Simulation scheduling method, device and equipment for cluster resources, medium and product

Abstract

The invention discloses a simulation scheduling method, device and equipment for cluster resources, a medium and a product. The method comprises the steps that a node resource configuration table of a target cluster group is acquired, the target cluster group is composed of at least one target cluster, and each target cluster is composed of at least one node and at least one working load; injecting node scheduling configuration matched with each node into a pre-configured simulator instance, and executing simulation scheduling operation on the target cluster group according to the simulator instance and each node scheduling configuration to obtain a scheduling result file; and generating a simulation scheduling report matched with the target cluster group according to the scheduling result file. Through the technical scheme of the invention, the simulation scheduling of the cluster resources can be realized, the acquisition efficiency and the result accuracy of the simulation scheduling result of the cluster resources are improved, and the efficiency of the scheduling work of the cluster resources and the service operation efficiency in the cluster environment are further improved.

Description

Technical Field

[0001] This invention relates to the field of distributed systems, specifically to the application of distributed systems in the field of financial technology, and particularly to a method, apparatus, device, medium, and product for simulating the scheduling of cluster resources. Background Technology

[0002] In enterprise production environments, multi-cluster deployments have gradually become the mainstream architectural model due to considerations such as high availability, disaster recovery capabilities, and compliance. However, this multi-cluster environment brings a series of new challenges to resource scheduling. Currently, there are solutions based on federated scheduling in the market, but these solutions have many drawbacks. First, these solutions lack a global view of resource utilization: In a multi-cluster environment, the resource usage of each cluster is interconnected, requiring a holistic understanding of resource distribution and utilization efficiency. However, existing federated scheduling solutions cannot comprehensively and in real-time understand the resource utilization of all clusters, failing to achieve optimal resource allocation. Second, existing solutions cannot pre-evaluate scheduling results: Before actually scheduling resources, it is impossible to know in advance the impact of the scheduling strategy on each cluster and the overall business, increasing the risk of business operations. Third, strategy configuration is complex and debugging is difficult: Since scheduling strategies involve numerous parameters and rules, once a configuration error occurs, the debugging process is extremely cumbersome, consuming a lot of time and energy, which seriously hinders enterprises from quickly responding to business needs and ensuring stable system operation.

[0003] Against this backdrop, traditional scheduling methods are increasingly unable to meet the complex needs of multi-cluster resource scheduling, highlighting the growing importance of simulated scheduling. Simulated scheduling allows for the testing and evaluation of different scheduling strategies without actually executing scheduling operations. However, existing scheduling schemes rarely involve simulated scheduling, failing to effectively address the complex and ever-changing resource scheduling situations in multi-cluster environments.

[0004] In summary, existing technologies have limitations in simulating and scheduling cluster resources, and the efficiency and accuracy of obtaining cluster resource scheduling results are poor. Consequently, both the efficiency of cluster resource scheduling and the efficiency of business operations in a cluster environment are poor. Summary of the Invention

[0005] This invention provides a method, apparatus, device, medium, and product for simulating the scheduling of cluster resources. It can solve the problems of existing methods for simulating the scheduling of cluster resources, such as the inability to achieve simulated scheduling of cluster resources, poor efficiency and accuracy in obtaining scheduling results, and consequently, poor efficiency in both scheduling of cluster resources and business operation efficiency in a cluster environment.

[0006] In a first aspect, embodiments of the present invention provide a method for simulating the scheduling of cluster resources, the method comprising:

[0007] Obtain the node resource configuration table of the target cluster group, wherein the target cluster group consists of at least one target cluster, and the target cluster consists of at least one node and at least one workload;

[0008] Inject node scheduling configurations that match each node into a pre-configured simulator instance, and perform simulated scheduling operations on the target cluster group according to the simulator instance and the scheduling configurations of each node to obtain a scheduling result file;

[0009] A simulated scheduling report matching the target cluster group is generated based on the scheduling result file.

[0010] Secondly, embodiments of the present invention provide a simulated scheduling device for cluster resources, the device comprising:

[0011] The data acquisition module is used to acquire the node resource configuration table of the target cluster group, wherein the target cluster group consists of at least one target cluster, and the target cluster consists of at least one node and at least one workload.

[0012] The simulation scheduling module is used to inject node scheduling configurations that match each node into a pre-configured simulator instance, and to perform simulated scheduling operations on the target cluster group according to the simulator instance and the scheduling configurations of each node, so as to obtain a scheduling result file.

