable network systems. The present specification is not limited to these.

In one embodiment, the target network system may specifically also be a network system that has been deployed and running other service function chains. In particular, for example, some service function instances already exist for some servers in the target network system before the target service function chain is deployed.

In an embodiment, the operation parameter may specifically include parameter data capable of characterizing a current latest operation state of a server, a switch, and other devices in the target network system.

In one embodiment, the operating parameters may specifically include: server operating parameters and switch operating parameters, etc. Wherein the server operation parameters at least comprise: the current on-off state of the server, the current function instance of the server and the like; the switch operating parameters may include at least: the current switch state of the switch, etc.

Further, the server operation parameters may specifically include: computing resources currently used by the server, service function instances currently existing by the server, etc. The switch operating parameters may specifically include: ports currently used by the switch, etc.

In an embodiment, the network topology data and the operation parameters of the target network system may be specifically determined according to a weighted energy consumption level auxiliary graph of an existing functional service chain previously deployed in the target network system.

In this embodiment, each time a new service function chain is deployed and run in the target network system, a corresponding weighted energy consumption level auxiliary graph is constructed. When a new weighted energy consumption level auxiliary graph is constructed each time, the latest network topology data and operation parameters of the target network system can be determined according to the weighted energy consumption level auxiliary graph of the current existing functional service chain, so that the existing service function chain which is previously deployed in the target network system can be fully and effectively utilized.

In one embodiment, the target weighted energy consumption level assistance map may be specifically understood as a weighted energy consumption level assistance map corresponding to a target service function chain.

Specifically, as shown in FIG. 2, the target weighted energy consumption level auxiliary graph (which may be denoted as E-SLG) may specifically include a plurality of structural layers. Each structural layer corresponds to a target service function in the chain of target service functions. Each structural layer may specifically include one or more server nodes, where each server node corresponds to a server that supports a target service function corresponding to the structural layer in which it is located. Connecting edges are further arranged between different server nodes in two adjacent structural layers and used for representing transfer paths (or transfer link links and the like) between two services corresponding to the two server nodes. Further, the server nodes and the connecting edges in the target weighted energy consumption level auxiliary graph are respectively marked with corresponding energy consumption weighting parameters, for example, the energy consumption weighting parameters (or called server energy consumption) of the server nodes and the energy consumption weighting parameters (or called routing energy consumption) of the connecting edges.

The energy consumption weight parameter of the server node can accurately represent the energy consumption of the server when the corresponding target service function in the target service function chain is realized under the condition that the current starting state and the current existing service function instance of the server corresponding to the server node are considered. The specific energy consumption weight parameter of the continuous edge can accurately represent the energy consumption of the transfer path corresponding to the continuous edge when the target service function chain is deployed and operated under the condition that the existing function service chain which is deployed in the target network system in advance is considered.

The target weighted energy consumption level auxiliary graph considers the service function chain deployed in advance, and the specific use and energy consumption conditions of the current server and the transit path. Therefore, based on the target weighted energy consumption level auxiliary graph, a deployment strategy (or mapping strategy) with relatively low (or minimum) energy consumption when the target service function chain is deployed and operated can be determined more accurately and efficiently.

In an embodiment, the above-mentioned constructing a target weighted energy consumption level auxiliary graph for a target service function chain according to a preset construction rule and by using the parameter data of the target service function chain, the network topology data and the operation parameter of the target network system may include the following contents in a specific implementation:

s1: determining a plurality of target service functions and a connection relation between the target service functions according to the parameter data of the target service function chain;

s2: constructing an initial energy consumption level auxiliary graph according to the plurality of target service functions, the connection relation among the target service functions, and the network topology data and the operation parameters of the target network system; wherein the initial energy consumption level auxiliary graph comprises a plurality of structural layers; the structural layer corresponds to a target service function; the structural layer comprises server nodes supporting the corresponding target service function;

s3: determining an energy consumption weight parameter of a server node and an energy consumption weight parameter of a connecting edge between server nodes in adjacent structural layers according to network topology data and operation parameters of a target network system;

s4: and processing the initial energy consumption hierarchy auxiliary graph according to the energy consumption weight parameters of the server nodes and the energy consumption weight parameters of the connecting edges to obtain a corresponding target weighted energy consumption hierarchy auxiliary graph.

