Calculation network resource scheduling method, system and device, storage medium and program product

Abstract

The invention discloses a computing network resource scheduling method, system and device, a storage medium and a program product, relates to the technical field of information, and is applied to a computing network resource scheduling system comprising a computing network operation layer. The method comprises the following steps: acquiring computing network resource scheduling information input by the computing network operation layer, the computing network resource scheduling information comprises a resource scheduling demand and resource service topology information; constructing a resource service knowledge graph according to the resource service topology information, and determining a network scheduling strategy according to the resource service knowledge graph and the resource scheduling demand; and scheduling the computing network resources according to the computing network scheduling strategy. The technical problem that the scheduling accuracy of the computing network resources is not high is solved.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to a method, system, device, storage medium and program product for scheduling computing network resources. Background Art

[0002] With the continuous development of IT (Information Technology), the use of computing networks is becoming more and more frequent, which puts forward higher requirements for the scheduling of computing network resources.

[0003] At present, when scheduling computing network resources, the optimal solution for computing network resource scheduling is usually sought for the local resources that currently need to be scheduled, and then resource calls are made based on the optimal solution for computing network resource scheduling of the local resources. However, this method of scheduling computing network resources will cause the problem of non-optimal computing network resource scheduling for the overall business operation due to only scheduling the optimal solution for local resources. Therefore, this method of scheduling computing network resources will cause the scheduling accuracy of computing network resources to be low due to non-optimal computing network resource scheduling for the overall business operation.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a scheduling method, system, storage medium and program product for computing network resources, aiming to solve the technical problem of low scheduling accuracy of computing network resources.

[0006] To achieve the above-mentioned purpose, the present application provides a method for scheduling computing network resources, the method for scheduling computing network resources is applied to a scheduling system for computing network resources, the scheduling system for computing network resources includes a computing network operation layer, and the method for scheduling computing network resources includes:

[0007] Acquire computing network resource scheduling information input by the computing network operation layer, wherein the computing network resource scheduling information includes resource scheduling requirements and resource service topology information;

[0008] Constructing a resource business knowledge graph according to the resource business topology information, and determining a computing network scheduling strategy according to the resource business knowledge graph and the resource scheduling requirements;

[0009] The computing network resources are scheduled according to the computing network scheduling strategy.

[0010] To achieve the above-mentioned purpose, the present application further provides a scheduling system for computing network resources, the scheduling system for computing network resources comprising a computing network operation layer and a controller connected to the computing network operation layer, the controller comprising:

[0011] An information acquisition module, used to acquire computing network resource scheduling information input by the computing network operation layer, wherein the computing network resource scheduling information includes resource scheduling requirements and resource service topology information;

[0012] A strategy determination module, used to construct a resource business knowledge graph according to the resource business topology information, and determine a computing network scheduling strategy according to the resource business knowledge graph and the resource scheduling requirements;

[0013] The resource scheduling module is used to schedule computing network resources according to the computing network scheduling strategy.

[0014] The present application also provides a scheduling device for computing network resources, the scheduling device for computing network resources comprising: at least one processor and a memory communicatively connected to the at least one processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can execute the steps of the scheduling method for computing network resources as described above.

[0015] The present application also provides a storage medium, on which is stored a program for implementing a method for scheduling computing network resources. When the program for the method for scheduling computing network resources is executed by a processor, the steps of the method for scheduling computing network resources as described above are implemented.

[0016] The present application also provides a program product, including a scheduling program for computing network resources, which implements the steps of the above-mentioned scheduling method for computing network resources when executed by a processor.

[0017] The scheduling method of computing network resources of the present application is applied to the scheduling system of computing network resources. The scheduling system of computing network resources includes a computing network operation layer, and the computing network resource scheduling information input by the computing network operation layer is obtained, and the computing network resource scheduling information includes resource scheduling requirements and resource business topology information; a resource business knowledge graph is constructed according to the resource business topology information, and a computing network scheduling strategy is determined according to the resource business knowledge graph and the resource scheduling requirements; and the computing network resources are scheduled according to the computing network scheduling strategy.

