Industrial edge network micro-service optimization method based on dynamic arrangement and migration expansion

Abstract

This invention provides a microservice optimization method for industrial edge networks based on dynamic orchestration and migration expansion, comprising: abstracting the industrial edge network into an intelligent industrial network model composed of heterogeneous edge nodes and links; modeling industrial application requests as request chains composed of multiple microservices with data dependencies; calculating microservice instance deployment schemes and request routing schemes; statistically analyzing time-varying request flows; and utilizing a method based on... m / m / c This invention utilizes a multivariate performance analysis model of queue networks and a binary search algorithm to analyze and statistically determine the minimum number of cores that satisfy latency constraints. It then dynamically scales and adjusts the core resources of deployed microservice instances. Based on the preliminary dynamic scaling and adjustment results and migration costs, it solves a mixed-integer nonlinear programming migration decision problem to optimize the optimal collaborative deployment and request routing of microservice instances among edge nodes. This invention improves the service continuity and overall energy efficiency of industrial edge network systems.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial edge network, and more particularly, to an industrial edge network micro-service optimization method and system based on dynamic arrangement and migration expansion, an electronic device and a storage medium. BACKGROUND

[0002] With the rapid development of Internet of Things, 5G and artificial intelligence technology, industrial automation, intelligent manufacturing and industrial Internet of Things have gradually become the core field of new generation information technology application. Industrial edge network, as a key platform for data processing and application deployment, is becoming increasingly important. Industrial edge network architecture is composed of a large number of heterogeneous edge nodes, including a group of edge servers and processing cores, and data transmission is realized through communication links. In this network, the application system is usually based on modular micro-service architecture, which splits complex industrial applications into multiple functionally single, independent and interactive micro-services through unified interfaces, and each micro-service instance is deployed on different edge nodes to cooperatively complete industrial application processing tasks in the form of a service graph.

[0003] Traditional cloud computing mode has high transmission delay and slow response speed when dealing with real-time requirements of industrial sites, and industrial edge computing technology emerges as the times require. However, the production plan, equipment state and various random events in the industrial field make the request flow have strong time-varying and uncertainty, resulting in dynamic changes in the request arrival rate, processing load and resource occupation of each micro-service instance in the system. Existing technologies focus on the joint optimization of micro-service static deployment and request routing, ignoring the cooperative adjustment of micro-service instance core resource scaling and inter-node migration expansion under dynamic conditions. In industrial edge network, node resources are limited and network link bandwidth fluctuates significantly, making it difficult to realize real-time adaptive scheduling of micro-service instances in core configuration and node distribution while ensuring request response delay. SUMMARY

[0004] The present application provides an industrial edge network micro-service optimization method and system based on dynamic arrangement and migration expansion, an electronic device and a storage medium to solve the problem that the limited node resources and network link bandwidth fluctuations in the existing industrial edge network cannot realize dynamic adaptive resource arrangement.

[0005] According to a first aspect of the present application, an industrial edge network micro-service optimization method based on dynamic arrangement and migration expansion is provided, comprising:

[0006] S1, abstracting the industrial edge network into an intelligent industrial network model composed of a plurality of heterogeneous edge nodes and links, modeling an industrial application request into a request chain composed of a plurality of microservices sequentially combined based on the intelligent industrial network model, and calculating a microservice instance deployment scheme and a request routing scheme in the request chain;

[0007] S2, statistically calculating a time-varying request flow in real time, analyzing a minimum core quantity meeting a delay constraint in a statistical result by using a multi-element performance analysis model based on a queue network and a binary search algorithm, and dynamically adjusting core resources of the deployed microservice instances based on an analysis result; m / m / c

[0008] S3, converting a complex multi-node migration decision problem into a mixed integer nonlinear programming solving problem and solving the problem according to a preliminary dynamic adjustment result and a migration cost, and realizing optimal cooperative deployment and request routing optimization of the microservice instances between the edge nodes according to a solving result.

[0009] On the basis of the above technical solution, the application can also be improved as follows.

[0010] Optionally, in step S1, the industrial edge network is abstracted into an intelligent industrial network model composed of a plurality of heterogeneous edge nodes and links, and an industrial application request is modeled into a request chain composed of a plurality of microservices sequentially combined based on the intelligent industrial network model, comprising:

[0011] S101, constructing an intelligent industrial network model based on a modular microservice architecture according to network configuration of the industrial edge network and communication relationship of servers, defining the intelligent industrial network model as an undirected graph model ;

[0012] wherein, is a set of edge nodes in the industrial edge network, ;

[0013] Each edge node of the intelligent industrial network model contains an industrial switch and a group of heterogeneous edge servers, and each group of servers contains different numbers of cores , is a set of links connecting two servers , each physical link corresponds to a pair of connected server nodes , represents the maximum bandwidth capacity between two connected servers , represents the transmission delay between two connected servers;

[0014] ​S102, define the industrial network service in the intelligent industrial network model is composed of a plurality of industrial microservices ms with data dependency relationship in sequence, wherein is a set of all industrial microservices;

[0015] S103, according to the dynamic change of industrial application request, the relevant traffic of request chain at the moment is defined as the request flow with Poisson distribution of arrival rate , is a set of request flows, represent the microservice order logic set of request chain , represent the actual arrival rate of request flow at the moment , ， represent the actual performance requirement of request chain at the moment , represent the response priority of request chain at the moment , represent the response tolerance delay of request chain at the moment , then represent the transmission bandwidth requirement of request chain at the moment .

[0016] Optionally, in step S1, the calculation of the microservice instance deployment scheme and the request routing scheme in the request chain includes:

[0017] S104, obtain the microservice instance deployment scheme and request routing scheme configured under the current industrial application request chain model;

[0018] wherein, the microservice instance deployment scheme at the moment is expressed as , represent the set of all microservice instances in the industrial application request chain model at the moment , represent the set of all microservice instances in the industrial application request chain model at the moment occupy the edge node resources;

[0019] the request routing scheme at the moment is expressed as , wherein, is defined as The set of route paths for all request flows at any given time. Defined as The set of transition probabilities for all request flows at a given time.