In an embodiment, the processing the initial energy consumption hierarchy auxiliary graph according to the energy consumption weight parameter of the server node and the energy consumption weight parameter of the connecting edge to obtain a corresponding target weighted energy consumption hierarchy auxiliary graph may include the following contents in a specific implementation: marking energy consumption weight parameters of corresponding server nodes at the server nodes in the initial energy consumption level auxiliary graph as the weights of the server nodes; and marking energy consumption weight parameters of corresponding connecting edges between server nodes in adjacent structural layers in the initial energy consumption level auxiliary graph as the weights of the connecting edges to obtain the target weighted energy consumption level auxiliary graph.

In one embodiment, referring to fig. 3, an example of constructing a target weighted energy consumption level assistance graph for a target service function chain including two target service functions (e.g., SF1, SF2) is provided. The parameter data of the target service function chain can be expressed as: (a, SF1, SF2, b).

According to the parameter data of the target service function chain, two target service functions can be split and respectively recorded as: SF1 and SF 2. The connection relationship between the target service functions may be: SF1 is connected with the starting point a; SF2 is connected to SF1 first and then to terminal b.

Referring to fig. 4, first, according to the connection relationship between the target service functions and the plurality of target service functions, the basic framework of the initial energy consumption level auxiliary graph is determined to include two structural layers in the vertical direction: a first structural layer and a second structural layer. The first structure layer positioned above is connected with the starting point a and corresponds to a target service function SF 1; the second, lower layer is connected to the destination b, corresponding to the target service function SF 2.

Then, according to the parameter data of the target service function chain, and the network topology data and the operation parameters of the target network systemFinding and screening out SF1 server capable of supporting the function of receiving target service currently from the target network system, and making the server node corresponding to the server:

filling the first structural layer. Wherein, | SF1| represents the total number of SF1 servers in the target network system that are currently capable of supporting the hosted target service function. Similarly, an SF2 server capable of currently supporting the target service function can be selected from the target network system, and the server node corresponding to the server:

filling the first structural layer. Wherein, | SF2| represents the total number of SF2 servers in the target network system that are currently capable of supporting the hosted target service function. Thus, an initial energy consumption level auxiliary graph for the target service function chain can be constructed.

Then, according to the corresponding calculation rule, the energy consumption weight parameters of each server node and the energy consumption weight parameters of each connecting edge between the server nodes in the adjacent structural layers can be calculated respectively according to the network topology data and the operation parameters of the target network system. And respectively marking the energy consumption weight parameters of the server nodes and the energy consumption weight parameters of the connecting edges to the corresponding server nodes or connecting edges in the initial energy consumption hierarchy auxiliary graph, so as to obtain a corresponding target weighted energy consumption hierarchy auxiliary graph.

In an embodiment, the determining the energy consumption weight parameter of the server node according to the network topology data and the operation parameter of the target network system includes, in specific implementation:

the energy consumption weight parameter of the server node may be determined according to the following equation:

wherein ,GH_sRepresenting the capacity of a server node corresponding to a server sA weight loss parameter, phi, represents the on-off state of the server s,

representing the energy consumption when the server s is switched on,

In one embodiment, the above ρ_sSpecifically, the ratio parameter may be a ratio parameter between energy consumption when the function instance of the target service function SF is expanded and energy consumption when the function instance of the target service function SF is created for the first time. The specific numerical value of the proportional parameter can be obtained by creating a function instance and performing a function instance extension test on a large number of service functions in advance.

In one embodiment, when the energy consumption weight parameter of the server node is specifically determined, according to the operation parameter of the target network system, when the current switching state of the server is determined to be the on state, phi in the equation can be set to 1; conversely, when it is determined that the current switch state of the server is the off state, φ in the equation may be set to 0. According to the operation parameters of the target network system, in the case that the server is determined to have a function instance (service function instance) of the target service function SF at present, θ in the equation can be set to 1; in contrast, in the case where it is determined that the server does not currently have a function instance (service function instance) of the target service function SF, θ in the equation may be set to 0.

In an embodiment, the determining, according to the network topology data and the operation parameter of the target network system, an energy consumption weight parameter of a transit path between server nodes in adjacent structural layers includes, in specific implementation:

the energy consumption weight parameter of the connecting edge between the server nodes in the adjacent structural layers can be determined according to the following formula:

Exchange of (1) (. gamma.)_mIndicating the number of switches in the transit path,

indicating the switch state of the switch r,

representing the energy consumption when the switch r is turned on,

In an embodiment, when the method is implemented, the following may be further included: and searching the lowest energy consumption path between the server nodes in the adjacent structural layers through a Dijkstra algorithm according to the initial energy consumption level auxiliary graph, the network topology data and the operation parameters of the target network system, and taking the lowest energy consumption path as the transfer path.