[0018] By obtaining resource scheduling requirements and resource business topology information, and constructing a resource business knowledge graph based on the resource business topology information, and then determining the computing network scheduling strategy based on the resource business knowledge graph and resource scheduling requirements, the computing network resources are finally scheduled according to the computing network scheduling strategy. This avoids the problem of only performing optimal resource scheduling on local resources in the prior art, which results in non-optimal computing network resource scheduling for the overall business operation. This method of scheduling computing network resources constructs a resource business knowledge graph related to resource business topology information, and then determines the computing network scheduling strategy based on resource scheduling requirements in the resource business knowledge graph, so as to schedule computing network resources from the perspective of the overall business, thereby avoiding the defect of performing optimal resource scheduling on local resources and improving the scheduling accuracy of computing network resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 A flowchart of a method for scheduling computing network resources provided in Example 1 of the present application;

[0022] Figure 2 A flowchart of a commonly used method for scheduling computing network resources is shown;

[0023] Figure 3 A schematic diagram of a common method for scheduling computing network resources;

[0024] Figure 4 A flowchart of a method for scheduling computing network resources for this application;

[0025] Figure 5 A schematic diagram of a knowledge graph of a method for scheduling computing network resources in this application;

[0026] Figure 6 A schematic diagram of the structure of a scheduling system for computing network resources provided in Example 4 of the present application;

[0027] Figure 7 This is a schematic diagram of the structure of an electronic device provided in Example 5 of the present application.

[0028] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0030] Embodiment 1

[0031] With the development of computing power networks, customers have also put forward higher requirements for computing network scheduling, such as efficient use of computing network resources and rapid completion of tasks. Among them, optimal cloud cost is one of the indicators that most customers care about the most. The cost of cloud resources = unit price of cloud resources × resource usage, where unit price is a variable attribute of resources at the operational level and is managed by the computing network operation layer. The existing scheduling of computing network resources often manages different resources according to the type, specification, region, unit price and other attributes of resources, and cannot provide the overall cost of the business. Therefore, in the process of resource orchestration, the computing network scheduling layer obtains the pricing information of individual resources from the computing network operation layer to find the scheduling strategy of "optimal cloud cost" for individual resources, but lacks overall business information. In other words, the scheduling strategy can only be the optimal solution for local resources and the optimal solution for local time. This local optimization solution may lead to an increase in overall costs when facing large-scale and complex business needs. For example, the scheduling strategy of a certain resource may be optimal in terms of local time and local resources, but it is not necessarily optimal in terms of overall business operation, and may even increase costs such as cross-provincial transmission.

[0032] Therefore, based on the above defects in the scheduling of computing network resources, this application proposes a new scheduling method for computing network resources. By constructing a resource business knowledge graph and scheduling computing network resources from the perspective of the overall business, the defects of resource scheduling for the optimal solution of local resources are avoided, and the scheduling accuracy of computing network resources is improved.

[0033] The present application embodiment provides a method for scheduling computing network resources. In the first embodiment of the method for scheduling computing network resources of the present application, reference is made to Figure 1 , Figure 1 A flowchart of a method for scheduling computing network resources provided in Embodiment 1 of the present application is provided. The method for scheduling computing network resources is applied to a scheduling system for computing network resources. The scheduling system for computing network resources includes a computing network operation layer. The method for scheduling computing network resources includes:

[0034] Step S10, obtaining computing network resource scheduling information input by the computing network operation layer, where the computing network resource scheduling information includes resource scheduling requirements and resource service topology information;

[0035] For example, refer to Figure 2 , Figure 2 The figure is a flow diagram of the scheduling method of commonly used computing network resources. The figure is a functional module diagram of the scheduling of commonly used computing network resources. The infrastructure layer in the figure is responsible for the management of computing network physical resources, including computing power resources and network resources. When the infrastructure resources change (such as adding or removing computing power nodes, network connectivity status changes, customer location changes, etc.), the change information will be used as the perception of the computing network status through the computing network perception layer, and synchronized to the computing network operation layer (updating the resource status of the computing network operation layer), thereby affecting the resource sales of the computing network operation layer, and at the same time, it will be fed back to the computing network scheduling layer through the computing network perception layer to affect the resource scheduling decision. When external customers have computing power needs, they will provide resource subscription requests to the computing network operation layer, and the requests will continue to be sent to the computing network scheduling layer for resource scheduling. The computing power orchestration center inside the computing network scheduling layer obtains a list of candidate resources based on the resource type, performance and other parameters entered by the customer, and then obtains the pricing information of the computing network resources from the computing network operation layer, that is, the "cloud cost" information is queried in the computing network scheduling layer, and the resource set with the lowest cost in the candidate resource list is selected as the final solution and sent to the computing power scheduling center. At this time, the computing power scheduling center sends a physical resource scheduling command to the infrastructure layer to activate resources, and forwards the computing network request to the computing network operation layer for feedback to the customer. That is, the infrastructure layer collects real-time data on computing network resources, including resource types, connectivity, service quality, etc., and transmits the collected information to the computing network perception layer; the computing network perception layer transmits the updated resource status back to the computing network operation layer to display the resources that can be used and are being used; the computing network perception layer provides real-time resource information to the computing network scheduling layer for use in later scheduling; the computing network operation layer forwards the customer's resource request to the computing network scheduling layer for resource application. For further information, please refer to Figure 3 , Figure 3This is a schematic diagram of a common scheduling method for computing network resources. The figure shows a simplified architecture diagram of a typical content service (such as Douyin). Video and other content data are stored in the database and are available to customers through online services. The operation logs of customers viewing are sent back to the big data task to calculate the customer portrait. The customer portrait data provides support for the online service. Among them, the construction of online services, content databases, customer portrait databases, and big data tasks may occur at different times and be initiated by different teams. At this time, the superposition of scheduling strategies of different batches may not necessarily achieve the optimal overall business cost. For example, online services and content databases may be built in the first batch. At this time, in order to reduce the cost caused by network transmission, they may be scheduled to the Shanghai data center close to the customer for service deployment. However, when calculating customer portraits in the second batch, the Inner Mongolia data center with relatively cheap computing and storage may be selected for deployment. However, customer portraits must eventually be provided to online services. Under this solution, when online services request customer portrait databases, they must be transmitted across provinces. As the business volume increases, the cost of cross-provincial transmission will continue to increase. In the end, it will be found that if the second batch applies for the second-best cost Nanjing data center, the overall cost may be lower. Furthermore, based on the above scheduling method of computing network resources, a scheduling method of computing network resources in this embodiment is proposed.