[0020] Optionally, step S2 includes:

[0021] S201, according to the time slots arriving in sequence and The request stream data is compared with the changes in the type of request stream and the arrival rate under the dual-timeslot scenario;

[0022] S202, based on the calculation results of S201, according to The multivariate performance analysis model of the queue network calculates the dwell time of each microservice instance. Use the binary search algorithm to determine the satisfying Minimum number of cores for constraints ,in, for t The request chain that ensures core shrinkage does not cause performance degradation at any given time. The response latency threshold;

[0023] S203, based on minimum core count For microservice instances whose single-node resources meet the scaling requirements, the deployment of that microservice instance on the current node will be expanded; for microservice instances whose single-node scaling is insufficient, they will be recorded in the migration set. .

[0024] Optionally, step S201 specifically includes:

[0025] make The request stream set of the time slot is ,when Request stream of time slots Upon arrival, it is categorized according to the type of request. The existing request stream set in the time slot as well as New types of request streams arriving in time slots ;

[0026] against The existing set of request streams at any given time ,consider Request flow arrival rate at any given time Changes to deployed microservice instances Load changes at the location, combined with The probability of route transition at time t and Request arrival rate at specific times Calculate the temporary request arrival rate at each instance. ;

[0027] Based on the calculated temporary request arrival rate and Total request arrival rate at any given time Each microservice instance is divided into a set of load-reducing instances and a set of load-increasing instances.

[0028] Optionally, step S203 specifically includes:

[0029] For the load reduction instance set, based on the minimum number of cores Release redundant cores;

[0030] For the load-increasing instance set, based on the minimum number of cores Determine the remaining number of cores on the node where the microservice instance resides. Does the microservice instance meet resource constraints? If there are sufficient remaining resources, expand the service; otherwise, merge the microservice instance with the minimum number of cores. Record to the collection of instances to be migrated .

[0031] Optionally, step S3 includes:

[0032] S301, based on the preliminary dynamic scaling adjustment results, based on FFD Algorithm determines new types of request streams The deployment plan was then synchronized to the set of instances to be migrated. ;

[0033] S302, Calculate the set to be migrated using the migration cost function. The service interruption cost, deployment pull cost, and resource consumption difference cost of migrating each microservice instance to other nodes that meet deployment constraints are used to obtain the migration cost set. ;

[0034] S303, based on a mixed-integer nonlinear programming solver, determines the set to be migrated based on migration cost. The optimal migration and scaling scheme for microservice instances to be migrated between target nodes to update the microservice deployment plan. and request routing scheme .

[0035] Optionally, step S302 further includes: constructing a migration cost function based on service interruption cost, pull deployment cost, and resource consumption difference cost using a migration scaling algorithm, wherein the migration cost function is expressed as:

[0036]

[0037] in, For migration costs, service interruption cost, service interruption cost weight, current migration node pulling deployment cost, current migration node pulling deployment cost weight, resource occupation difference cost, resource occupation difference cost weight;

[0038] define microservice instance from source edge node migrate to target edge node interruption cost generated during migration is , indicating the duration of service interruption, then service interruption cost at time is represented as:

[0039]

[0040] wherein the binary variable means whether the microservice instance migrates to the target edge node at time , is the microservice instance deployed on the target edge node , is the set of all microservice instances in the system at time , is the set of all microservice instances in the system at time ;

[0041] define pulling microservice generated cost is , indicating the pulling and deployment time of microservice , then the new instance deployment cost of the target edge node when the microservice instance migrates is represented as:

[0042]

[0043] complete migration node pulling deployment cost at time is represented as:

[0044]

[0045] define is The difference in resource overhead caused by microservice migration at this time includes the difference in communication overhead between instances and the difference in resource overhead occupied by instances, expressed as:

[0046]

[0047] in, This represents the difference in communication overhead between instances. Weighting the difference in communication overhead between instances. The difference in resource overhead for the instance. Weight the difference in resource overhead used by instances;

[0048] Inter-instance communication overhead difference Difference between instance resource consumption and They are represented as follows:

[0049]

[0050] .

[0051] in, for t Two connected servers at any time The remaining bandwidth capacity between for Two connected servers at any time The remaining bandwidth capacity between for The source edge node where the microservice instance is located The number of cores, for The source edge node where the microservice instance is located The number of core components.

[0052] Optionally, step S303 includes:

[0053] Based on a mixed-integer nonlinear programming solver, the set to be migrated is determined according to the migration cost. The optimal migration and expansion scheme for microservice instances to be migrated between the source edge node and the target edge node is formalized as follows: Optimization objective and constraints:

[0054]

[0055]

[0056] in, Indicates that solving makes When the minimum value is reached ( The value, Source edge node n Microservice Examples Core number, Source edge node n Accessing microservice instances Request delivery rate For target edge nodes Microservice Examples Core number, For target edge nodes Accessing microservice instances Request delivery rate For the source edge node n Microservice Examples The core number is and source edge nodes n Accessing microservice instances The request delivery rate Microservice Instance Processing latency, To the target edge node Microservice Examples The core number is and target edge nodes Accessing microservice instances The request delivery rate Microservice Instance Processing latency, For the microservice instance obtained by computation Maximum tolerable latency, for Time source edge node n Accessing microservice instances Request delivery rate The source edge node where the microservice instance resides n The remaining number of cores, For target edge nodes The remaining number of cores;

[0057] Based on various constraints, and with the objective of minimizing migration cost, a mixed-integer nonlinear programming solver is invoked. Gurobi Solving the optimization objective yields an approximate optimal solution;

[0058] Update based on the approximate optimal solution Moment's microservice deployment solution and request routing scheme .

