Resource processing method and device of B2B2C system, electronic equipment and storage medium

Abstract

The invention discloses a resource processing method and device of a B2B2C system, electronic equipment and a storage medium, and can be applied to the technical field of Internet of Things. According to the application, the first application service resource deployed in the B2B2C system is acquired, the processing capability threshold of the first application service resource is calculated, the first application service resource is matched with the first service resource demand to obtain the matching result, and then the scheduling strategy of the first application service resource is executed according to the matching result and the processing capability threshold. And after the current load index of the corresponding first application service resource after the scheduling strategy is executed is obtained, the service resource of the B2B2C system is expanded according to the current load index, so that the service resource in the B2B2C system can be automatically adjusted according to the service demand. And therefore, the utilization rate of the service resources of the B2B2C system can be effectively improved under the condition of ensuring that the service requirements are accurately completed.

Description

Technical Field

[0001] The present application relates to the technical field of Internet of Things, and in particular to a resource processing method and device, electronic device and storage medium of a B2B2C system. Background Art

[0002] In related technologies, the B2B2C system emphasizes the aggregation of computing power and storage capacity, that is, in computing and tasks, hundreds of tasks and thousands of nodes may be run at the same time, which makes the corresponding IoT application data show explosive growth. At present, the cloud computing model corresponding to B2B2C has many problems in processing massive data and responding to real-time business needs. For example, when the system resources are under-estimated, the computing power and storage capacity of the posture will be difficult to meet the growing data needs; when the system resources are over-estimated, the system resource utilization rate will be low, which will lead to waste of system resources.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to provide a resource processing method and device, electronic device and storage medium of a B2B2C system, which can effectively improve the utilization rate of service resources of the B2B2C system.

[0005] To achieve the above purpose, one aspect of an embodiment of the present application provides a resource processing method for a B2B2C system, the method comprising the following steps:

[0006] Acquire a first application service resource that has been deployed in the B2B2C system;

[0007] Calculating a processing capacity threshold of the first application service resource;

[0008] Matching the first application service resource with the first business resource requirement to obtain a matching result;

[0009] executing a scheduling strategy for the first application service resource according to the matching result and the processing capacity threshold;

[0010] Obtaining a current load indicator of the first application service resource corresponding to the execution of the scheduling strategy;

[0011] The service resources of the B2B2C system are expanded according to the current load indicator.

[0012] In some embodiments, the calculating the processing capacity threshold of the first application service resource includes:

[0013] Classifying all the first application service resources to obtain a first application service resource set;

[0014] Clustering the application service resources in the first application service resource set to obtain a cluster center of each cluster;

[0015] A processing capacity threshold of the first application service resource is determined according to the cluster center.

[0016] In some embodiments, classifying all the first application service resources to obtain a first application service resource set includes:

[0017] All the first application service resources are classified by using a Fourier transform algorithm to obtain a first application service resource set.

[0018] In some embodiments, matching the first application service resource with the first business resource requirement includes:

[0019] When there is a running application service resource in the B2B2C system as the first application service resource, obtaining a second business resource demand corresponding to the first application service resource;

[0020] The first application service resource corresponding to the first business resource requirement is determined according to the second business resource requirement and the first business resource requirement.

[0021] In some embodiments, matching the first application service resource with the first business resource requirement further includes:

[0022] When there is no running application service resource in the B2B2C system, obtaining a historical service application resource as a first application service resource, and obtaining a third business resource demand corresponding to the first application service resource;

[0023] The first application service resource corresponding to the first business resource requirement is determined according to the third business resource requirement and the first business resource requirement.

[0024] In some embodiments, executing the scheduling strategy of the first application service resource according to the matching result and the processing capacity threshold includes:

[0025] Acquire at least one scheduling strategy as a target scheduling strategy from a plurality of computing power resource scheduling strategies according to the matching result and the processing capacity threshold, wherein the plurality of computing power resource scheduling strategies include a cost-aware scheduling strategy, a load-aware scheduling strategy, an energy efficiency-aware scheduling strategy, or a preset service level agreement scheduling strategy;

[0026] Execute the target scheduling policy corresponding to the first application service resource.