[0036] In this embodiment, reference is given priority to Figure 4 , Figure 4 This is a flow chart of a method for scheduling computing network resources of the present application. The method for scheduling computing network resources is applied to a scheduling system for computing network resources. The scheduling system for computing network resources includes a computing network operation layer and a controller of the present embodiment, wherein the controller may be a FinOps (Financial Operations) module, or may be a controller composed of other control modules. The FinOps module is used as an example for explanation. The FinOps module is directly connected to the computing network operation layer, and then obtains the synchronous business expansion and request global cost information of the computing network operation layer, so as to determine the global scheduling strategy within the FinOps module, and finally the global scheduling strategy is used to implement the scheduling of computing network resources in the computing network scheduling layer. The FinOps module includes a business topology management function: constructing a knowledge graph of resources and businesses through the connectivity between resources and the attribution between resources and businesses; a cost management function: recording all historical cost information of resources based on the time dimension (for example, by day or by hour), including the latest cost. A cost association analysis function: based on the knowledge graph of resources and businesses, and the cost information of the resource time dimension, the time-based cost information of the business dimension is calculated. Application to Figure 3In all businesses, the cost of online services and the cost of customer portraits can be known by calculating the cost of each resource. Instead of the cost of a server or a disk, the cost can be meaningful to the business, which is more valuable for customer decision-making. Cost governance function: According to the business cost information, the resource scheduling strategy is optimized and adjusted at any time (customer demand or cost changes), and the resource preference parameters are sent to the computing network scheduling layer through the computing network operation layer for final scheduling. Among them, the cost governance function abstracts the resource matching strategy into a subgraph matching algorithm in the cost map, that is, to find the subgraph with the smallest weight in the weighted large graph whose topology meets the structure of the customer demand map. The weight can be cost, real-time, region, and security, etc. For example, the best subgraph is determined as the global scheduling strategy by comprehensively considering cost, real-time, region, and security to ensure the scheduling accuracy of computing network resources.

[0037] In one embodiment, based on Figure 4 When the FinOps module performs computing network resource scheduling, after obtaining the computing network resource scheduling information input by the computing network operation layer, it will schedule computing network resources for a certain customer (hereinafter referred to as the target customer). The computing network resource scheduling information includes resource scheduling requirements and resource business topology information. Resource scheduling requirements refer to the scheduling requirements of target customers, such as low cost, general real-time performance, and information on what kind of computing network resources need to be scheduled. Resource business topology information refers to the information on the resources and businesses of the target customers. Then, the resource business knowledge graph of the customer can be constructed based on the information on the resources and businesses, and the accuracy of the scheduling of computing network resources can be ensured by calling based on the resource business knowledge graph. It is worth noting that the resource scheduling demand may not be initiated by the customer. In one case, if customer A1 is using computing network resource A normally, and the controller finds that the cost of computing network resource A has changed, it will re-analyze the computing network resources called by customer A1, that is, rebuild the customer A1 resource business knowledge graph, and then determine in the resource business knowledge graph whether it is necessary to continue to use computing network resource A, or to use better computing network resource B to meet the resource scheduling demand most recently input by customer A1, so as to improve the intelligence of the scheduling of computing network resources, and intelligently select the best computing network resources to improve the customer experience (the customer can set whether adaptive changes can be made).