[0013] The report generation module is used to generate a simulated scheduling report that matches the target cluster group based on the scheduling result file.

[0014] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform a cluster resource simulation scheduling method according to any embodiment of the present invention.

[0018] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute a simulated scheduling method for cluster resources as described in any embodiment of the present invention.

[0019] Fifthly, embodiments of the present invention provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements a cluster resource simulation scheduling method as described in any embodiment of the present invention.

[0020] The technical solution of this invention obtains the node resource configuration table of the target cluster group, then injects node scheduling configurations matching each node into a pre-configured simulator instance, and performs simulated scheduling operations on the target cluster group according to the simulator instance and the node scheduling configurations to obtain a scheduling result file. Finally, a simulated scheduling report matching the target cluster group is generated based on the scheduling result file. This solves the problem that the prior art cannot achieve simulated scheduling of cluster resources, and the efficiency and accuracy of obtaining cluster resource scheduling results are poor, resulting in poor efficiency of cluster resource scheduling and business operation efficiency in the cluster environment. The invention achieves simulated scheduling of cluster resources, improves the efficiency and accuracy of obtaining simulated scheduling results of cluster resources, and thus improves the efficiency of cluster resource scheduling and business operation efficiency in the cluster environment.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a simulated scheduling method for cluster resources provided in Embodiment 1 of the present invention;

[0024] Figure 2 This is a flowchart of a simulated scheduling method for cluster resources according to Embodiment 2 of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of a cluster resource simulation scheduling device provided according to Embodiment 3 of the present invention;

[0026] Figure 4 This is a schematic diagram of the structure of an electronic device that implements a simulated scheduling method for cluster resources according to an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, any variations of the terms "comprising" and "having" are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 1 This is a flowchart of a cluster resource simulation scheduling method provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of simulating the scheduling of cluster resources. The method can be executed by a cluster resource simulation scheduling device, which can be implemented in hardware and / or software. The cluster resource simulation scheduling device can be configured in a terminal or server with cluster resource simulation scheduling function.

[0031] like Figure 1 As shown, the method includes:

[0032] S110. Obtain the node resource configuration table of the target cluster group, wherein the target cluster group consists of at least one target cluster, and the target cluster consists of at least one node and at least one workload.

[0033] The node resource configuration table includes: cluster information of each target cluster in the target cluster group, node information of each node that makes up the target cluster, and load attributes and representative workload data that match each workload.

[0034] The target cluster group consists of at least one target cluster, which is a cluster environment built based on a container cluster management system and consists of at least one node and at least one workload. Furthermore, the container cluster management system is an open-source container cluster management system used to automatically deploy, scale, and manage containerized applications. Furthermore, the node is a management node responsible for cluster management and a business node running user services, and the workload is the smallest scheduling unit in the target cluster.

[0035] Optionally, before obtaining the node resource configuration table of the target cluster group, the method further includes: using a pre-configured cluster management tool to traverse each target cluster in the target cluster group to obtain a node list matching each target cluster and workload data matching the target cluster group. The node list includes at least one node information, which includes: the node's CPU information, memory, tag, allocable resources, topology, and exclusion attributes; performing field trimming, data anonymization, and format standardization on each node information and workload data to obtain standard node information matching each node and representative workload data matching each target cluster; updating the node list of each target cluster based on the standard node information; and assembling the updated node list and workload data to obtain the node resource configuration table of the target cluster group.

[0036] Specifically, the pre-configured cluster management tool can be a tool that implements multi-cluster connection authentication based on a cluster configuration file; further, the node's exclusion attribute is specifically the node taint of the node, used to restrict the scheduling of the smallest scheduling unit on the node.

[0037] S120. Inject node scheduling configurations that match each node into the pre-configured simulator instance, and perform simulated scheduling operations on the target cluster group according to the simulator instance and the scheduling configurations of each node to obtain a scheduling result file.

[0038] The simulator instance is a dedicated service instance used to reproduce the container cluster scheduling process. It can be deployed in a local environment or a containerized environment to simulate the scheduling decision process in an environment completely isolated from the production cluster, thus avoiding interference with online services. Furthermore, the node scheduling configuration is a configuration file containing scheduling policy rules, plugin combinations, and weight settings. It is used to define the decision logic when the simulator instance executes scheduling, including key scheduling parameters such as resource priority judgment, node affinity matching rules, and taint tolerance policies.