In one embodiment, when the energy consumption weight parameter of the connection edge is specifically determined, according to the operation parameter of the target network system, when the current switch state of the switch is determined to be the on state, the energy consumption weight parameter in the equation may be used as the energy consumption weight parameter of the connection edge

Is set to 1; conversely, when the current switch state of the switch is determined to be the off state, the current switch state in the formula can be determined

Is set to 0.

In this embodiment, in specific implementation, based on the initial energy consumption level auxiliary graph, a Dijkstra algorithm is invoked to search out a lowest energy consumption path between two server nodes m and a server node n in an adjacent structure, which is used as a transfer path between servers corresponding to the two server nodes

The Dijkstra algorithm (or Dijkstra algorithm) may be an algorithm that solves the single-source shortest path problem of the weighted graph by using a similar breadth-first search method.

Through the embodiment, based on the initial energy consumption hierarchy auxiliary graph, the Dijkstra algorithm is called to quickly find the optimal transit path for determining the weight of the connecting edge between two server nodes in the adjacent structure in the initial energy consumption hierarchy auxiliary graph.

In an embodiment, the determining a target deployment policy for a target service function chain according to the target weighted energy consumption level auxiliary graph may include the following steps:

s1: searching a minimum weight path between a starting point and an end point according to the target weighted energy consumption level auxiliary graph to serve as a target link path;

s2: and determining a target deployment strategy aiming at the target service function chain according to the target link path.

The target link path may be specifically understood as a weighted minimum path that is found based on the target weighted energy consumption level auxiliary graph and ends from the starting point to the ending point. The target deployment policy may also be referred to as a lowest power consumption service function chain mapping policy, and may be specifically understood as a deployment policy that can implement a target business service and has the lowest energy consumption.

In an embodiment, the searching for the minimum weight path between the starting point and the ending point according to the target weighted energy consumption level auxiliary graph as the target link path may include the following steps: superposing energy consumption weight parameters of connecting edges marked by the connecting edges between the server nodes in each adjacent structural layer in the target weighted energy consumption level auxiliary graph on the energy consumption weight parameters of the initial server nodes of the connecting edges to obtain a target weighted energy consumption level auxiliary graph after superposition operation; and searching a minimum weight path between a starting point and an end point through a Dijkstra algorithm according to the target weighted energy consumption level auxiliary graph after the superposition operation, and taking the minimum weight path as a target link path.

Through the embodiment, the Dijkstra algorithm can be called to quickly find the optimal target link path based on the target weighted energy consumption level auxiliary graph after the superposition operation.

In an embodiment, after determining a target deployment policy for a target service function chain according to the target weighted energy consumption level auxiliary graph, when the method is implemented, the following may be further included: and deploying a target service function chain in the target network system according to the target deployment strategy.

In an embodiment, the deploying a target service function chain in the target network system according to the target deployment policy may include, when implemented: according to the target deployment strategy, a server corresponding to a server node passed by the target link path carries out one of the following operations: starting a server and creating a function instance of a corresponding target service function on the server; creating a function instance of a corresponding target service function on the opened server; and performing function instance expansion of the target service function on the server with the function instance of the corresponding target service function.

Therefore, according to the target deployment strategy, the calculation resources of the function instances existing in the server in the target network system are fully utilized to expand the function instances, and the deployment and the operation of the target service function chain are completed with relatively low energy consumption and low data processing cost.

In one embodiment, after deploying a target service function chain in the target network system according to the target deployment policy, the method further comprises: receiving and responding to an operation request aiming at a target service function chain; and operating the target service function chain to provide corresponding target business service for the demand party.

As can be seen from the above, based on the data processing method provided in the embodiment of the present specification, when a target service function chain needs to be deployed in a target network system, parameter data of the target service function chain, and network topology data and operation parameters of the target network system may be obtained first; according to a preset construction rule, constructing and obtaining a target weighted energy consumption level auxiliary graph aiming at the target service function chain by using the parameter data of the target service function chain, the network topology data and the operation parameters of the target network system; and determining a target deployment strategy aiming at the target service function chain according to the target weighted energy consumption level auxiliary graph. Therefore, the calculation resources of the function instance existing in the target network system at present can be fully utilized through constructing and based on the target weighted energy consumption level auxiliary graph, the target deployment strategy with low energy consumption is accurately determined, and further, the target service function chain can be deployed and operated in the target network system with low processing cost according to the target deployment strategy, so that corresponding service is provided for the demand party.