[0038] Step S20, constructing a resource business knowledge graph according to the resource business topology information, and determining a computing network scheduling strategy according to the resource business knowledge graph and resource scheduling requirements;

[0039] In this embodiment, after obtaining the resource scheduling requirements and resource business topology information, a resource business knowledge graph will be constructed based on the resource business topology information, that is, the resource business knowledge graph required by the entire target customer is constructed by determining the resources and services in the resource business topology information, wherein the resource business knowledge graph refers to a topological network composed of resources, services, and the relationship between them when the target customer applies for resources to meet the business demands. In this topological network, the type, id, cost and other attribute information of resources and services will be added to form a knowledge graph of the entire business. And then based on the determined resource business knowledge graph, the optimal computing network scheduling strategy for resource scheduling needs is determined. The computing network scheduling strategy refers to the optimal computing network scheduling method determined based on resource business. For example, the commonly used method is that the S1 scheduling of target customer A1 is the best scheduling for computing network resource A outside the province, but the overall cost is not taken into account. For example, most of the scheduling resources of target customer A1 are computing network resources within the province, and from the perspective of the overall business, the S1 scheduling of out-of-province scheduling is not the best overall, and the S1 scheduling of computing network resource A within the province is the best. The accuracy of computing network resource scheduling can be improved by scheduling based on the overall situation, which can further meet the customer's usage experience. It is worth mentioning that resource scheduling requirements can be requirements such as cost, real-time performance, region and security. For example, if the computing network resources with the highest real-time performance are given priority for scheduling, the computing network resources with the highest real-time performance in the resource business knowledge graph will be given priority. If the computing network resources in the province are given priority for scheduling, the computing network resources in the province in the resource business knowledge graph will be given priority. If there are multiple requirements, they will be selected based on the weight, such as cost first, followed by real-time performance, and then the computing network scheduling strategy will be determined based on the weight of the computing network resources (priority of cost and real-time performance).

[0040] Step S30, scheduling computing network resources according to the computing network scheduling strategy.

[0041] In this embodiment, after the computing network scheduling strategy is determined, the computing network resources will be scheduled based on the computing network scheduling strategy. The main process is to send the computing network scheduling strategy to the computing network scheduling layer to control the computing network scheduling layer to schedule the computing network resources in the infrastructure layer, thereby completing the entire computing network resource scheduling process based on global business to ensure the scheduling accuracy of the computing network resources.

[0042] In one embodiment, the scheduling process of the entire computing network resources is that the FinOps module batch queries the FinOps data of all businesses associated with this resource request through the association and traversal algorithm of the knowledge graph, replacing the previous method of isolated query of each resource one by one, and finally transmits the queried FinOps data to the computing network scheduling layer through the computing network operation layer for resource scheduling, and synchronizes the status information to the computing network operation layer after the resource scheduling is successful, and synchronizes the topology (including connectivity and cost) information of the business to the FinOps module through the computing network operation layer. The scheduling process of the entire computing network resources batch queries all data of the subgraph through the association and traversal algorithm of the business graph, which is more efficient than the point query method in which each resource node initiates the query process separately. At the same time, by building a more comprehensive business knowledge graph, it is possible to more accurately evaluate the cost at the business level, thereby formulating a better scheduling strategy. It is worth mentioning that the FinOps module can also further analyze and predict resource price fluctuations and demand peaks (such as cost forecasting based on cost change trends) by collecting cost information of resource and business time series, so as to take measures in advance during resource scheduling and reduce the scheduling cost of the overall computing network resources.

[0043] As an example, a method for scheduling computing network resources includes, through steps S10 to S30: a scheduling system applied to computing network resources, the scheduling system for computing network resources includes a computing network operation layer, by obtaining computing network resource scheduling information input by the computing network operation layer, the computing network resource scheduling information includes resource scheduling requirements and resource business topology information; constructing a resource business knowledge graph according to the resource business knowledge graph and resource scheduling requirements, and determining a computing network scheduling strategy according to the computing network scheduling strategy. By obtaining resource scheduling requirements and resource business topology information, and constructing a resource business knowledge graph according to the resource business topology information, and then determining a computing network scheduling strategy based on the resource business knowledge graph and resource scheduling requirements, finally scheduling computing network resources according to the computing network scheduling strategy. This avoids the problem in the prior art of only performing optimal resource scheduling on local resources, which results in suboptimal computing network resource scheduling for the overall business operation. This method of scheduling computing network resources constructs a resource business knowledge graph related to resource business topology information, and then determines the computing network scheduling strategy based on resource scheduling requirements in the resource business knowledge graph, so as to schedule computing network resources from the perspective of the overall business, thereby avoiding the defect of performing optimal resource scheduling on local resources and improving the scheduling accuracy of computing network resources.