[0039] The process involves performing simulated scheduling operations on the target cluster group based on the simulator instance and the scheduling configuration of each node to obtain a scheduling result file. This includes: initializing the pre-configured simulator instance, and after confirming that the network transmission interface of the simulator instance is available, sequentially uploading the pre-set simulated node dataset, simulated workload file, and node scheduling configuration of each node to the simulator instance through the network transmission interface; triggering a simulated scheduling operation through the simulator instance to obtain a scheduling result file matching the simulated scheduling operation; wherein, the scheduling result file includes the scheduling status, scheduling remarks, and load score of each workload, the scheduling status includes: scheduling successful or scheduling failed, and the scheduling remarks include: scheduling node address or scheduling failure reason.

[0040] The simulated node dataset consists of preprocessed standard node information, including key scheduling fields such as CPU specifications, memory capacity, tag attributes, and allocable resource quotas for each node. The simulated workload file stores representative minimum scheduling unit data that matches the workload characteristics of the target cluster group, including information such as resource request volume, affinity requirements, and quality of service level. The node scheduling configuration for each node is used to define the scheduling policy details for different nodes or node groups, such as the resource allocation weight of specific nodes and the types of workloads that are prohibited from scheduling.

[0041] Specifically, based on the simulator instance and the scheduling configuration of each node, a simulated scheduling operation is performed on the target cluster group to obtain a scheduling result file. This includes the following steps: First, the pre-configured simulator instance is initialized to ensure it is available. Then, a network connection testing tool is used to verify whether the simulator instance's network transmission interface is responding normally, preventing connection anomalies during subsequent data uploads or scheduling trigger operations. After confirming the network transmission interface is available, the pre-set simulated node dataset, simulated workload file, and node scheduling configuration of each node are uploaded to the simulator instance sequentially through this interface in a preset order. Second, after all data uploads are completed, the scheduling trigger interface in the simulator instance's network transmission interface is called. The simulator instance then performs a simulated scheduling operation on each smallest scheduling unit in the simulated workload file according to the strategy logic defined in the node scheduling configuration. This involves matching the workload's scheduling requirements based on the resource status and attribute characteristics of each node in the simulated node dataset, calculating the adaptation score for each node, and selecting the optimal node to allocate the workload. After the simulated scheduling process for all workloads is completed, the simulator instance generates and returns a scheduling result file matching this simulated scheduling operation.

[0042] Furthermore, if the workload scheduling is successful, the scheduling remarks of the workload are used to record the allocated scheduling node address; if the scheduling fails, the scheduling remarks of the workload are used to record the reason for the failure (such as insufficient node resources, failure to meet affinity requirements, or the existence of intolerable node taints). Furthermore, the load score is a detailed adaptation score calculated by the simulator instance for each matching node of the workload according to the scheduling strategy during the scheduling process, including the sub-scores of each scheduling plugin and the total score.

[0043] Those skilled in the art should understand that the method of obtaining the scheduling status, scheduling notes and load scores of each workload in the target cluster group through simulator instances is a mature existing technology. This embodiment only introduces its steps, and does not elaborate on its specific calculation methods and implementation steps.

[0044] S130. Generate a simulated scheduling report that matches the target cluster group based on the scheduling result file.

[0045] Optionally, after generating a simulated scheduling report matching the target cluster group based on the scheduling result file, the method further includes: updating the scheduling configuration of each node in response to the user's editing operation; returning to the operation of obtaining the node resource configuration table of the target cluster group based on the updated scheduling configuration of each node, until an updated scheduling report matching the updated scheduling configuration of each node is obtained; processing the simulated scheduling report and the updated scheduling report through a pre-configured quantitative indicator system comparison model to obtain comparison difference data; generating a comparison result based on the comparison difference data and sending it to the user, so that the user can update the scheduling configuration of each node based on the comparison result.