Embodiments of the present specification further provide a server, including a processor and a memory for storing processor-executable instructions, where the processor, when implemented, may perform the following steps according to the instructions: acquiring parameter data of a target service function chain, and network topology data and operation parameters of a target network system; according to a preset construction rule, constructing a target weighted energy consumption level auxiliary graph aiming at the target service function chain by utilizing the parameter data of the target service function chain, and the network topology data and the operation parameters of a target network system; and determining a target deployment strategy aiming at the target service function chain according to the target weighted energy consumption level auxiliary graph.

In order to more accurately complete the above instructions, referring to fig. 5, another specific server is provided in the embodiments of the present specification, wherein the server includes a network communication port 501, a processor 502 and a memory 503, and the above structures are connected by an internal cable, so that the structures can perform specific data interaction.

The network communication port 501 may be specifically configured to acquire parameter data of a target service function chain, and network topology data and operation parameters of a target network system.

The processor 502 may be specifically configured to construct a target weighted energy consumption level auxiliary graph for a target service function chain according to a preset construction rule, by using the parameter data of the target service function chain, and the network topology data and the operation parameter of the target network system; and determining a target deployment strategy aiming at the target service function chain according to the target weighted energy consumption level auxiliary graph.

The memory 503 may be specifically configured to store a corresponding instruction program.

In this embodiment, the network communication port 501 may be a virtual port that is bound to different communication protocols, so that different data can be sent or received. For example, the network communication port may be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for mail data communication. In addition, the network communication port can also be a communication interface or a communication chip of an entity. For example, it may be a wireless mobile network communication chip, such as GSM, CDMA, etc.; it can also be a Wifi chip; it may also be a bluetooth chip.

In this embodiment, the processor 502 may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth. The description is not intended to be limiting.

In this embodiment, the memory 503 may include multiple layers, and in a digital system, the memory may be any memory as long as binary data can be stored; in an integrated circuit, a circuit without a physical form and with a storage function is also called a memory, such as a RAM, a FIFO and the like; in the system, the storage device in physical form is also called a memory, such as a memory bank, a TF card and the like.

The present specification further provides a computer storage medium based on the above data processing method, where the computer storage medium stores computer program instructions, and when the computer program instructions are executed, the computer storage medium implements: acquiring parameter data of a target service function chain, and network topology data and operation parameters of a target network system; according to a preset construction rule, constructing a target weighted energy consumption level auxiliary graph aiming at the target service function chain by utilizing the parameter data of the target service function chain, and the network topology data and the operation parameters of a target network system; and determining a target deployment strategy aiming at the target service function chain according to the target weighted energy consumption level auxiliary graph.

In this embodiment, the storage medium includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk Drive (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.

In this embodiment, the functions and effects specifically realized by the program instructions stored in the computer storage medium can be explained by comparing with other embodiments, and are not described herein again.

Referring to fig. 6, in a software level, an embodiment of the present specification further provides a data processing apparatus, which may specifically include the following structural modules:

the obtaining module 601 may be specifically configured to obtain parameter data of a target service function chain, and network topology data and operation parameters of a target network system;

the constructing module 602 may be specifically configured to construct a target weighted energy consumption level auxiliary graph for a target service function chain according to a preset constructing rule, by using the parameter data of the target service function chain, and the network topology data and the operation parameters of the target network system;

the determining module 603 may be specifically configured to determine a target deployment policy for the target service function chain according to the target weighted energy consumption level auxiliary graph.

In one embodiment, the operating parameters may specifically include: server operating parameters and switch operating parameters; wherein the server operating parameters include at least: the current on-off state of the server and the current function instance of the server; the switch operating parameters include at least: the current switch state of the switch.