[0059] According to a second aspect of the present invention, an industrial edge network microservice optimization system based on dynamic orchestration and migration expansion is provided, comprising:

[0060] a pre-arrangement module, configured to abstract the industrial edge network into an intelligent industrial network model composed of a plurality of heterogeneous edge nodes and links, and model an industrial application request into a request chain composed of a plurality of microservices in sequence based on the intelligent industrial network model, and calculate a microservice instance deployment scheme and a request routing scheme in the request chain;

[0061] a core dynamic adjustment module, configured to statistically calculate time-varying request flows in real time, and analyze a minimum core quantity meeting a delay constraint in the statistical results by using a multi-element performance analysis model based on a queue network and a binary search algorithm, and dynamically scale core resources of the deployed microservice instances based on the analysis results; m / m / c a core dynamic adjustment module, configured to statistically calculate time-varying request flows in real time, and analyze a minimum core quantity meeting a delay constraint in the statistical results by using a multi-element performance analysis model based on a queue network and a binary search algorithm, and dynamically scale core resources of the deployed microservice instances based on the analysis results;

[0062] an arrangement optimization module, configured to convert a complex multi-node migration decision problem into a mixed integer nonlinear programming solving problem and solve the problem according to the preliminary dynamic scaling adjustment results and migration costs, and realize optimal cooperative deployment of the microservice instances among the edge nodes and request routing optimization according to a solving result.

[0063] According to a third aspect of the present application, an electronic device is provided, comprising a memory and a processor, the processor being configured to implement the steps of the above-mentioned industrial edge network microservice optimization method based on dynamic arrangement and migration expansion when executing a computer management program stored in the memory.

[0064] According to a fourth aspect of the present application, a computer readable storage medium is provided, having a computer management program stored thereon, the computer management program being configured to implement the steps of the above-mentioned industrial edge network microservice optimization method based on dynamic arrangement and migration expansion when executed by a processor.

[0065] The industrial edge network microservice optimization method, system, electronic device and storage medium based on dynamic arrangement and migration expansion provided by the present application can maximize the guarantee of request success rate by using the microservice cooperative optimization mode based on dynamic arrangement and migration expansion, taking into account the response time delay requirement of the system in the whole time period, reducing the end-to-end request delay, not only significantly reducing the additional resource waste and energy consumption cost caused by frequent migration, improving the service continuity and overall energy efficiency of the industrial edge network system, but also maximizing the guarantee of request success rate. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 A flowchart of the industrial edge network microservice optimization method based on dynamic arrangement and migration expansion provided by the present application is provided.

[0067] Figure 2 An intelligent industrial network model structure diagram is provided for an embodiment.

[0068] Figure 3This is a flowchart illustrating the process of dynamically scaling and adjusting core resources of a deployed microservice instance in one embodiment.

[0069] Figure 4 This is a schematic diagram illustrating the process of achieving optimal collaborative deployment and request routing optimization of microservice instances across edge nodes through migration and expansion in one embodiment.

[0070] Figure 5 A block diagram of an industrial edge network microservice optimization system based on dynamic orchestration and migration expansion provided by the present invention;

[0071] Figure 6 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0072] Figure 7 This is a schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. Detailed Implementation

[0073] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0074] Figure 1 A flowchart of an industrial edge network microservice optimization method based on dynamic orchestration and migration expansion provided by this invention is shown below. Figure 1 As shown, the method includes steps S1 to S3:

[0075] S1. In scenarios such as industrial IoT and smart manufacturing, the industrial edge network is abstracted into an intelligent industrial network model composed of multiple heterogeneous edge nodes and links. Based on the intelligent industrial network model, industrial application requests are modeled as request chains composed of multiple microservices with data dependencies in sequence, and the deployment scheme of microservice instances and request routing scheme in the request chain are calculated.

[0076] S2, real-time statistics of time-varying request streams, utilizing based on m / m / c The minimum number of cores that meet the latency constraints is analyzed and statistically determined using a multivariate performance analysis model and a binary search algorithm for queue networks. Based on the analysis results, the core resources of deployed microservice instances are dynamically scaled and adjusted.

[0077] S3 transforms the complex multi-node migration decision problem into a mixed integer nonlinear programming (MINLP) problem based on the initial dynamic scaling adjustment results and migration costs, and solves the problem. Based on the solution results, it realizes the optimal collaborative deployment of microservice instances and request routing optimization among edge nodes.

[0078] It can be understood that based on the defects in the background art, the embodiment of the application proposes an industrial edge network microservice optimization method based on dynamic arrangement and migration expansion. In the scene of industrial Internet of Things and intelligent manufacturing, the method adopts a microservice cooperative optimization mode based on dynamic arrangement and migration expansion, takes into account the response time delay requirement of the system in the whole period, reduces the end-to-end request delay, not only significantly reduces the additional resource waste and energy consumption cost caused by frequent migration, improves the service continuity and overall energy efficiency of the industrial edge network system, but also maximizes the request success rate.

[0079] In a possible embodiment mode, please refer to Figure 2 , in step S1, in the scene of industrial Internet of Things and intelligent manufacturing, the industrial edge network is abstracted into an intelligent industrial network model composed of multiple heterogeneous edge nodes and links, and based on the intelligent industrial network model, the industrial application request is modeled as a request chain composed of multiple microservices with data dependency relationship, specifically including sub-steps S101-S103:

[0080] S101, according to the network configuration of the industrial edge network and the communication relationship of the server, an intelligent industrial network model based on modular microservice architecture is constructed, and the intelligent industrial network model is defined as an undirected graph model ;

[0081] Among them, is a set of edge nodes in the industrial edge network, ; for example Figure 2 the edge node , the edge node , the edge node , the edge node .

[0082] Each edge node of the intelligent industrial network model contains an industrial switch and a group of heterogeneous edge servers, and each group of servers contains different numbers of cores . is a set of links connecting two servers , each physical link corresponds to a pair of connected server nodes . Due to the resource limitation of the edge network, the bandwidth between adjacent nodes is considered, represents the maximum bandwidth capacity between two connected servers , and the total bandwidth resource occupied on the link should be less than the bandwidth resource of the link. represents the transmission delay between two connected servers;

[0083] S102, in the intelligent industrial environment based on the modular micro-service architecture, defining the industrial network service in the intelligent industrial network model is composed of a plurality of industrial micro-services with data dependency relationship sequentially combined, wherein is a set of all industrial micro-services, for example Figure 2 micro-services micro-services Edge node industrial application request chain initiated by micro-services micro-services micro-services micro-services sequentially combined, edge node industrial application request chain initiated by micro-services micro-services micro-services sequentially combined.