[0046] The quantitative indicator system comparison model is used to calculate the quantitative indicators of scheduling effect corresponding to different scheduling configurations in batches based on the simulation scheduling report and according to the preset indicator calculation rules, and outputs comparative difference data including the values, changes, and trends of the indicators before and after. Further, the quantitative indicators of scheduling effect include: scheduling success rate change rate, failure type proportion difference, workload scheduling achievement rate, and node load balancing degree. Specifically, the scheduling success rate change rate refers to the change in the proportion of successfully scheduled workloads to the total workload in the simulated scheduling of the target cluster group before and after the node scheduling configuration update; the failure type proportion difference refers to the change in the proportion of failed workloads corresponding to various scheduling failure reasons to the total failed workload in the simulated scheduling before and after the node scheduling configuration update; the workload scheduling achievement rate refers to the proportion of workloads that meet the preset scheduling target to the total workload in the simulated scheduling; and the node load balancing degree refers to the degree of balance in the workload or resource consumption carried by each node in the target cluster group after the simulated scheduling.

[0047] The technical solution of this invention obtains the node resource configuration table of the target cluster group, then injects the node scheduling configuration matching each node into a pre-configured simulator instance, and performs simulated scheduling operations on the target cluster group according to the simulator instance and the node scheduling configuration to obtain a scheduling result file. Finally, a simulated scheduling report matching the target cluster group is generated based on the scheduling result file. This realizes the simulated scheduling of cluster resources, improves the efficiency and accuracy of obtaining the simulated scheduling results of cluster resources, and thus improves the efficiency of cluster resource scheduling and the efficiency of business operation in the cluster environment.

[0048] Example 2

[0049] Figure 2 This is a flowchart of a simulated scheduling method for cluster resources provided in Embodiment 2 of the present invention. This embodiment is a refinement based on the above embodiment. Specifically, this embodiment refines the method for generating a simulated scheduling report matching the target cluster group based on the scheduling result file.

[0050] like Figure 2 As shown, the method includes:

[0051] S210. Obtain the node resource configuration table of the target cluster group.

[0052] The target cluster group consists of at least one target cluster, and the target cluster consists of at least one node and at least one workload.

[0053] S220. Inject node scheduling configurations that match each node into the pre-configured simulator instance, and perform simulated scheduling operations on the target cluster group according to the simulator instance and the scheduling configurations of each node to obtain a scheduling result file.

[0054] S230. Parse the scheduling result file to obtain the scheduling status, scheduling notes and load score of each workload.

[0055] S240. Based on the scheduling status, classify each workload to obtain a scheduling success sequence and a scheduling failure sequence.

[0056] For the successfully scheduled sequence, execute S250;

[0057] For the scheduling failure sequence, execute S260.

[0058] S250. Based on the load attributes of each workload in the successful scheduling sequence, calculate the distribution of each workload on each target node, obtain the successful sequence analysis results matching each target node, and execute S270.

[0059] The load attributes include the workload's resource request volume, service type, priority, and business line.

[0060] Specifically, during the statistical distribution, the nodes are first grouped by their unique identifier, and then further subdivided within each group according to load attributes. For example, the number of core-level workloads and their total resource requests, as well as the percentage of Web service-type workloads, are counted on a given node. Success sequence analysis results are organized by node, with each node's results including: basic node information, total number of workloads, number of workloads and resource percentages under each attribute category, and the average load score.

[0061] S260. Cluster each workload according to the scheduling remarks of each workload in the scheduling failure sequence to obtain the clustering result of the failure sequence, and then execute S270.

[0062] The clustering process involves grouping workloads based on their scheduling notes within the failure sequence. Specifically, this includes: first, processing the scheduling notes of each workload through text segmentation and keyword extraction to merge semantically similar failure reasons into the same failure type; then, statistically analyzing the number of workloads included in each failure type, the target clusters involved, and the associated load attribute features. The failure sequence clustering results are presented as a classification list, with each category including: failure type name, number and percentage of workloads, and node information of the involved nodes.

[0063] S270. Generate a simulation scheduling report based on the analysis results of each successful sequence and the clustering results of the failed sequences.

[0064] The process of generating a simulated scheduling report based on the analysis results of each successful sequence and the clustering results of the failed sequences includes: generating a structured data table matching the target cluster group based on the scheduling status, scheduling remarks, and load scores of each workload; generating a failure type distribution map based on the clustering results of the failed sequences, and simultaneously generating a node resource utilization comparison map matching each node based on the analysis results of the successful sequences and the node information of each node, forming a set of statistical charts; obtaining the total number of workloads in the target cluster group, and calculating the scheduling success rate based on the analysis results of the successful sequences and the total number of workloads; obtaining a list of core issues according to preset core issue rules and the clustering results of the failed sequences; and integrating the structured data table, the failure type distribution map, the set of statistical charts, the scheduling success rate, and the list of core issues into a simulated scheduling report for output.