In an embodiment, when the building module 602 is implemented specifically, the building of the target weighted energy consumption level auxiliary graph for the target service function chain according to the preset building rule, by using the parameter data of the target service function chain, and the network topology data and the operation parameter of the target network system, may be implemented as follows: determining a plurality of target service functions and a connection relation between the target service functions according to the parameter data of the target service function chain; constructing an initial energy consumption level auxiliary graph according to the plurality of target service functions, the connection relation among the target service functions, and the network topology data and the operation parameters of the target network system; wherein the initial energy consumption level auxiliary graph comprises a plurality of structural layers; the structural layer corresponds to a target service function; the structural layer comprises server nodes supporting the corresponding target service function; determining an energy consumption weight parameter of a server node and an energy consumption weight parameter of a connecting edge between server nodes in adjacent structural layers according to network topology data and operation parameters of a target network system; and processing the initial energy consumption hierarchy auxiliary graph according to the energy consumption weight parameters of the server nodes and the energy consumption weight parameters of the connecting edges to obtain a corresponding target weighted energy consumption hierarchy auxiliary graph.

In an embodiment, when the building module 602 is implemented, the energy consumption weight parameter of the server node may be determined according to the following equation:

wherein ,GH_sRepresents an energy consumption weight parameter for a server node corresponding to server s, represents a switch state of server s,

representing the energy consumption when the server s is switched on,

representing the energy consumption when the function instance of the target service function SF is first created in case the server s does not currently have a function instance of the target service function SF,

represents the energy consumption, P, when extending the function instance of the target service function SF in case the server s currently has a function instance of the target service function SF_s ^peakPresentation serviceEnergy consumption at full load of the device s, C_sRepresenting the total amount of computing resources of the server s.

In an embodiment, when the building module 602 is implemented specifically, the energy consumption weight parameter of a connecting edge between server nodes in adjacent structural layers may be determined according to the following equation:

indicating the switch state of the switch r,

representing the energy consumption when the switch r is turned on,

In an embodiment, when the building module 602 is implemented specifically, the lowest energy consumption path between server nodes in adjacent structural layers may be searched through a Dijkstra algorithm according to an initial energy consumption hierarchy auxiliary graph, and network topology data and operation parameters of a target network system, so as to serve as the transit path.

In an embodiment, when the building module 602 is implemented specifically, the initial energy consumption hierarchy auxiliary graph may be processed in the following manner to obtain a corresponding target weighted energy consumption hierarchy auxiliary graph: marking energy consumption weight parameters of corresponding server nodes at the server nodes in the initial energy consumption level auxiliary graph; and marking energy consumption weight parameters of corresponding connecting edges between server nodes in adjacent structural layers in the initial energy consumption level auxiliary graph to obtain the target weighted energy consumption level auxiliary graph.

In an embodiment, when the determining module 603 is implemented specifically, a target deployment policy for a target service function chain may be determined according to the target weighted energy consumption level auxiliary graph in the following manner: searching a minimum weight path between a starting point and an end point according to the target weighted energy consumption level auxiliary graph to serve as a target link path; and determining a target deployment strategy aiming at the target service function chain according to the target link path.

In an embodiment, when the determining module 603 is implemented specifically, the energy consumption weight parameters of the connecting edges labeled by the connecting edges between the server nodes in each adjacent structural layer in the target weighted energy consumption hierarchy auxiliary graph may be superimposed on the energy consumption weight parameters of the initial server node of the connecting edges, so as to obtain the target weighted energy consumption hierarchy auxiliary graph after the superimposing operation; and searching a minimum weight path between a starting point and an end point through a Dijkstra algorithm according to the target weighted energy consumption level auxiliary graph after the superposition operation, and taking the minimum weight path as a target link path.

In an embodiment, the apparatus may further include a deployment module, which may be configured to deploy a target service function chain in the target network system according to the target deployment policy.

In one embodiment, the target network system may specifically include at least one of: a network system of a micro data center, a network system of edge computing, a network system of the internet of things, and the like.

It should be noted that, the units, devices, modules, etc. illustrated in the above embodiments may be implemented by a computer chip or an entity, or implemented by a product with certain functions. For convenience of description, the above devices are described as being divided into various modules by functions, which are described separately. It is to be understood that, in implementing the present specification, functions of each module may be implemented in one or more pieces of software and/or hardware, or a module that implements the same function may be implemented by a combination of a plurality of sub-modules or sub-units, or the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

As can be seen from the above, the data processing apparatus provided in the embodiments of the present specification makes full use of the computing resources of the function instance existing in the target network system by constructing and based on the target weighted energy consumption level auxiliary graph, and accurately determines the target deployment policy with less energy consumption, so that the target service function chain can be deployed and operated in the target network system at a lower processing cost according to the target deployment policy, thereby providing corresponding service to the demander.