[0084] S103, according to the dynamically changing industrial application request, defining the relevant traffic of request chain at the moment as request flow with Poisson distribution of arrival rate , is a set of request flows; represent the micro-services of the request chain sequential logic set, represent the actual arrival rate of request flow at the moment ， represent the actual performance demand of request chain at the moment represent the response priority of request chain at the moment represent the response tolerance delay of request chain at the moment represent the transmission bandwidth demand of request chain at the moment

[0085] In one possible implementation mode, in step S1, the calculation of the micro-service instance deployment scheme and the request routing scheme in the request chain comprises:

[0086] S104, obtaining the micro-service instance deployment scheme configured under the current industrial application request chain model​​​​​ With request routing scheme .

[0087] To efficiently handle complex and diverse industrial application requests in industrial edge networks, it is necessary to consider both the orchestration of each microservice instance and the distribution of request traffic across the entire physical network. Throughout the dynamic orchestration process, microservices... At the edge node Instantiated deployment in the middle consumes a certain number of edge node resources. It provides services externally, and due to dynamic changes in service demand and network load, it adjusts the amount of resources used or migrates the deployment nodes of instances across different time slots. Therefore, it will The microservice instance deployment scheme of the time is described as follows: , represent The collection of all microservice instances in the current industrial application request chain model. represent The set of edge node resources occupied by all microservice instances in the current industrial application request chain model;

[0088] Once a request flow enters the industrial edge network, it is routed sequentially to the nodes where each microservice instance is deployed, according to the order of the microservices in the request chain. This process continues until the last microservice instance has finished processing, at which point the request flow is considered complete. During this process, to address the uneven distribution of resources and dynamic load changes within the network, the request flow can be dynamically distributed to different instances of the same microservice according to a traffic splitting strategy, adapting dynamically to the computing power and resource status of different nodes. To more clearly describe this process, we define... for At this moment, the request stream From a microservice instance Move to another microservice instance The probability of [something]. Furthermore, due to the involvement of forwarding routes to different microservice instances, the complete request flow processing may contain multiple routing paths. Definition for Request stream at any time The set of routing paths, where To record the routing path of a specific node, a binary variable is defined. express Is it at the edge node? Stop there. From above, The request flow routing scheme at any given time is described as follows: ,in Defined as The set of route paths for all request flows at any given time. Defined as The set of transition probabilities for all request flows at a given time.

[0089] In one possible embodiment, such as Figure 3 As shown in the flowchart, step S2 includes sub-steps S201~S203:

[0090] S201, according to the time slots arriving in sequence and The request stream data is compared with the change in the type of request stream and the arrival rate under the dual-timeslot scenario.

[0091] More specifically, sub-step S201 includes:

[0092] As time slots change, the requests received by the edge cloud service clusters of the current smart factory from industrial applications also change accordingly. The request stream set of the time slot is ,when Request stream of time slots Upon arrival, it is categorized according to the type of request. The existing request stream set in the time slot as well as New types of request streams arriving in time slots .

[0093] in, The existing request stream set in the time slot This indicates that the corresponding microservice instances have already deployed these request flows on the edge servers. The only factor to consider for time slots is the change in arrival rate. Accordingly, New types of request streams arriving in time slots yes The constantly increasing request flow types necessitate the deployment or migration of new microservice instances to meet the demands of new microservice instances and new routing requirements. To further reduce overall system overhead, a core scaling strategy with lower adjustment costs is prioritized, enabling more timely responses to changes in request load.

[0094] against The existing set of request streams at any given time First consideration Request flow arrival rate at any given time Changes to deployed microservice instances Load changes at the location, combined with The probability of route transition at time t and Request arrival rate at specific times Calculate the temporary request arrival rate at each instance. This provides the foundation for core control, thereby ensuring that microservice instances can flexibly respond to the time-varying characteristics of request traffic;

[0095] Based on the calculated temporary request arrival rate and Total request arrival rate at any given time Each microservice instance is divided into a set of unloaded instances and a set of load-increasing instances to facilitate the shrinking and expansion of the core usage of microservice instances in the future.

[0096] S202, based on the calculation results of S201, according to The multivariate performance analysis model of the queue network calculates the dwell time of each microservice instance. The binary search multi-queue algorithm is used to determine the satisfying condition. Minimum number of cores for constraints ,in, for t The request chain that ensures core shrinkage does not cause performance degradation at any given time. The response latency threshold, i.e., the specified response time; for example, for edge nodes. n microservice instances It is necessary to determine whether the stay duration is met. Minimum number of cores , This is the response latency threshold for this microservice instance. Calculate the minimum number of cores. This ensures that core contraction saves costs while maintaining service quality, preventing the overall system performance from degrading.

[0097] S203, based on minimum core count For microservice instances whose single-node resources meet the scaling requirements, the deployment of that microservice instance on the current node will be expanded; for microservice instances whose single-node scaling is insufficient, they will be recorded in the migration set. .

[0098] In sub-step S203, load release and core expansion are performed on microservice instances that need to participate in scaling to meet service quality requirements.

[0099] In practice, to reduce resource waste and provide more free cores for overloaded instances, the algorithm prioritizes releasing cores from instances that are de-loaded. For the set of de-loaded instances, the algorithm determines which instances meet the required latency for their dwell time. Less than the specified response time Minimum number of cores for constraints By leveraging the scaling deployment mechanism of microservice instances, the load on edge nodes where microservice instances are deployed is reduced at a lower cost, freeing up redundant cores. After the redundant cores are released, core expansion of the load-increasing instances is then performed. During core expansion, for the set of load-increasing instances, binary search is also attempted to find instances with sufficient latency. Less than the specified response time Minimum number of cores for constraints And based on the minimum number of cores Determine the remaining number of cores on the node where the microservice instance resides. Does the microservice instance meet resource constraints? If there are sufficient remaining resources, expand the service; otherwise, merge the microservice instance with the minimum number of cores. Record to the collection of instances to be migrated .