[0027] In some embodiments, the expanding the service resources of the B2B2C system according to the current load indicator includes:

[0028] Obtaining historical load indicators of the B2B2C system;

[0029] predicting future load status according to the historical load indicator;

[0030] The service resources of the B2B2C system are expanded according to the current load indicator and the future load status.

[0031] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides a resource processing device of a B2B2C system, the device comprising:

[0032] The first module is used to obtain the first application service resource deployed in the B2B2C system;

[0033] A second module is used to calculate the processing capacity threshold of the first application service resource;

[0034] A third module is used to match the first application service resource with the first business resource requirement to obtain a matching result;

[0035] A fourth module, configured to execute a scheduling strategy for the first application service resource according to the matching result and the processing capability threshold;

[0036] A fifth module is used to obtain a current load indicator of the first application service resource corresponding to the execution of the scheduling strategy;

[0037] The sixth module is used to expand the service resources of the B2B2C system according to the current load indicator.

[0038] To achieve the above object, another aspect of an embodiment of the present application provides an electronic device including:

[0039] at least one processor;

[0040] at least one memory for storing at least one program;

[0041] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0042] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0043] The embodiments of the present application include at least the following beneficial effects: The present application provides a resource processing method and device, an electronic device, and a storage medium for a B2B2C system. The scheme obtains a first application service resource deployed in the B2B2C system, calculates a processing capacity threshold of the first application service resource, and matches the first application service resource with a first business resource demand to obtain a matching result. Then, a scheduling strategy for the first application service resource is executed according to the matching result and the processing capacity threshold. Then, after obtaining a current load index of the first application service resource corresponding to the execution of the scheduling strategy, the service resources of the B2B2C system are expanded according to the current load index, so that the service resources in the B2B2C system can be automatically adjusted according to the service demand, thereby effectively improving the utilization rate of the service resources of the B2B2C system while ensuring that the service demand is accurately met. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of a resource processing method of a B2B2C system provided in an embodiment of the present application;

[0045] Figure 2 It is a data processing flow chart of B2B2C;

[0046] Figure 3 It is a schematic diagram of an implementation environment provided by an embodiment of the present application;

[0047] Figure 4 The embodiment of this application provides Figure 1 Flow chart of step S120;

[0048] Figure 5 The embodiment of this application provides Figure 1 The first flow chart of step S130;

[0049] Figure 6 The embodiment of this application provides Figure 1 A second flow chart of step S130;

[0050] Figure 7 The embodiment of this application provides Figure 1 Flow chart of step S150;

[0051] Figure 8 The embodiment of this application provides Figure 1 Flow chart of step S160;

[0052] Fig. 9 is a schematic diagram of the structure of a resource processing device of a B2B2C system provided in an embodiment of the present application;

[0053] Fig.10 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application, they are only examples of devices and methods consistent with some aspects of the embodiments of the present application.

[0055] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0056] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0058] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations:

[0059] B2B2C: is an e-commerce type of online shopping business model. B is the abbreviation of BUSINESS and C is the abbreviation of CUSTOMER. The first B refers to the supplier of goods or services, the second B refers to the enterprise engaged in e-commerce, and C refers to the consumer. Figure 2As shown, consumers can make demands to enterprises engaged in e-commerce, and enterprises engaged in e-commerce can make production demands of corresponding products to manufacturers based on the demands. After completing the production of products, manufacturers will provide qualified products to enterprises engaged in e-commerce. Enterprises engaged in e-commerce will determine high-quality products from these qualified products and provide these high-quality products to consumers.

[0060] B2B2C system: a system that provides customers with 2C traffic operation capabilities in the B2B2C model, and has traffic operation service capabilities such as 2C rate customization, precise traffic control, merchant management, online payment, distribution settlement, etc.

[0061] 5G (5th Generation Mobile Communication Technology) is a new generation of broadband mobile communication technology with the characteristics of high speed, low latency and large connection. It is the network infrastructure for realizing the interconnection between man, machine and things.

[0062] The Internet of Things (IoT) refers to the use of various information sensors, radio frequency identification technology, global positioning system, infrared sensors, laser scanners and other devices and technologies to collect any objects or processes that need to be monitored, connected, and interacted in real time, and to collect various required information such as sound, light, heat, electricity, mechanics, chemistry, biology, and location. Through various possible network accesses, it realizes ubiquitous connection between objects and objects and between objects and people, and realizes intelligent perception, identification and management of objects and processes. The Internet of Things is an information carrier based on the Internet, traditional telecommunications networks, etc., which allows all ordinary physical objects that can be independently addressed to form an interconnected network.