In a specific scenario example, the data processing method provided by the embodiments of the present specification may be utilized to implement determination of a minimum power consumption service function chain mapping policy (e.g., a target deployment policy) for a micro data center (e.g., a target network system) based on deployed function instances.

In the present scenario example, the following parameters are specifically referred to as follows.

(1) G ═ S, R, L) (e.g., network topology data for the target network system). G represents a server network topology of the miniature data center; wherein S represents a set of server nodes (e.g., a data set of servers); r represents a set of routing nodes (e.g., a data set of switches); l represents a set of links (e.g., a data set of links).

(2) For each server S ∈ S, P_s ^peakEnergy consumption representative of its full load condition;

representing the energy consumption required for its activation; c_sRepresenting its total computational resource size;

representing the computing resources of server s that are in use;

representing the kind of service function s can carry.

(3) For each routing node (or switch node) R e R,

representing the energy consumption required to turn on this routing node;

represents the energy consumption generated by each port when transmitting data; delta_rRepresenting the total port number of this routing node.

(3) For a service function chain service, SFC ═ (a, SF1, SF2, … …, SFi, … … SFf, b) where a, b represent the start and end points, respectively; SFi represents the ith required service function; f is a positive integer representing the total number of service functions required.

(4) For each of the service functions SFi,

representing the computational resources required to instantiate the first SFi on server s;

(i.e.,. rho.)_s) Representing the computational resources required to expand an instance of an SFi to carry a new SFi if that instance already exists on a server s

Computing resources required to recreate the instance from the original

The ratio of (a) to (b).

In order to implement the low-power-consumption service function chain mapping taking the established instance and the shared new instance into consideration, a power consumption level auxiliary graph (e.g., a target weighted power consumption level auxiliary graph) of the service function chain mapping may be first constructed, as shown in fig. 2.

In this scenario example, the service function chain is SFC ═ (a, SF1, SF2, b). In the figure, each ellipse represents a server (server node) in the mini data center. Wherein the content of the first and second substances,

representing the xth server capable of carrying SF 1. Since the required service function chain requests two service functions in total, the service function chain maps the power consumption level auxiliary graph to only two layers. Wherein each layer comprises all servers capable of carrying SFi. For example, the first tier includes all servers capable of carrying SF1, where N_SFiIs a collection of server nodes that includes all SFi-capable servers.

In the figure, each solid line represents the lowest power consumption overhead path (e.g., a continuous edge) from one point to another. Specifically, GH may be used_m,nTo represent the power consumption from node m to node n. The power consumption is calculated as follows:

wherein ,

equal to 1 or 0, indicating whether a new router or switch needs to be started, | γ_mAnd | represents the number of routing nodes traversed in the path from m to n. I.e. GH_m,nThe energy consumption overhead of newly opening the routing nodes and the routing node ports from one point to another point is calculated.

For each server's power consumption, the GH can be used_sThe specific calculation method is as follows:

wherein, phi is equal to 1 or 0, which indicates whether the server s needs to be newly started, if so, 1, otherwise, 0; θ is equal to 1 or 0, indicating whether an instance of the demanded service function SF already exists on the server s, and is 1 if not, and is 0 otherwise. GH_sIt is calculated that the amount of energy consumption required by the server s in order to fulfill the required service function SF by instantiating (or expanding existing instances) can be noted by means of an oval-shaped annotation box.

Through the two energy consumption calculation formulas, the energy consumption weight of each node and edge in the service function chain mapping power consumption level auxiliary graph can be obtained.

In specific implementation, first, the service function chain mapping power consumption level auxiliary map (corresponding to the target weighted energy consumption level auxiliary map) can be obtained through a corresponding algorithm in the following manner.