[0044] Embodiment 2

[0045] Further, in another embodiment of the present application, the same or similar content as the above-mentioned embodiment 1 can refer to the above introduction and will not be repeated later. On this basis, the resource business topology information includes resource topology information and business topology information. The resource topology information includes the initial cost of multiple resource nodes and the edge cost of each resource node communication. The steps of constructing a resource business knowledge graph according to the resource business topology information include:

[0046] Step S21, for each resource node, determine the sum of the initial cost and the edge cost as the resource point cost;

[0047] Step S22, constructing a resource business knowledge graph based on the resource point cost and business topology information of each resource node.

[0048] In this embodiment, resource business topology information includes resource topology information and business topology information. Resource topology information includes the initial cost of multiple resource nodes and the edge cost of each resource node communication. Resource topology information refers to the relevant information of the computing network resource point. Business topology information is based on the business-related information formed by the computing network resources. Resource nodes refer to nodes on the computing network resources. The initial cost refers to the initial cost of the resource node, which can be pre-defined based on the customer. The edge cost refers to the cost of communicating with the resource node. Then, all resource nodes in the resource layer and the "access" relationship between them are filtered out. Resource nodes include all software and hardware resources that will directly generate costs, such as a bare metal machine, a hosted database, etc. These resources can be queried in the bill details. The "access" relationship reflects the cost sharing and attribution relationship. For example, a bare metal machine has a usage relationship with a microservice because it deploys a microservice, and a hosted database has a relationship of belonging to a certain business because it stores the data of a certain business, that is, the edge cost of the resource node. For each resource node, the sum of the initial cost and the edge cost is determined as the resource point cost, and then the resource business knowledge graph is constructed based on the resource point cost and business topology information of the resource node. The calculation formula for the resource point cost is as follows:

[0049]

[0050] Among them, Node old is the initial cost of the node, Node new is the cost of the node after update (resource point cost), Edge kIt is the edge cost of the node pointing to the kth target node. For example, the initial cost of resource node F is F11, the edge cost for target node F1 is F12, and the edge cost for target node F2 is F13, that is, the resource point cost of resource node F is F11+F12+F13. Finally, the resource point cost corresponding to each resource node can be determined, and then the resource business knowledge graph can be constructed based on the resource point cost and business topology information corresponding to each resource node to provide a basis for the scheduling of subsequent computing network resources. That is, the resource nodes and business layer nodes after the cost is updated together form a new DAG graph (Directed Acyclic Graph). The nodes in the DAG graph are interconnected through the "attribution" relationship, and then the resource business knowledge graph is constructed through the DAG graph.

[0051] Furthermore, the business topology information includes the cost ratio of the downstream sub-cost of each resource node. The steps of constructing a resource business knowledge graph according to the resource point cost and business topology information of each resource node include:

[0052] Step S221, for each resource node's ownership relationship, determine the product of the resource point cost and the cost ratio as the downstream node cost;

[0053] Step S222, constructing a resource business knowledge graph based on the downstream node cost, resource point cost and the ownership relationship of the resource node.

[0054] In this embodiment, the service topology information includes the cost ratio of the downstream cost of each resource node, wherein the candidate node set to be shared can be found first, the characteristics of this type of node are: all upstream nodes have shared costs to it, but it has not yet shared costs to downstream nodes (the first execution must be the resource node obtained in the above steps); then these candidate nodes follow the "attribution" relationship and share their own costs to the next level nodes in proportion. The calculation formula is:

[0055]

[0056] Among them, Node next is the cost of the downstream node, Node k is the cost of the kth upstream candidate node of the downstream node, Ratio kIt is the proportion of the k-th candidate node allocated to this node. If there is an upstream node for the downstream node, the cost of the upstream node is F11, and the cost ratio is a, then the cost of the downstream node is aF11, and then the downstream node cost of all downstream nodes can be determined, and the cost calculation continues for each downstream node until the cost of all nodes in the DAG graph is calculated. Finally, a resource business knowledge graph can be constructed based on the downstream node cost, resource point cost, and the attribution relationship of the resource node, that is, the cost of each business in the DAG graph is determined based on the resource nodes. If there is only one business in the DAG graph, the scheduling cost of the computing network resources of the business will be determined. If there are multiple businesses, the scheduling costs of multiple businesses when using different computing network resources will be determined. Further, refer to Figure 5 , Figure 5 This is a knowledge graph diagram of the scheduling method of computing network resources in this application. The entire knowledge graph diagram can be divided into two parts: the business layer and the resource layer. Resource layer: The data center of the first / second computer room is used for explanation, which describes the connection relationship of physical resources, including the "deployment" relationship between resources (such as EC2) and data centers, and the "access" relationship between resources (such as gateways and k8s nodes). Among them, the "access" relationship can also be divided into types such as intranet access, cross-network dedicated line access, and public network access according to the data center to which it belongs. The resource layer nodes and relationships of the graph contain cost attribute information, such as the cost of EC2 nodes, the dedicated line cost of cross-provincial dedicated line access, and the traffic cost of public network access. Business layer: describes the "attribution" relationship of the business, including which sub-businesses the resources "belong to" and which parent businesses the sub-businesses "belong to". The nodes and relationships of the business layer also contain cost attribute information. The cost on the node represents the overall cloud cost of the business; the cost on the relationship indicates how much cloud cost the downstream "attributes" to the upstream business through this path. Resources are scheduled based on the knowledge graph diagram, and the scheduling accuracy of computing network resources is guaranteed from the overall perspective of the business.