[0063] The Slurm job scheduling tool is a free and open source job scheduler for Linux and Unix-like kernels, used by many of the world's supercomputers and computer clusters. It provides three key functions: First, it allocates exclusive and / or non-exclusive access to resources (computer nodes) to users for a period of time so that they can execute work. Second, it provides a framework for launching, executing, and monitoring work (typically parallel jobs, such as MPI) on a set of allocated nodes. Finally, it arbitrates resource contention by managing a queue of pending jobs.

[0064] Kubernetes is an open source container orchestration engine from Google that supports automated deployment, large-scale scalability, and containerized application management. When deploying an application in a production environment, you usually need to deploy multiple instances of the application to load balance application requests. In Kubernetes, you can create multiple containers, each of which runs an application instance, and then use the built-in load balancing strategy to manage, discover, and access this group of application instances, without requiring operations and maintenance personnel to perform complex manual configuration and processing.

[0065] SLA (Service-Level Agreement) is a contract between a network service provider and a customer, which defines terms such as service type, service quality, and customer payment.

[0066] In related technologies, the development of new network technologies such as 5G and the Internet of Things has brought about the rapid development of the digital economy and the explosive increase in digital applications. Along with this comes the demand for enterprises to fine-tune B2C traffic operations, which requires operators to be able to provide real-time online control of traffic. Compared with quasi-real-time and offline control requirements, real-time online control requires higher resource occupancy and higher costs. Unlike the general service resource management platform that distributes resources to multiple tenants through virtual cloud technology, the B2B2C system emphasizes the aggregation of computing power and storage capabilities, that is, hundreds of tasks and thousands of nodes may be running simultaneously in computing and tasks. Through the B2B2C system resource task scheduling device based on a large model, the executed tasks can be optimally matched with the available resources, thereby minimizing the length of time the tasks wait in the queue, maximizing the amount of task parallelism, and obtaining the optimal resource utilization.

[0067] Currently, there are two mainstream service resource scheduling systems: Slurm and Kubernetes. Slurm is mainly used for task scheduling in HPC scenarios and has been widely adopted by supercomputers (including Tianhe) and computer clusters around the world; Kubernetes, as a container orchestration platform, is used for scheduling and automatic deployment, management, and expansion of containerized applications. Currently, Kubernetes and the broader container ecosystem are gradually maturing and are forming a general computing platform and ecosystem. These scheduling systems cannot adjust application resources according to application business needs. When system resources are under-estimated, the computing power and storage capacity of the posture will be difficult to meet the growing data needs. When system resources are over-estimated, the system resource utilization rate will be low, resulting in a waste of system resources, and it will be impossible to improve the utilization of B2B2C system service resources while meeting the needs.

[0068] In view of this, embodiments of the present application provide a resource processing method and device, an electronic device, and a storage medium for a B2B2C system. The present application obtains a first application service resource deployed in the B2B2C system, calculates a processing capacity threshold of the first application service resource, and matches the first application service resource with a first business resource demand to obtain a matching result. Then, a scheduling strategy for the first application service resource is executed according to the matching result and the processing capacity threshold. Then, after obtaining a current load indicator of the first application service resource corresponding to the execution of the scheduling strategy, the service resources of the B2B2C system are expanded according to the current load indicator, so that the service resources in the B2B2C system can be automatically adjusted according to the service demand, thereby effectively improving the utilization rate of the service resources of the B2B2C system while ensuring that the service demand is accurately met.

[0069] like Figure 3 As shown, the present application embodiment provides a schematic diagram of an implementation environment. Figure 3 , the implementation environment includes at least one terminal 310 and a server 320. The terminal 310 and the server 320 can be connected to the network wirelessly or wired to complete data transmission and exchange. It can be understood that the resource processing method of the B2B2C system provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server, and can also be software running in the terminal or the server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The cloud server, the server can also be a node server in the blockchain network; the software can be an application that implements the resource processing method of the B2B2C system, etc., but is not limited to the above forms.