Specifically, input: service function chain SFC ═ (a, SFF 1., SF_fB), mini-datacenter network topology G ═ S, R, L; and (3) outputting: service function chain mapping power consumption level auxiliary graph (A), (B), (C) and C)

Wherein V ═ { V ═ V₀，V₁，...，V_f，V_f+1})。

The specific implementation steps of the algorithm can comprise the following steps:

s1: for each required SF_iIn other words, a server s is sought that can host it (i.e. it is sought

) And add it to the node set

Performing the following steps;

s2: using xi_iRepresenting the ith element in the SFC, for each xi_iIn the E-SLG

S3: setting an integer variable i to be 0;

s4: for each at

Node m and each in

According to GH_m，nThe calculation method searches the path of the lowest power consumption from m to n through Dijkstra algorithm_m，nIn E-SLG by edge E_m，nConnect m and n with a weight of path_m，nPower consumption (i.e., E ═ E @_m，n)；

S5: for each at

According to GH_mIn the calculation method, the calculation server m bears SF_iThe required power consumption is calculated as the weight of the node;

s6: if i is not greater than f +1, i is i +1, and the process returns to step S4;

s7: and returning to the service function chain mapping power consumption level auxiliary graph (E-SLG) created in the step.

Through the above algorithm, a corresponding service function chain mapping power consumption level auxiliary graph (E-SLG) can be created for a requested service function chain service.

Further, the lowest power consumption service function chain mapping policy (e.g., target deployment policy) may be obtained by a corresponding algorithm in the following manner.

Specifically, input: service function chain mapping power consumption level assistance graph (b)

) (ii) a And (3) outputting: a Service Function Path (SFP) (e.g., a target link path).

s1: setting an integer variable i to 0;

s2: in E-SLG, for each

For node m, add the power consumption weight of m to each edge starting from it;

s3: if i is less than or equal to f +1, i is equal to i +1, and the step 2 is returned;

s4: on E-SLG, using Dijkstra algorithm to find the path of minimum weight from a to b as SFP;

s5: and returning to the SFP.

Through the algorithm, a service function path SFP from the starting point a to the end point b of the micro data center can be obtained, and a corresponding lowest power consumption service function chain mapping strategy can be determined based on the path SFP.

In this scenario example, it is also proved that the policy corresponding to the SFP based on the path is the mapping policy with the minimum power consumption. The following can be referred to for a specific certification process.

Theory: the energy consumption generated by the service function path generated by the minimum power consumption service function chain mapping policy is the minimum energy consumption generated by the service function chain required by the load in the current mini data center.

And (3) proving that: in the service function chain mapping power consumption level auxiliary graph, (1) each layer of nodes comprises all servers which can bear corresponding service functions, (2) the connection between layers comprises the lowest power consumption communication condition from one layer of node servers to the next layer of nodes, and (3) because the edge taking m as the starting point needs to pass through m, the calculation power consumption of the node m is added to the edge taking m as the starting point, so that no extra energy consumption calculation is added. In summary, it can be first demonstrated that the lowest consuming service path from a to b exists in the generated service function chain mapping power consumption level auxiliary graph. We will use a forensic approach, assuming that there is a service function path with lower cost than the lowest cost path in the service function chain mapping power consumption level secondary graph, then (1) it uses connectivity outside the service function chain mapping power consumption level secondary graph, and (2) for a certain pair of nodes, a path with lower cost can be selected. For point (1), the service function chain mapping power consumption level auxiliary graph contains all the lowest-cost connections, and since the service function path is a multi-segment point-to-point connection, if the lowest energy consumption cost is to be used, each segment of the service function path needs to be the lowest energy consumption cost of the current segment of the path, so that point (1) does not hold. For point (2), if a path with lower energy consumption overhead exists, the service function chain maps the establishment of the power consumption level auxiliary graph to generate an error; mapping the power consumption level assistance map based on the service function chain is correctly established, then (2) does not hold. Therefore, the lowest-overhead service function path must exist in the service function chain mapping power consumption level auxiliary graph. Therefore, the mapping policy for the lowest power consumption service function chain may find the path with the lowest cost from a to b by using Dijkstra algorithm, that is, the path with the lowest power consumption cost in all the service function paths. Therefore, the energy consumption required for this path must also be minimal.

Through the above scenario example, it is verified that the data processing method provided in the embodiment of the present specification performs function instance expansion by using a deployed service function instance through constructing and mapping a power consumption level auxiliary graph based on the service function chain, and can determine a service function data chain mapping policy with the lowest power consumption overhead on the premise of not changing a network topology of a micro data center; meanwhile, the expense of computing resources is reduced by enabling a plurality of service function chains to share the function examples, the total amount of the service function chains which can be received is increased, and the overall processing cost is reduced.