[0057] In one embodiment, the step of determining a computing network scheduling strategy according to a resource business knowledge graph and a resource scheduling requirement includes:

[0058] Step S23, determining the scheduling basis in the resource scheduling requirement, and selecting a matching schedulable subgraph in the resource business knowledge graph based on the scheduling basis;

[0059] Step S24, when there is one schedulable subgraph, the scheduling strategy corresponding to the schedulable subgraph is used as the computing network scheduling strategy;

[0060] Step S25, when there are multiple schedulable subgraphs, determine the target schedulable subgraph with the smallest weight among the schedulable subgraphs, and use the scheduling strategy corresponding to the target schedulable subgraph as the computing network scheduling strategy.

[0061] In this embodiment, when determining the computing network scheduling strategy based on the resource scheduling requirements in the resource business knowledge graph, the scheduling basis in the resource scheduling requirements is determined, and then a matching schedulable subgraph is selected in the resource business knowledge graph based on the scheduling basis, where the scheduling basis refers to the customer's scheduling basis, such as the need to schedule storage computing network resources and the requirement for storage computing network resources to be low cost. Then, a matching schedulable subgraph can be selected in the resource business knowledge graph. Figure 1 Generally, it is a subgraph that satisfies the storage computing network resources, that is, the subgraph is determined based on the customer's primary needs, where the primary needs refer to the customer's basic scheduling needs, that is, what the customer needs to do when scheduling the computing network resources. At this time, you can select a matching schedulable subgraph in the resource business knowledge graph based on the customer's primary needs, that is, a business subgraph composed of computing network resources that meet the customer's primary needs. At this time, when there is one schedulable subgraph, the scheduling policy corresponding to the schedulable subgraph is used as the computing network scheduling policy. Because there is no optional situation at this time, the scheduling policy corresponding to the schedulable subgraph will be directly used as the computing network scheduling policy. The scheduling policy corresponding to the schedulable subgraph refers to the scheduling policy for scheduling each computing network resource in the schedulable subgraph; conversely, when there are multiple schedulable subgraphs, the target schedulable subgraph with the smallest weight in the schedulable subgraph will be determined, and the scheduling policy corresponding to the target schedulable subgraph will be used as the computing network scheduling policy. The minimum weight is actually based on the secondary demand of the scheduling basis (that is, what advantages this scheduling needs to meet, such as real-time performance, cost, etc.) , and then the schedulable subgraph with the smallest comprehensive weight of real-time and cost (i.e., the best real-time and cost, such as comprehensively selecting a schedulable subgraph with real-time preference and low cost as the target schedulable subgraph, assuming that the user weight is 80% real-time and 20% cost, it can be biased towards real-time and appropriately consider the cost) can be determined based on multiple schedulable subgraphs as the target schedulable subgraph. The scheduling strategy corresponding to the target schedulable subgraph refers to the scheduling strategy for scheduling each computing network resource in the target schedulable subgraph, and then each computing network resource in the schedulable subgraph can be scheduled based on the best scheduling method to improve the scheduling accuracy of computing network resources. It is worth noting that the entire process of determining the schedulable subgraph is the process of determining the idle resources of the computing network resources, that is, it is necessary to first determine that the computing network resources are in an idle state, and then the subsequent primary demand judgment and secondary demand judgment will be executed, thereby ensuring the accuracy of the scheduling of computing network resources.

[0062] Embodiment 3

[0063] Further, in another embodiment of the present application, the same or similar content as the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. On this basis, the scheduling system of computing network resources includes a computing network scheduling layer connected to the computing network operation layer, and an infrastructure layer connected to the computing network scheduling layer. The steps of scheduling computing network resources according to the computing network scheduling strategy include:

[0064] Step S31, determine the target computing network resources that need to be scheduled in the computing network scheduling strategy, and control the computing network scheduling layer to call the target computing network resources in the infrastructure layer.