[0100] It is understandable that in step S2, the microservice instance is completed. The node occupies the core Even after scaling adjustments, some instances (such as collections to be migrated) will still remain. The individual microservice instances in the network cannot meet the resource expansion requirements on a single edge node. The constant request load, coupled with the fact that existing microservice orchestration solutions cannot fully respond to it. New type of request stream arriving at the time Therefore, step S3 adopts a migration cost-based expansion strategy to transfer microservice instances between nodes.

[0101] In one possible embodiment, please refer to Figure 4 The process, step S3, includes sub-steps S301~S303:

[0102] S301, based on the preliminary dynamic scaling adjustment results, based on FFD Algorithm determines new types of request streams The deployment plan was then synchronized to the set of instances to be migrated. .

[0103] Specifically, in order to effectively respond New type of request stream arriving at the time The first step is to implement a microservice orchestration scheme after core scaling has been completed. Building upon this foundation, additional microservice instances need to be deployed to support new types of request flows, utilizing industrial-grade... FFD The algorithm determines its initial routing scheme, and this initial solution will be recorded in the migration instance set. This facilitates subsequent steps in leveraging migration expansion to further optimize migration overhead and reduce migration costs.

[0104] S302, determine the migration instance set The migration cost of the microservice instance to be migrated among the target edge nodes is specifically calculated by using a migration cost function The service interruption cost, the pull deployment cost, and the resource occupation difference cost of each microservice instance in the to-be-migrated set migrating to other nodes satisfying the deployment constraint are obtained to obtain a migration cost set .

[0105] It can be understood that the migration cost needs to be considered when the microservice instance is migrated, and the migration cost includes three components: the service interruption cost, the pull deployment cost, and the resource occupation difference cost of each microservice instance migrating to other nodes satisfying the deployment constraint.

[0106] First, a migration cost function is constructed based on the service interruption cost, the pull deployment cost, and the resource occupation difference cost by using a migration expansion algorithm, and the migration cost function is represented as:

[0107]

[0108] Among them, is the migration cost, is the service interruption cost, is the service interruption cost weight, is the pull deployment cost of the current migration node, is the pull deployment cost weight of the current migration node, is the resource occupation difference cost, is the resource occupation difference cost weight.

[0109] When the microservice instance is migrated, the related running data of the microservice instance needs to be synchronized from the source edge node to the target edge node and then deployed and started on the target edge node . During this period, the microservice instance cannot provide services to the outside, causing service interruption. Therefore, frequent migration of the microservice instance will affect the continuity of the service. The interruption cost generated during the migration of the microservice instance from the source edge node to the target edge node is , which represents the duration of service interruption, and the service interruption cost at the moment is represented as:

[0110]

[0111] Among them, the binary variable has the meaning that the microservice instance at the moment Whether to migrate to the target edge node , , For the microservice instance deployed in the target edge node , , For the set of all microservice instances in the system at the moment, For the set of all microservice instances in the system at the moment. When the microservice instance migrates, if the target edge node does not deploy the same microservice, it needs to pull the microservice from the cloud and deploy the instance. If the same microservice has been deployed, there is no corresponding pulling cost. Define the cost generated by pulling the microservice

[0112] , , represents the pulling and deployment time of the microservice , When the microservice instance migrates, the new instance deployment cost of the target edge node is represented as:

[0113]

[0114] Therefore, The complete migration node pulling and deployment cost at the moment is represented as:

[0115]

[0116] Since the microservice application is characterized by a chain structure, when the microservice instance migrates between edge nodes, the communication overhead between microservice instances will change. At the same time, due to the influence of request performance constraints, the amount of resources occupied by upstream and downstream microservice instances will also change, thereby affecting the cost of microservice instance migration. Define as the resource overhead difference caused by microservice migration at the moment , which includes the instance communication overhead difference and the instance resource occupation overhead difference, and is represented as:

[0117]

[0118] Among them, is the instance communication overhead difference, is the instance communication overhead difference weight, is the instance resource occupation overhead difference, is the instance resource occupation overhead difference weight;

[0119] The instance communication overhead difference ​Difference between instance resource consumption and They are represented as follows:

[0120]

[0121] .

[0122] in, for t Two connected servers at any time The remaining bandwidth capacity between for Two connected servers at any time The remaining bandwidth capacity between for t The source edge node where the microservice instance is located n The number of cores, for t -1 The source edge node where the microservice instance is located at time 1 n The number of core components.

[0123] Traverse the collection of migration instances For microservice instances, calculate migration costs and record them in the migration cost set. In the subsequent process, based on the principle of minimum migration cost, the migration and expansion scheme for each instance is evaluated. Since the migration process will not only generate deployment costs for node resource occupation and service interruption costs for related industrial application requests that are being executed, it is also necessary to further consider the expected difference in instance routing latency before and after the adjustment in order to correct the performance loss caused by the migration.

[0124] S303, based on a mixed-integer nonlinear programming solver Gurobi The optimal migration and expansion scheme for the microservice instances to be migrated between target nodes is determined based on migration costs. Specifically, this involves using a mixed-integer nonlinear programming solver to determine the set of microservice instances to be migrated based on migration costs. The optimal migration and scaling scheme for microservice instances to be migrated between target nodes to update the microservice deployment plan. and request routing scheme .

[0125] Understandably, in In a queuing model, scaling up a single instance results in performance degradation. Given instance processing latency constraints, to compensate for this, edge nodes need more cores to offset the performance loss, while simultaneously distributing the load from arriving requests evenly to achieve maximum processing efficiency. Because... The nonlinear characteristics of queuing systems require further consideration of the nonlinear constraints of queuing delay calculation and core resource allocation. Accordingly, this step models the complex multi-node migration decision problem as a typical mixed-integer nonlinear programming (MINLP) problem to achieve optimal collaborative deployment and request routing optimization of microservice instances among edge nodes.