Although the present specification provides method steps as described in the examples or flowcharts, additional or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an apparatus or client product in practice executes, it may execute sequentially or in parallel (e.g., in a parallel processor or multithreaded processing environment, or even in a distributed data processing environment) according to the embodiments or methods shown in the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded. The terms first, second, etc. are used to denote names, but not any particular order.

Those skilled in the art will also appreciate that, in addition to implementing the controller in purely computer readable program code means, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

From the above description of the embodiments, it is clear to those skilled in the art that the present specification can be implemented by software plus necessary general hardware platform. With this understanding, the technical solutions in the present specification may be essentially embodied in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments in the present specification.

The embodiments in the present specification are described in a progressive manner, and the same or similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. The description is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

While the specification has been described with examples, those skilled in the art will appreciate that there are numerous variations and permutations of the specification that do not depart from the spirit of the specification, and it is intended that the appended claims include such variations and modifications that do not depart from the spirit of the specification.

Claims

1. A method of data processing, comprising:

2. The method of claim 1, wherein the operating parameters comprise: server operating parameters and switch operating parameters; wherein the server operating parameters include at least: the current on-off state of the server and the current existing function instance of the server; the switch operating parameters include at least: the current switch state of the switch.

3. The method according to claim 2, wherein constructing a target weighted energy consumption level auxiliary graph for the target service function chain according to a preset construction rule by using the parameter data of the target service function chain and the network topology data and the operation parameters of the target network system comprises:

determining a plurality of target service functions and the connection relation among the target service functions according to the parameter data of the target service function chain;

and processing the initial energy consumption level auxiliary graph according to the energy consumption weight parameters of the server nodes and the energy consumption weight parameters of the connecting edges to obtain a corresponding target weighted energy consumption level auxiliary graph.

4. The method of claim 3, wherein determining the energy consumption weight parameter of the server node based on the network topology data and the operational parameter of the target network system comprises:

wherein ,GH_sRepresents an energy consumption weight parameter of a server node corresponding to the server s, phi represents a switch state of the server s, P_s ^actRepresenting the energy consumption when the server s is turned on,

representing the computational resources used when extending the function instance of the target service function SF in case the server s currently has a function instance of the target service function SF, P_s ^peakRepresenting the energy consumption of the server s at full load, C_sShow clothesThe server s calculates the total amount of resources.

5. The method of claim 3, wherein determining the energy consumption weight parameter of the connecting edge between the server nodes in the adjacent structural layers according to the network topology data and the operation parameter of the target network system comprises:

determining the energy consumption weight parameter of the connecting edge between the server nodes in the adjacent structural layers according to the following formula:

indicating the switching state of the exchanger r, P_r ^actIndicating the energy consumption at the time of opening the exchanger r, P_r ^portRepresenting the energy consumption of the ports of the switch r in transferring data.

6. The method of claim 5, further comprising:

7. The method according to claim 3, wherein processing the initial energy consumption hierarchy assistance map according to the energy consumption weight parameters of the server nodes and the energy consumption weight parameters of the connecting edges to obtain a corresponding target weighted energy consumption hierarchy assistance map comprises:

marking energy consumption weight parameters of corresponding server nodes at the server nodes in the initial energy consumption level auxiliary graph; and marking energy consumption weight parameters of corresponding connecting edges at the connecting edges between the server nodes in the adjacent structural layers in the initial energy consumption level auxiliary graph to obtain the target weighted energy consumption level auxiliary graph.

8. The method of claim 7, wherein determining a target deployment strategy for a target service function chain according to the target weighted energy consumption level assistance map comprises:

9. The method of claim 8, wherein searching for a minimum weight path between a start point and an end point as a target link path according to a target weighted energy consumption level auxiliary graph comprises:

10. The method of claim 7, wherein after determining a target deployment policy for a target service function chain according to the target weighted energy consumption level assistance map, the method further comprises:

11. The method of claim 1, wherein the target network system comprises at least one of: a network system of a micro data center, a network system of edge computing and a network system of the Internet of things.

12. The method of claim 1, wherein the network topology data and the operational parameters of the target network system are determined from a weighted energy consumption hierarchy assistance map of existing functional service chains previously deployed in the target network system.

13. A data processing apparatus, comprising:

14. A server comprising a processor and a memory for storing processor-executable instructions which, when executed by the processor, implement the steps of the method of any one of claims 1 to 12.

15. A computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, carry out the steps of the method of any one of claims 1 to 12.