[0070] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0071] Figure 1 is an optional flowchart of the resource processing method of the B2B2C system provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0072] Step S110, obtaining a first application service resource deployed in the B2B2C system;

[0073] Step S120: Calculate the processing capacity threshold of the first application service resource;

[0074] Step S130: Match the first application service resource with the first business resource requirement to obtain a matching result;

[0075] Step S140: executing the scheduling strategy of the first application service resource according to the matching result and the processing capacity threshold;

[0076] Step S150: obtaining a current load indicator of the first application service resource corresponding to the execution of the scheduling strategy;

[0077] Step S160: Expand the service resources of the B2B2C system according to the current load index.

[0078] In the embodiments of the present application, Figure 4 As shown, the process of calculating the processing capacity threshold of the first application service resource includes but is not limited to the following steps:

[0079] Step S410: Classify all first application service resources to obtain a first application service resource set;

[0080] Step S420: clustering the application service resources in the first application service resource set to obtain a cluster center of each cluster;

[0081] Step S430: Determine the processing capacity threshold of the first application service resource according to the cluster center.

[0082] It is understandable that the first application service resources include but are not limited to the number of CPU cores, memory capacity, storage capacity, video memory capacity, bandwidth, etc. Since the morphological characteristics of application service resource data are various, this embodiment can classify all first application service resources through the Fourier transform algorithm to obtain a first application service resource set. Specifically, the first application service resources are divided into two categories with obvious periodic morphology and without obvious periodic morphology by the Fourier transform method, and then the clustering method based on structural characteristics is used in each major category to perform smaller subdivision. Among them, the discrete Fourier transform of formula (1) can be used to obtain the frequency amplitude spectrum:

[0083]

[0084] In the formula, x(n) represents the nth first application service resource, and N represents the total number of first application service resources.

[0085] Then find the maximum value of the amplitude spectrum |F| max , mean |F| mean and standard deviation |F| std Among them, these three values ​​satisfy the constraints of formula (2):

[0086] |F| max >|F| mean +c*|F| std Formula (2);

[0087] Where c is a setting coefficient, when |F| max If the corresponding period is equal to the set value, the timing has an obvious periodic shape.

[0088] After the classification is completed, the application service resources in each set can be clustered. Specifically, the application service resources of n service nodes can be divided into k clusters and processed using the K-means method:

[0089] Step 1: Randomly select k samples from the data set as the centers of k clusters;

[0090] Step 2: Calculate the distance between each sample and the centers of k clusters, and assign these samples to the cluster with the closest distance to the center;

[0091] Step 3: Recalculate the centers of each of the k clusters based on the clustering results;

[0092] Step 4: Repeat steps 2 and 3 until convergence;

[0093] Step 5: Output the center of each category and the category label of each sample.

[0094] In this embodiment, since a large amount of business data is processed on the B2B2C system and a large amount of performance data is generated, the system operation status can be monitored and controlled. For the large-scale data with different characteristics of the B2B2C platform, the data features obtained by this embodiment based on the structural feature clustering method have multiple time series structures that represent periodicity, trend, non-Gaussianity, nonlinearity, etc. According to the characteristics of the collected data, clustering is performed using a hierarchical clustering framework to reduce the complexity of clustering. In the application process, this embodiment can perform big data intelligent analysis of service operation effects and performance trends based on the full business data model, and strengthen self-learning, and continuously iterate and optimize the performance indicator threshold strategy of service operation online to find the performance indicator threshold (processing capacity threshold) of service operation.

[0095] In the embodiments of the present application, Figure 5 As shown, the process of matching the first application service resource with the first business resource requirement includes but is not limited to the following steps:

[0096] Step S510: When there is a running application service resource in the B2B2C system as the first application service resource, obtain a second business resource demand corresponding to the first application service resource;

[0097] Step S520: Determine a first application service resource corresponding to the first business resource requirement according to the second business resource requirement and the first business resource requirement.

[0098] It can be understood that this embodiment can determine the application service resources corresponding to the first business resource demand through the business resource demand corresponding to the application service resources already running on the server resources, that is, determine the application service resources by the amount of data that can be processed, thereby improving the matching accuracy of the application resources.