[0065] In this embodiment, the scheduling system of computing network resources includes a computing network scheduling layer connected to the computing network operation layer, and an infrastructure layer connected to the computing network scheduling layer. After determining the computing network scheduling strategy, the computing network scheduling layer will be controlled to call the target computing network resources in the infrastructure layer by determining the target computing network resources that need to be scheduled in the computing network scheduling strategy to complete the computing network resource scheduling. This embodiment uses FinOps to calculate and record the knowledge graph of computing network business data (the knowledge graph organizes the topology of computing network system resources, the topology of business, and the relationship between the two), and produces the scheduling strategy parameters required by the downstream computing network scheduling layer based on this graph information. Compared with the existing proposal that can only rely on the independent scheduling of individual resources, it can well solve the conflict problem between the local optimal and global optimal solutions and reduce the cost of using the computing network.

[0066] In one embodiment, the step of obtaining the computing network resource scheduling information input by the computing network operation layer includes:

[0067] Step S11, obtaining computing network resource information input by the computing network operation layer, wherein the computing network resource information includes resource cost status and resource scheduling status;

[0068] Step S12, when the resource cost state is a preset cost change state, and / or the resource scheduling state is a preset demand resource state, the resource scheduling demand and resource business topology information corresponding to the target scheduling object are used as the computing network resource scheduling information, wherein the target scheduling object includes a scheduling object whose resource cost state is a preset cost change state, and / or the resource scheduling state is a preset demand resource state.

[0069] In this embodiment, when communicating with the computing network operation layer in real time, it will be determined whether computing network resource scheduling is needed based on the communication information between the computing network operation layer and the computing network resource information input by the computing network operation layer in real time. The computing network resource information includes resource cost status and resource scheduling status. The resource cost status refers to the cost status of the resource points of the entire computing network, which is generally changed or unchanged. Whether computing network resource scheduling is needed can be determined based on whether the cost changes. The resource scheduling status refers to the needs input by the customer. For example, if customer A needs to call computing network resources, this demand will be input into the FinOps module as the resource scheduling status. Furthermore, when it is determined that the resource cost state is a preset cost change state, and / or the resource scheduling state is a preset demand resource state, the resource scheduling demand and resource business topology information corresponding to the target scheduling object are used as the computing network resource scheduling information, wherein the target scheduling object includes a scheduling object whose resource cost state is a preset cost change state, and / or whose resource scheduling state is a preset demand resource state. The preset demand resource state means that there may be a computing network resource scheduling demand, and the preset cost change state means that the computing network resource cost changes. wherein the target scheduling object may be a customer who has its own scheduling demand, or the resource cost changes, and the changed resource is a resource used by the customer (on the premise that the customer definition can adaptively adjust the computing network resources), so as to schedule the customer's computing network resources based on customer demand and real-time cost changes, thereby improving the intelligence of computing network resource scheduling.

[0070] In one embodiment, the input of the FinOps module includes two parts: the business topology change event Eventtopo (resource cost status) and the newly applied resource parameters Argsresource (resource scheduling status). Among them, the structure of Eventtopo is {type:type1,id:id1,args:args1}. Type indicates what type of change event it is, and the candidate set includes: adding nodes, modifying nodes, deleting nodes, adding edges, modifying edges, and deleting edges; id is the unique ID of the modified object; args represents the parameters of the modified node or edge, which is mainly attribute information. It is a kv structure organized in json format, and may include attributes such as cost, updatetime, operator, etc. The structure of Argsresource is consistent with the parameters of the request computing network scheduling layer in the old algorithm, mainly including resource type, resource quantity, security preference, cost preference, etc. For example Figure 3In the scenario shown, the resource type is disk storage; the resource quantity is 1T; the security preference is that data can only flow within the province and not be stored in other provinces; the cost preference is to have the lowest price; and the performance accepts hourly responses. Furthermore, the output structure of the FinOps module is: {nodes:[{id:id1,type:type1,cluster:cluster1...},{id:id2,type:type2,cluster:cluster2...}...],edges:[{id:id1,type:type1,from:from1,to:to1}...]}. Among them, nodes record the information of all computing nodes, mainly including id (unique ID of the computing node), type (node ​​type), cluster (cloud and cluster information where the node is located); edges record the information of all networks, mainly including id (unique ID of the network), type (type of the network), from (the starting computing node information of the network), to (the target computing node information of the network). In addition, it can also contain other attribute information used for calculation or query. For example Figure 3 In the scenario, the output nodes are: id1 represents the unique ID of the disk resource; type is SATAHDD, which is a low-cost disk type with poor performance; cluster is the Beijing cluster. The information of the edges is: id-edge1 represents the unique ID of the network path; type is a dedicated line; from is the ID of a microservice in idx; to is id1, which is the ID of the disk above. There is more than one set of information above. After this information is passed to the computing network scheduling layer, it will try to schedule resources. If the scheduling is successful, it will be returned to the customer. If it fails, the failure information will be fed back to the FinOps module to recalculate the candidate resources. The above is only an example of the output and input structure of the FinOps module. It can also be other structures, which are not limited here.