[0126] Therefore, in order to minimize the additional migration cost caused by the split expansion, in sub-step S303, based on the mixed-integer nonlinear programming solver, the set to be migrated is divided according to the migration cost. The optimal migration and expansion scheme for microservice instances to be migrated between the source edge node and the target edge node is formalized as follows: Optimization objective and constraints:

[0127]

[0128]

[0129] in, Indicates that solving makes When the minimum value is reached ( The value, Source edge node n Microservice Examples Core number, Source edge node n Accessing microservice instances Request delivery rate For target edge nodes Microservice Examples Core number, For target edge nodes Accessing microservice instances Request delivery rate For the source edge node n Microservice Examples The core number is and source edge nodes n Accessing microservice instances The request delivery rate Microservice Instance Processing latency, To the target edge node Microservice Examples The core number is and target edge nodes Accessing microservice instances The request delivery rate Microservice Instance Processing latency, For the microservice instance obtained by computation Maximum tolerable latency, for Time source edge node n Accessing microservice instances Request delivery rate The source edge node where the microservice instance resides n The remaining number of cores, For target edge nodes The remaining number of cores.

[0130] Based on the above constraints, and ensuring that the deployment cost of each microservice is below a preset threshold, a migration cost function is constructed that includes service interruption cost, pull deployment cost, and resource consumption difference cost. These costs are then combined in a weighted summation, and the goal is to minimize this weighted cost by calling its standard solver. Gurobi The interface solves the mixed-integer nonlinear programming (MINLP) problem, outputting an approximate optimal solution. This optimal solution corresponds to the migration scheme that minimizes the total cost of microservice migration while satisfying the low deployment cost constraint. It further clarifies the deployment and migration decisions for each microservice instance among candidate nodes in the edge network, and updates the system based on this optimal solution. Moment's microservice deployment solution and request routing scheme .

[0131] Figure 5 A structural diagram of an industrial edge network microservice optimization system based on dynamic orchestration and migration expansion is provided for an embodiment of the present invention, as shown below. Figure 5 As shown, an industrial edge network microservice optimization system based on dynamic orchestration and migration expansion includes a pre-orchestration module, a core dynamic adjustment module, and an orchestration optimization module, wherein:

[0132] The pre-arrangement module is used to abstract the industrial edge network into an intelligent industrial network model composed of multiple heterogeneous edge nodes and links, and based on the intelligent industrial network model, to model industrial application requests as a request chain composed of multiple microservices with data dependencies in sequence, and to calculate the deployment scheme of microservice instances and the request routing scheme in the request chain.

[0133] The core dynamic adjustment module is used to statistically analyze time-varying request streams in real time and utilize based on... m / m / c The minimum number of cores that meet latency constraints is analyzed in the multivariate performance analysis model and binary search algorithm analysis results of queue networks, and the core resources of deployed microservice instances are dynamically scaled and adjusted based on the analysis results.

[0134] The orchestration optimization module is used for converting a complex multi-node migration decision problem into a mixed integer nonlinear programming solving problem according to the preliminary dynamic scaling adjustment result and the migration cost, solving the mixed integer nonlinear programming solving problem, and realizing optimal cooperative deployment and request routing optimization of the microservice instances between the edge nodes according to a solving result.

[0135] It can be understood that the industrial edge network microservice optimization system based on dynamic orchestration and migration expansion provided by the present application corresponds to the industrial edge network microservice optimization method based on dynamic orchestration and migration expansion provided by the foregoing embodiments, and the related technical features of the industrial edge network microservice optimization system based on dynamic orchestration and migration expansion can refer to the related technical features of the industrial edge network microservice optimization method based on dynamic orchestration and migration expansion, which will not be repeated here.

[0136] Please refer to Figure 6 , Figure 6 An embodiment of the electronic device provided by the present application is shown in the embodiment schematic diagram of the electronic device. As shown in Figure 6 , the present application provides an electronic device 600, which comprises a memory 610, a processor 620, and a computer program 611 stored in the memory 610 and capable of running on the processor 620. When the processor 620 executes the computer program 611, the following steps are realized:

[0137] S1, abstracting the industrial edge network into an intelligent industrial network model composed of a plurality of heterogeneous edge nodes and links, modeling the industrial application request into a request chain composed of a plurality of microservices with data dependency relationship based on the intelligent industrial network model, and calculating the microservice instance deployment scheme and the request routing scheme in the request chain;

[0138] S2, real-time statistical time-varying request flow, using a multi-element performance analysis model based on a queue network and a binary search algorithm to analyze the minimum core number that meets the delay constraint in the statistical result, and performing dynamic scaling adjustment of the core resources of the deployed microservice instances based on the analysis result; m / m / c

[0139] S3, converting a complex multi-node migration decision problem into a mixed integer nonlinear programming solving problem according to the preliminary dynamic scaling adjustment result and the migration cost, solving the mixed integer nonlinear programming solving problem, and realizing optimal cooperative deployment and request routing optimization of the microservice instances between the edge nodes according to a solving result.

[0140] Please refer to Figure 7 , Figure 7 An embodiment of the computer readable storage medium provided by the present application is shown in the embodiment schematic diagram of the computer readable storage medium. As shown in Figure 7 , the present embodiment provides a computer readable storage medium 700, which stores a computer program 711. When the computer program 711 is executed by a processor, the following steps are realized:​

[0141] S1, abstracting the industrial edge network into an intelligent industrial network model composed of a plurality of heterogeneous edge nodes and links, modeling an industrial application request into a request chain composed of a plurality of microservices with data dependency relationship based on the intelligent industrial network model, and calculating a microservice instance deployment scheme and a request routing scheme in the request chain;

[0142] S2, statistically calculating a time-varying request flow in real time, and analyzing a minimum core quantity meeting a delay constraint in a statistical result by using a multi-element performance analysis model based on a queue network and a binary search algorithm, and dynamically adjusting core resources of the deployed microservice instances based on an analysis result; m / m / c S3, converting a complex multi-node migration decision problem into a mixed integer nonlinear programming solving problem and solving according to a preliminary dynamic scaling adjustment result and a migration cost, and realizing optimal cooperative deployment and request routing optimization of the microservice instances between the edge nodes according to a solving result.