[0099] In the embodiment of the present application, when there is no similar service resource running in the B2B2C system, such as Figure 6 As shown, the matching process includes but is not limited to the following steps:

[0100] Step S610: Acquire a historical service application resource as a first application service resource;

[0101] Step S620: Obtain the third business resource requirement corresponding to the first application service resource;

[0102] Step S630: Determine a first application service resource corresponding to the first business resource requirement according to the third business resource requirement and the first business resource requirement.

[0103] Among them, historical service application resources are resources that have been deployed in the B2B2C system but are not currently running. These resources may or may not have been run.

[0104] Specifically, in this embodiment, the historical service application resources can be obtained by similarity. The vector similarity between two application service resources can be calculated by Formula 3:

[0105]

[0106] In the formula, represents any two application service resources, and represents the vector similarity between any two application service resources.

[0107] After obtaining the vector similarity, the historical application service resources obtained through demand screening can be further screened to obtain service resources that are closer to the current business resource requirements.

[0108] In the embodiments of the present application, Figure 7 As shown, the process of executing the scheduling strategy of the first application service resource according to the matching result and the processing capacity threshold includes but is not limited to the following steps:

[0109] Step S710: acquiring at least one scheduling strategy from multiple computing power resource scheduling strategies as a target scheduling strategy according to the matching result and the processing capacity threshold, wherein the multiple computing power resource scheduling strategies include a cost-aware scheduling strategy, a load-aware scheduling strategy, an energy efficiency-aware scheduling strategy, or a preset service level agreement scheduling strategy;

[0110] Step S720: Execute the target scheduling strategy corresponding to the first application service resource.

[0111] It is understandable that different resource providers have different pricing for computing resources such as computing resources, network resources, and memory resources. Different business scenarios have great differences in computing, network, memory, storage, etc. Therefore, the cost-aware scheduling strategy takes the optimal deployment cost as the optimization goal, can complete the pricing of network resources, and uses the computing network resource pool cost pricing model to design a comprehensive cost evaluation B2B2C resource scheduling algorithm. The load-aware scheduling strategy takes the optimal load as the optimization goal. By studying the definition and calculation method of the computing network resource pool load, a comprehensive load evaluation computing network resource scheduling algorithm is designed. The energy efficiency-aware scheduling strategy takes the minimum energy cost required for the business as the optimization goal. By investigating the energy efficiency of the resource pool and studying the energy efficiency model of the resource scheduling scheme, a comprehensive energy efficiency evaluation computing network resource scheduling algorithm is designed. The scheduling engine schedules the deployed container set to run on the physical machine according to the energy efficiency-aware scheduling strategy, so that the container can be configured together with the resources required by the program, and the energy consumption of the data center can be minimized while meeting the resource requirements; the preset service level agreement (SLA) refers to the mutually recognized agreement or contract reached between the enterprise providing the service and the customer on the quality and level of the service, which is the cornerstone of solving these problems in Web services. SLA helps to divide responsibilities and risks between network services, making network services more orderly. The preset service level agreement scheduling strategy can study the key characteristic indicators of the network SLA of the network resource scheduling solution, perceive and learn the relevant SLA calculation model, and then design a scheduling algorithm for network resources based on the comprehensive network SLA evaluation.

[0112] Specifically, after obtaining the matching result and the processing capacity threshold, this embodiment can combine a variety of different resource scheduling strategies through a multi-objective optimization method, as shown in Formula 4:

[0113] rss i =Function(server,service,request) Formula 4;

[0114] In the formula, rss i It represents a low-level resource scheduling strategy, which can be represented by a function; server represents cluster parameter information; service represents microservice request information, and request represents other request parameters.

[0115] Specifically, when deploying under the i-th resource scheduling strategy, the system first inputs the microservice information and cluster information. Then, it calculates the optimal cluster using the i-th strategy, such as the cluster with the best computing cost and the cluster strategy with the best SLA, and finally outputs the deployment result.

[0116] When processing user requests in the request message cache queue, the algorithm type used when the microservice was deployed is used to schedule these requests. Specifically, the application request information and microservice historical data need to be input first, and the application request model is built based on the microservice request information; then the microservice deployment information of the application request is found, and the same type of application request scheduling algorithm is executed based on the perception algorithm used when the microservice was deployed.

[0117] The process of the application request scheduling algorithm using the same type of application awareness is similar to the deployment algorithm. The difference is that its output is the probability distribution of cluster scheduling and the probability selection of the cluster requesting scheduling.