[0065] The technical solution of this invention involves obtaining the node resource configuration table of the target cluster group, injecting node scheduling configurations matching each node into a pre-configured simulator instance, and performing simulated scheduling operations on the target cluster group according to the simulator instance and the node scheduling configurations to obtain a scheduling result file. The scheduling result file is then parsed to obtain the scheduling status, scheduling remarks, and load scores of each workload. Based on the scheduling status, each workload is classified to obtain a scheduling success sequence and a scheduling failure sequence. For the scheduling success sequence, the distribution of each workload on each target node is statistically analyzed based on the load attributes of each workload in the scheduling success sequence to obtain a success sequence analysis result matching each target node. For the scheduling failure sequence, each workload is clustered according to the scheduling remarks of each workload in the scheduling failure sequence to obtain a failure sequence clustering result. Finally, a simulated scheduling report is generated based on the analysis results of each success sequence and the failure sequence clustering result. This achieves simulated scheduling of cluster resources, improves the efficiency and accuracy of obtaining simulated scheduling results of cluster resources, and thus improves the efficiency of cluster resource scheduling and the efficiency of business operation in a cluster environment.

[0066] Example 3

[0067] Figure 3 This is a schematic diagram of the structure of a cluster resource simulation scheduling device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0068] Data acquisition module 310 is used to acquire the node resource configuration table of the target cluster group, wherein the target cluster group consists of at least one target cluster, and the target cluster consists of at least one node and at least one workload;

[0069] The simulation scheduling module 320 is used to inject node scheduling configurations that match each node into a pre-configured simulator instance, and to perform simulation scheduling operations on the target cluster group according to the simulator instance and the scheduling configurations of each node to obtain a scheduling result file.

[0070] The report generation module 330 is used to generate a simulated scheduling report that matches the target cluster group based on the scheduling result file.

[0071] The technical solution of this invention obtains the node resource configuration table of the target cluster group, then injects the node scheduling configuration matching each node into a pre-configured simulator instance, and performs simulated scheduling operations on the target cluster group according to the simulator instance and the node scheduling configuration to obtain a scheduling result file. Finally, a simulated scheduling report matching the target cluster group is generated based on the scheduling result file. This realizes the simulated scheduling of cluster resources, improves the efficiency and accuracy of obtaining the simulated scheduling results of cluster resources, and thus improves the efficiency of cluster resource scheduling and the efficiency of business operation in the cluster environment.

[0072] Based on the above embodiments, the data acquisition module 310 is further configured to: before acquiring the node resource configuration table of the target cluster group, use a pre-configured cluster management tool to traverse each target cluster in the target cluster group to obtain a node list matching each target cluster and workload data matching the target cluster group. The node list includes at least one node information, which includes: the node's central processing unit information, memory, tag, allocable resources, topology, and exclusion attributes; perform field trimming, data anonymization, and format standardization on each node information and each workload data to obtain standard node information matching each node and representative workload data matching each target cluster; update the node list of each target cluster based on the standard node information; and assemble the updated node list and workload data to obtain the node resource configuration table of the target cluster group.

[0073] Based on the above embodiments, the simulation scheduling module 320 includes:

[0074] The simulator initialization unit is used to initialize the pre-configured simulator instance, and after confirming that the network transmission interface of the simulator instance is available, uploads the pre-set simulation node dataset, simulation workload file and node scheduling configuration of each node to the simulator instance through the network transmission interface in sequence.

[0075] The simulation scheduling unit is used to trigger a simulation scheduling operation through the simulator instance to obtain a scheduling result file that matches the simulation scheduling operation; wherein, the scheduling result file includes the scheduling status, scheduling remarks and load score of each workload, the scheduling status includes: scheduling successful or scheduling failed, and the scheduling remarks include: scheduling node address or scheduling failure reason.

[0076] Based on the above embodiments, the report generation module 330 includes:

[0077] The file parsing unit is used to parse the scheduling result file to obtain the scheduling status, scheduling notes and load scores of each workload.

[0078] The load classification unit is used to classify each workload based on the scheduling status to obtain a scheduling success sequence and a scheduling failure sequence;

[0079] The first sequence processing unit is used to analyze the distribution of each workload on each target node based on the load attributes of each workload in the successfully scheduled sequence, and obtain the successful sequence analysis results that match each target node.