[0143] The industrial edge network microservice optimization method, system, electronic device and storage medium provided by the embodiment of the present application can reduce the end-to-end request delay by adopting the microservice cooperative optimization method based on the dynamic arrangement and migration expansion, significantly reduce the additional resource waste and energy cost caused by frequent migration, improve the service continuity and overall energy efficiency of the industrial edge network system, and maximize the request success rate.

[0144] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0145] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media containing computer usable program code (including but not limited to disk storage, CD-ROM, optical storage, etc.).

[0146]

[0147] ​The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the one or more flowcharts and / or blocks

[0148] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the one or more flowcharts and / or blocks

[0149] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the one or more flowcharts and / or blocks

[0150] While the preferred embodiments of the application have been described, additional variations and modifications can be employed by those skilled in the art. Therefore, the appended claims are intended to cover all such variations and modifications as falling within the scope of the application.

[0151] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A microservice optimization method for industrial edge networks based on dynamic orchestration and migration expansion, characterized in that, include: S1, the industrial edge network is abstracted into an intelligent industrial network model composed of multiple heterogeneous edge nodes and links. Based on the intelligent industrial network model, industrial application requests are modeled as request chains composed of multiple microservices with data dependencies in sequence, and the deployment scheme of microservice instances and request routing scheme in the request chain are calculated. S2, real-time statistics of time-varying request streams, utilizing based on m / m / c The minimum number of cores satisfying latency constraints is determined from the statistical results of a multivariate performance analysis model and a binary search algorithm for queue networks. Based on the analysis results, dynamic scaling adjustments are made to the core resources of deployed microservice instances. The dynamic scaling adjustments to core resources include: S203: Based on the minimum number of cores, for microservice instances whose single-node resources meet the scaling requirements, the deployment of that microservice instance on the current node is expanded; for microservice instances whose single-node scaling is insufficient, they are recorded in the migration set. ; S3, based on the preliminary dynamic scaling adjustment results and migration costs, transforms the complex multi-node migration decision problem into a mixed-integer nonlinear programming problem and solves it. Based on the solution results, it achieves optimal collaborative deployment of microservice instances among edge nodes and optimizes request routing; including: S302, Calculate the set to be migrated using the migration cost function. The service interruption cost, deployment pull cost, and resource consumption difference cost of migrating each microservice instance to other nodes that meet deployment constraints are used to obtain the migration cost set. ; S303, based on a mixed-integer nonlinear programming solver, determines the set to be migrated based on migration cost. The optimal migration and scaling scheme for microservice instances to be migrated between target nodes to update the microservice deployment plan. ( +1) and request routing scheme ( +1).

2. The industrial edge network microservice optimization method based on dynamic orchestration and migration expansion according to claim 1, characterized in that, In step S1, the industrial edge network is abstracted into an intelligent industrial network model composed of multiple heterogeneous edge nodes and links. Based on the intelligent industrial network model, industrial application requests are modeled as request chains composed of multiple microservices with data dependencies, including: S101, Based on the network configuration of the industrial edge network and the communication relationships of the servers, a smart industrial network model based on a modular microservice architecture is constructed, and the smart industrial network model is defined as an undirected graph model. ; in, For edge nodes in industrial edge networks The set, ; Each edge node in the intelligent industrial network model comprises an industrial switch and a set of heterogeneous edge servers, with each set of servers containing a different number of cores. , To connect two servers A collection of links, each physical link A pair of corresponding connected server nodes , Indicates two connected servers Maximum bandwidth capacity between This indicates the transmission latency between two connected servers; S102 defines the industrial network service in the intelligent industrial network model as consisting of multiple industrial microservices with data dependencies. Composed of sequential combinations, ∈ ,in A collection of all industrial microservices; S103, based on dynamically changing industrial application requests, will Request chain at any time Relevant traffic is defined as request flow whose arrival rate follows a Poisson distribution. , For a collection of request streams, Representative request chain microservices Sequential logical sets, represent Request chain at any time Actual arrival rate ， represent Request chain at any time The actual performance requirements, among which represent Request chain at any time Response priority represent Request chain at any time Response tolerance latency Then it means Request chain at any time The transmission bandwidth requirements.

3. The industrial edge network microservice optimization method based on dynamic orchestration and migration expansion according to claim 2, characterized in that, In step S1, the microservice instance deployment scheme and request routing scheme in the computation request chain include: S104, Obtain the microservice instance deployment scheme configured under the current industrial application request chain model. With request routing scheme ; in, The deployment scheme for microservice instances at any given time is described as follows: , represent The collection of all microservice instances in the current industrial application request chain model. represent The set of edge node resources occupied by all microservice instances in the current industrial application request chain model; The request routing scheme at any given time is described as follows: ,in, Defined as The set of routing paths for all request flows at any given time. Defined as The set of transition probabilities for all request flows at a given time.

4. The industrial edge network microservice optimization method based on dynamic orchestration and migration expansion according to claim 3, characterized in that, In step S2, the real-time statistical time-varying request stream utilizes a method based on... m / m / c The minimum number of cores satisfying the latency constraint in the multivariate performance analysis model and binary search algorithm analysis results of queue networks includes: S201, according to the time slots arriving in sequence and The request stream data is compared with the changes in the type of request stream and the arrival rate under the dual-timeslot scenario; S202, based on the calculation results of S201, according to The multivariate performance analysis model of the queue network calculates the dwell time of each microservice instance. Use the binary search algorithm to determine the satisfying Minimum number of cores for constraints ,in, for t The request chain that ensures core shrinkage does not cause performance degradation at any given time. The response delay threshold.

5. The industrial edge network microservice optimization method based on dynamic orchestration and migration expansion according to claim 4, characterized in that, Step S201 specifically includes: make The request stream set of the time slot is ,when Request stream of time slots Upon arrival, it is categorized according to the type of request. The existing request stream set in the time slot as well as New types of request streams arriving in time slots ; against The existing set of request streams at any given time ,consider Request flow arrival rate at any given time Changes to deployed microservice instances Load changes at the location, combined with The probability of route transition at time t and Request arrival rate at specific times Calculate the temporary request arrival rate at each instance. ; Based on the calculated temporary request arrival rate and Total request arrival rate at any given time Each microservice instance is divided into a set of load-reducing instances and a set of load-increasing instances.