[0118] This embodiment determines the target scheduling strategy of application service resources corresponding to the current business resource demand and executes the target scheduling strategy to ensure that the current business demand is effectively completed and improve resource utilization.

[0119] In the embodiments of the present application, Figure 8 As shown, the process of expanding the service resources of the B2B2C system according to the current load index includes but is not limited to the following steps:

[0120] Step S810: Obtain historical load indicators of the B2B2C system;

[0121] Step S820: predicting future load status based on historical load indicators;

[0122] Step S830: Expand the service resources of the B2B2C system according to the current load indicator and the future load status.

[0123] It is understandable that historical load indicators include but are not limited to memory usage, CPU usage, system disk utilization, system network bandwidth utilization, etc. The weights of these load indicators are confirmed by the entropy of system performance, and the corresponding X t , define the template vector with length m as shown in Formula 5:

[0124] X m (i) = {x i ,x i+1 ,…,x i+m-1},i=1,2,…,N-m+1 Formula 5;

[0125] The distance between two template vectors is defined by formula 6:

[0126] d[X m (i),X m (j)]=max k＝0,…,m-1 ||x i+k -x j+k ||,i≠j Formula 6;

[0127] Then the entropy weight of the load indicator weight is shown in Formula 7:

[0128]

[0129] In the formula, B represents the number of template vector pairs with a length of m whose distance is less than a certain threshold r, and A represents the number of template vector pairs with a length of m+1 whose distance is less than the threshold.

[0130] Define the comprehensive load of the B2B2C system as L, obtain the CPU usage from the network resource status as C, the memory usage as M, the disk usage as D, and the network bandwidth usage as B. The corresponding entropy weights are w c ,w m ,w d ,w b ,…, where c,m,d,b∈[0,1], the comprehensive load of the system is shown in Formula 8:

[0131] L=w c C+w m M+w d D+w b B Formula 8;

[0132] This embodiment determines the comprehensive load situation by monitoring the resource-level indicators of the entire B2B2C system in real time.

[0133] At the same time, this embodiment will also monitor the collected historical load indicators of microservices, and predict the microservice load status at the next moment through these historical monitoring data and prediction models. In addition, after the resource load prediction value is output, it is used to guide the subsequent resource application work corresponding to the expansion of the B2B2C service. As the B2B2C system runs, the time-series load indicators will also accumulate. Based on the time-series load indicator data of the system operation, the application of machine resources based on the load in the future period can be predicted to have the elastic scaling of the system. The gray prediction model is used to predict the B2B2C application service load. The prediction result is a time-series system load-related indicator value, which is passed to the expansion planning module as a resource indicator to guide the resource application work corresponding to the expansion of the B2B2C service.

[0134] In the B2B2C network dynamic scaling function, you can use the step-by-step scaling method to operate. The maximum value of the future time series is predicted by predicting the application output load. If the maximum value exceeds the application service resource processing capacity threshold, the scaling plan calculates the number of replicas * expansion step, and selects the larger value between the number of replicas * expansion step and the maximum number of replicas, and executes the expansion command to increase resources, otherwise it will decrease.

[0135] In summary, the method provided in the embodiment of the present application is based on large-scale data with different characteristics of the B2B2C system. For data with different structures, hierarchical clustering is performed according to the data characteristics, and the resource threshold for the IT system to process different data is given to ensure the intelligent operation and maintenance of the system; and in view of the problem of elastic and flexible scheduling of multi-level resources over a large range of the entire domain under the differentiated business needs of B2B2C users, a B2B2C internal resource scheduling strategy and network resource collaborative scheduling are designed to achieve the best matching of tasks automatically executed by resources with available resources, thereby effectively improving resource utilization.

[0136] Reference Fig. 9 , an embodiment of the present application provides a resource processing device of a B2B2C system, the device comprising:

[0137] The first module 910 is used to obtain a first application service resource deployed in the B2B2C system;

[0138] The second module 920 is used to calculate the processing capacity threshold of the first application service resource;

[0139] The third module 930 is used to match the first application service resource with the first business resource requirement to obtain a matching result;

[0140] The fourth module 940 is used to execute the scheduling strategy of the first application service resource according to the matching result and the processing capacity threshold;

[0141] The fifth module 950 is used to obtain the current load index of the first application service resource corresponding to the execution of the scheduling strategy;

[0142] The sixth module 960 is used to expand the service resources of the B2B2C system according to the current load index.