[0080] The second sequence processing unit is used to cluster each workload according to the scheduling remarks of each workload in the scheduling failure sequence to obtain the clustering result of the failure sequence.

[0081] Clustering unit, used to generate a simulated scheduling report based on the analysis results of each successful sequence and the clustering results of the failed sequences.

[0082] Based on the above embodiments, the clustering unit includes:

[0083] The structured table generation unit is used to generate a structured data table that matches the target cluster group based on the scheduling status, scheduling remarks and load scores of each workload. The structured data table includes the identification information of each workload, the scheduling node address or scheduling failure reason, the load score and the scheduling status.

[0084] The result combination unit is used to generate a failure type distribution map based on the clustering results of failure sequences, and at the same time, it generates a node resource utilization comparison map that matches each node based on the analysis results of successful sequences and the node information of each node, forming a set of statistical charts.

[0085] The success rate calculation unit is used to obtain the total workload of the target cluster group and calculate the scheduling success rate based on the success sequence analysis results and the total workload.

[0086] The list generation unit is used to generate a list of core issues based on preset core issue rules and the clustering results of the failure sequences.

[0087] The results integration unit is used to integrate structured data tables, failure type distribution charts, statistical chart sets, scheduling success rates, and a list of core issues into a simulated scheduling report for output.

[0088] Based on the above embodiments, the report generation module 330 is further configured to: after generating a simulated scheduling report matching the target cluster group according to the scheduling result file, update the scheduling configuration of each node in response to the user's editing operation; return to execute the operation of obtaining the node resource configuration table of the target cluster group based on the updated scheduling configuration of each node, until an updated scheduling report matching the updated scheduling configuration of each node is obtained; process the simulated scheduling report and the updated scheduling report through a pre-configured quantitative indicator system comparison model to obtain comparison difference data; generate a comparison result based on the comparison difference data and send it to the user so that the user can update the scheduling configuration of each node based on the comparison result.

[0089] The cluster resource simulation scheduling device provided in this embodiment of the invention can execute the cluster resource simulation scheduling method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0090] Example 4

[0091] Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0092] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory 12 and an access memory 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the read-only memory 12 or loaded from the storage unit 18 into the access memory 13. The access memory 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, read-only memory 12, and access memory 13 are interconnected via a bus 14. An input / output interface 15 is also connected to the bus 14.

[0093] Multiple components in electronic device 10 are connected to input / output interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0094] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing units, graphics processing units, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a method for simulating the scheduling of cluster resources.

[0095] Accordingly, the method includes:

[0096] Obtain the node resource configuration table of the target cluster group, wherein the target cluster group consists of at least one target cluster, and the target cluster consists of at least one node and at least one workload;

[0097] Inject node scheduling configurations that match each node into a pre-configured simulator instance, and perform simulated scheduling operations on the target cluster group according to the simulator instance and the scheduling configurations of each node to obtain a scheduling result file;

[0098] A simulated scheduling report matching the target cluster group is generated based on the scheduling result file.

[0099] In some embodiments, a method for simulating the scheduling of cluster resources can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via read-only memory 12 and / or communication unit 19. When the computer program is loaded into access memory 13 and executed by processor 11, one or more steps of the method for simulating the scheduling of cluster resources described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform a method for simulating the scheduling of cluster resources by any other suitable means (e.g., by means of firmware).

[0100] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays, application-specific integrated circuits (ASICs), application-specific standard products (ASICs), systems-on-a-chip (SoCs), payload programmable logic devices, computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0101] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0102] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0103] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a cathode ray tube or liquid crystal display) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0104] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0105] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product within the cloud computing service system to address the shortcomings of traditional physical hosts, such as high management difficulty and weak business scalability.

[0106] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

Claims

1. A method for simulated scheduling of cluster resources, characterized in that, The method comprises the following steps: obtaining a node resource configuration table of a target cluster group, the target cluster group comprising at least one target cluster, and each target cluster comprising at least one node and at least one workload; injecting a node scheduling configuration matched with each node into a pre-configured simulator instance, and performing a simulation scheduling operation on the target cluster group according to the simulator instance and the node scheduling configuration, to obtain a scheduling result file; generating a simulation scheduling report matched with the target cluster group according to the scheduling result file.