6. The industrial edge network microservice optimization method based on dynamic orchestration and migration expansion according to claim 5, characterized in that, Step S203 specifically includes: For the load reduction instance set, based on the minimum number of cores Release redundant cores; For the load-increasing instance set, based on the minimum number of cores Determine the remaining number of cores on the node where the microservice instance resides. Does the microservice instance meet resource constraints? If there are sufficient remaining resources, expand the service; otherwise, reduce the number of microservice instances to the minimum number of cores. Record to the collection of instances to be migrated .

7. The industrial edge network microservice optimization method based on dynamic orchestration and migration expansion according to claim 6, characterized in that, Step S3, before calculating the migration cost, also includes: S301, based on the preliminary dynamic scaling adjustment results, based on FFD Algorithm determines new types of request streams The deployment plan was then synchronized to the set of instances to be migrated. .

8. The industrial edge network microservice optimization method based on dynamic orchestration and migration expansion according to claim 7, characterized in that, Step S302 further includes: constructing a migration cost function based on service interruption cost, pull deployment cost, and resource consumption difference cost using a migration expansion algorithm, wherein the migration cost function is expressed as: in, For migration costs, For service interruption costs, Weighting of service interruption costs The current deployment cost for migration nodes, To retrieve deployment cost weights for the current migration nodes, For the difference in cost of resource usage, Weighting of the difference in resource usage costs; Define microservice instances From the source edge node Migrate to target edge node The interruption cost incurred during the period is This indicates the duration of the service interruption. Service interruption cost at any given time Represented as: Among them, binary variables The meaning is Current microservice instances Should we migrate to the target edge node? Place, , To deploy on the target edge node Microservice instances , for The collection of all microservice instances in the system at any given time. for The collection of all microservice instances in the system at time -1; Define pull microservices The resulting cost is , indicating microservices The time required for fetching and deployment determines the deployment cost of the new instance at the target edge node when migrating a microservice instance. Represented as: The current complete migration node pull and deployment cost Represented as: definition r ( )for The difference in resource overhead caused by microservice migration at this time includes the difference in communication overhead between instances and the difference in resource overhead occupied by instances, and is expressed as: in, This represents the difference in communication overhead between instances. Weighting the difference in communication overhead between instances. The difference in resource overhead for the instance. Weight the difference in resource overhead used by instances; Inter-instance communication overhead difference Difference between instance resource consumption and They are represented as follows: in, for t Two connected servers at any time The remaining bandwidth capacity between for t At time -1, two connected servers The remaining bandwidth capacity between for t The source edge node where the microservice instance is located n The number of cores for t -1 The source edge node where the microservice instance is located at time 1 n The number of core components.

9. A method for optimizing industrial edge network microservices based on dynamic orchestration and migration expansion as described in claim 7 or 8, characterized in that, Step S303 includes: Based on a mixed-integer nonlinear programming solver, the set to be migrated is determined according to the migration cost. The optimal migration and expansion scheme for microservice instances to be migrated between the source edge node and the target edge node is formalized as follows: Optimization objective and constraints: in, Indicates that solving makes When the minimum value is reached ( The value, Source edge node n Microservice Examples Core number, Source edge node n Accessing microservice instances Request delivery rate For target edge nodes Microservice Examples Core number, For target edge nodes Accessing microservice instances Request delivery rate For the source edge node n Microservice Examples The core number is and source edge nodes n Accessing microservice instances The request delivery rate Microservice Instance Processing latency, To the target edge node Microservice Examples The core number is and target edge nodes Accessing microservice instances The request delivery rate Microservice Instance Processing latency, For the microservice instance obtained by computation Maximum tolerable latency, for Time source edge node n Accessing microservice instances Request delivery rate The source edge node where the microservice instance resides n The remaining number of cores, For target edge nodes The remaining number of cores; Based on various constraints, and with the objective of minimizing migration cost, a mixed-integer nonlinear programming solver is invoked. Gurobi Solving the optimization objective yields an approximate optimal solution; Update based on the approximate optimal solution Microservice deployment solution at +1 moment ( +1) and request routing scheme ( +1).

10. A microservice optimization system for industrial edge networks based on dynamic orchestration and migration expansion, characterized in that, include: The pre-arrangement module is used to abstract the industrial edge network into an intelligent industrial network model composed of multiple heterogeneous edge nodes and links, and based on the intelligent industrial network model, to model industrial application requests as a request chain composed of multiple microservices with data dependencies in sequence, and to calculate the deployment scheme of microservice instances and the request routing scheme in the request chain. The core dynamic adjustment module is used to statistically analyze time-varying request streams in real time and utilize based on... m / m / c The algorithm analyzes the minimum number of cores required to satisfy latency constraints using a multivariate performance analysis model and a binary search algorithm for queue networks. Based on these results, it dynamically scales and adjusts the core resources of deployed microservice instances. This dynamic scaling adjustment includes: expanding the deployment of the microservice instance on the current node for microservice instances whose single-node resources meet the scaling conditions, based on the minimum number of cores; and adding microservice instances whose single-node scaling is insufficient to accommodate further expansion to a migration set. ; The orchestration optimization module is used to transform the complex multi-node migration decision problem into a mixed-integer nonlinear programming problem based on the initial dynamic scaling adjustment results and migration costs, and then solve it. Based on the solution, it achieves optimal collaborative deployment and request routing optimization of microservice instances among edge nodes; including: Calculate the set to be migrated using the migration cost function The service interruption cost, deployment pull cost, and resource consumption difference cost of migrating each microservice instance to other nodes that meet deployment constraints are used to obtain the migration cost set. ; Based on a mixed-integer nonlinear programming solver, the set to be migrated is determined according to the migration cost. The optimal migration and scaling scheme for microservice instances to be migrated between target nodes to update the microservice deployment plan. ( +1) and request routing scheme ( +1).

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