[0143] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0144] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the resource processing method of the B2B2C system when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0145] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0146] See also Fig.10 , Fig.10 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0147] The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0148] The memory 1020 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1020 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1020, and the processor 1010 calls and executes the resource processing method of the B2B2C system in the embodiment of this application;

[0149] Input / output interface 1030, used to implement information input and output;

[0150] The communication interface 1040 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0151] Bus 1050 , which transmits information between various components of the device (e.g., processor 1010 , memory 1020 , input / output interface 1030 , and communication interface 1040 );

[0152] The processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 are connected to each other in communication within the device via a bus 1050 .

[0153] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the resource processing method of the B2B2C system is implemented.

[0154] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0155] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0156] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0157] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0158] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0159] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0160] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0161] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0162] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0163] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0165] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0166] The preferred embodiments of the present application are described above with reference to the accompanying drawings, but the scope of the rights of the present application is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present application should be within the scope of the rights of the present application.

Claims

1. A resource processing method for a B2B2C system, characterized in that: The method comprises the following steps: Acquire a first application service resource that has been deployed in the B2B2C system; Calculating a processing capacity threshold of the first application service resource; Matching the first application service resource with the first business resource requirement to obtain a matching result; executing a scheduling strategy for the first application service resource according to the matching result and the processing capacity threshold; Obtaining a current load indicator of the first application service resource corresponding to the execution of the scheduling strategy; The service resources of the B2B2C system are expanded according to the current load indicator.

2. The method according to claim 1, characterized in that: The calculating the processing capacity threshold of the first application service resource includes: Classifying all the first application service resources to obtain a first application service resource set; Clustering the application service resources in the first application service resource set to obtain a cluster center of each cluster; A processing capacity threshold of the first application service resource is determined according to the cluster center.

3. The method according to claim 2, characterized in that The classifying all the first application service resources to obtain a first application service resource set includes: All the first application service resources are classified by using a Fourier transform algorithm to obtain a first application service resource set.

4. The method according to claim 1, characterized in that: The matching of the first application service resource with the first business resource requirement includes: When there is a running application service resource in the B2B2C system as the first application service resource, obtaining a second business resource demand corresponding to the first application service resource; The first application service resource corresponding to the first business resource requirement is determined according to the second business resource requirement and the first business resource requirement.

5. The method according to claim 4, characterized in that The matching of the first application service resource with the first business resource requirement further includes: When there is no running application service resource in the B2B2C system, obtaining a historical service application resource as a first application service resource, and obtaining a third business resource demand corresponding to the first application service resource; The first application service resource corresponding to the first business resource requirement is determined according to the third business resource requirement and the first business resource requirement.

6. The method according to claim 1, characterized in that The executing the scheduling strategy of the first application service resource according to the matching result and the processing capability threshold comprises: Acquire at least one scheduling strategy as a target scheduling strategy from a plurality of computing power resource scheduling strategies according to the matching result and the processing capacity threshold, wherein the plurality of computing power resource scheduling strategies include a cost-aware scheduling strategy, a load-aware scheduling strategy, an energy efficiency-aware scheduling strategy, or a preset service level agreement scheduling strategy; Execute the target scheduling policy corresponding to the first application service resource.

7. The method according to claim 1, characterized in that The step of expanding the service resources of the B2B2C system according to the current load indicator includes: Obtaining historical load indicators of the B2B2C system; predicting future load status according to the historical load indicator; The service resources of the B2B2C system are expanded according to the current load indicator and the future load status.

8. A resource processing device for a B2B2C system, characterized in that: The device comprises: The first module is used to obtain the first application service resource deployed in the B2B2C system; A second module is used to calculate the processing capacity threshold of the first application service resource; A third module is used to match the first application service resource with the first business resource requirement to obtain a matching result; A fourth module, configured to execute a scheduling strategy for the first application service resource according to the matching result and the processing capability threshold; A fifth module is used to obtain a current load indicator of the first application service resource corresponding to the execution of the scheduling strategy; The sixth module is used to expand the service resources of the B2B2C system according to the current load indicator.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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