2. The method of claim 1, wherein, The node resource configuration table comprises cluster information of each target cluster in the target cluster group, node information of each node comprising the target cluster, and load attributes and representative workload data matched with each workload.

3. The method according to any of claims 1-2, characterized in that, Before obtaining the node resource configuration table of the target cluster group, the method further comprises the following steps: traversing each target cluster in the target cluster group by using a pre-configured cluster management tool, to obtain a node list matched with each target cluster and workload data matched with the target cluster group, the node list comprising at least one node information, and the node information comprising central processing unit information, memory, label, allocatable resource, topology structure and repulsion attribute of the node; performing field clipping, data desensitization and format standardization processing on each node information and each workload data, to obtain standard node information matched with each node and representative workload data matched with each target cluster; updating the node list of each target cluster based on each standard node information; assembling the updated node list and each workload data, to obtain the node resource configuration table of the target cluster group.

4. The method of claim 1, wherein, The simulation scheduling operation performed on the target cluster group according to the simulator instance and the node scheduling configuration comprises the following steps: initializing the pre-configured simulator instance, and after confirming that a network transmission interface of the simulator instance is available, uploading a pre-set simulation node data set, a simulation workload file and the node scheduling configuration of each node to the simulator instance through the network transmission interface; triggering the simulation scheduling operation by the simulator instance, to obtain a scheduling result file matched with the simulation scheduling operation; wherein the scheduling result file comprises a scheduling state, a scheduling note and a load score of each workload, and the scheduling state comprises scheduling success or scheduling failure, and the scheduling note comprises a scheduling node address or a scheduling failure reason.

5. The method according to any one of claims 1 to 4, characterized in that, The simulation scheduling report matched with the target cluster group generated according to the scheduling result file comprises the following steps: parsing the scheduling result file to obtain the scheduling state, the scheduling note and the load score of each workload; classifying each workload based on the scheduling state, to obtain a scheduling success sequence and a scheduling failure sequence; for the scheduling success sequence, counting the distribution of each workload on each target node according to the load attributes of each workload in the scheduling success sequence, to obtain a success sequence analysis result matched with each target node; and generating the simulation scheduling report matched with the target cluster group according to the success sequence analysis result and the scheduling failure sequence. The workloads in the scheduling failure sequence are clustered according to the scheduling notes of the workloads in the scheduling failure sequence, to obtain a failure sequence clustering result; Analog scheduling report is generated based on the success sequence analysis result and the failure sequence clustering result.

6. The method of claim 5, wherein, Analog scheduling report is generated based on the success sequence analysis result and the failure sequence clustering result, including: Based on the scheduling state, scheduling notes and load scoring of each workload, a structured data table matching the target cluster group is generated, which contains the identification information of each workload, scheduling node address or scheduling failure reason, load scoring and scheduling state; Based on the failure sequence clustering result, a failure type distribution chart is generated, and based on the success sequence analysis result and the node information of each node, a node resource utilization comparison chart matching each node is generated, to form a set of statistical charts; The total number of workloads of the target cluster group is obtained, and the success rate is calculated based on the success sequence analysis result and the total number of workloads; According to the preset core problem rule and the failure sequence clustering result, a core problem list is obtained; The structured data table, failure type distribution chart, statistical chart set, success rate and core problem list are integrated into an analog scheduling report for output.

7. The method of claim 1, wherein, After generating the analog scheduling report matching the target cluster group based on the scheduling result file, further including: In response to the user's editing operation, the node scheduling configuration is updated; Based on the updated node scheduling configuration, the operation of obtaining the node resource configuration table of the target cluster group is returned, until the updated scheduling report matching the updated node scheduling configuration is obtained; The analog scheduling report and the updated scheduling report are processed by the pre-configured quantitative index system comparison model to obtain comparison difference data; Based on the comparison difference data, a comparison result is generated and sent to the user, so that the user updates the node scheduling configuration based on the comparison result.

8. An electronic device, comprising: The electronic device includes: At least one processor; and The memory is in communication connection with the at least one processor; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the cluster resource simulation scheduling method of any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the cluster resource simulation scheduling method of any one of claims 1-7.

10. A computer program product, characterised in that, The computer program product includes a computer program, which, when executed by the processor, implements the cluster resource simulation scheduling method according to any one of claims 1-7.

Images