[0071] Embodiment 4

[0072] The present application also provides a scheduling system for computing network resources, referring to Figure 6 , Figure 6 This is a structural diagram of a scheduling system for computing network resources provided in Embodiment 5 of the present application. The scheduling system for computing network resources includes a computing network operation layer and a controller connected to the computing network operation layer. The controller includes:

[0073] The information acquisition module A01 is used to obtain the computing network resource scheduling information input by the computing network operation layer, and the computing network resource scheduling information includes resource scheduling requirements and resource business topology information;

[0074] The strategy determination module A02 is used to construct a resource business knowledge graph based on the resource business topology information, and determine the computing network scheduling strategy based on the resource business knowledge graph and resource scheduling requirements;

[0075] The resource scheduling module A03 is used to schedule computing network resources according to the computing network scheduling strategy.

[0076] Optionally, the policy determination module A02 is further used for:

[0077] For each resource node, determine the sum of the initial cost and the edge cost as the resource point cost;

[0078] Construct a resource business knowledge graph based on the resource point cost and business topology information of each resource node.

[0079] Optionally, the policy determination module A02 is further used for:

[0080] For each resource node’s ownership relationship, determine the product of the resource point cost and the cost ratio as the downstream node cost;

[0081] Construct a resource business knowledge graph based on downstream node costs, resource point costs, and the ownership relationship of resource nodes.

[0082] Optionally, the policy determination module A02 is further used for:

[0083] Determine the scheduling basis in the resource scheduling requirements, and select a matching schedulable subgraph in the resource business knowledge graph based on the scheduling basis;

[0084] When there is one schedulable subgraph, the scheduling strategy corresponding to the schedulable subgraph is used as the computing network scheduling strategy;

[0085] When there are multiple schedulable subgraphs, determine the target schedulable subgraph with the smallest weight among the schedulable subgraphs, and use the scheduling strategy corresponding to the target schedulable subgraph as the computing network scheduling strategy.

[0086] Optionally, the resource scheduling module A03 is further used for:

[0087] Determine the target computing network resources that need to be scheduled in the computing network scheduling strategy, and control the computing network scheduling layer to call the target computing network resources in the infrastructure layer.

[0088] Optionally, the information acquisition module A01 is further used to:

[0089] Obtaining computing network resource information input by the computing network operation layer, wherein the computing network resource information includes resource cost status and resource scheduling status;

[0090] When the resource cost state is a preset cost change state, and / or the resource scheduling state is a preset demand resource state, the resource scheduling demand and resource business topology information corresponding to the target scheduling object are used as the computing network resource scheduling information, wherein the target scheduling object includes a scheduling object whose resource cost state is a preset cost change state, and / or the resource scheduling state is a preset demand resource state.

[0091] The scheduling system for computing network resources provided by the present invention adopts the scheduling method for computing network resources in the above embodiment, and solves the technical problem that the scheduling accuracy of computing network resources is not high. Compared with the prior art, the beneficial effects of the scheduling system for computing network resources provided by the embodiment of the present invention are the same as the beneficial effects of the scheduling method for computing network resources provided by the above embodiment, and other technical features of the scheduling system for computing network resources are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0092] Embodiment 5

[0093] An embodiment of the present invention provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the scheduling method for computing network resources in the above-mentioned embodiment one.

[0094] Reference below Figure 7 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The electronic device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0095] like Figure 7 As shown, the electronic device may include a processing system 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage system 1003 into a random access memory (RAM) 1004. Various programs and data required for the operation of the electronic device are also stored in the RAM 1004. The processing system 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus.

[0096] Typically, the following systems can be connected to the I / O interface 1006: an input system 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output system 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage system 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication system 1009. The communication system can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0097] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication system 1009, or installed from the storage system 1003, or installed from the ROM 1002. When the computer program is executed by the processing system 1001, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0098] The electronic device provided by the present invention adopts the scheduling method of computing network resources in the above embodiment to solve the technical problem that the scheduling accuracy of computing network resources is not high. Compared with the prior art, the beneficial effects of the electronic device provided by the embodiment of the present invention are the same as the beneficial effects of the scheduling method of computing network resources provided by the above embodiment, and other technical features in the electronic device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0099] It should be understood that the various parts of the present disclosure can be implemented with hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0100] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0101] Embodiment 6

[0102] This embodiment provides a storage medium, which may be a computer-readable storage medium having computer-readable program instructions stored thereon, and the computer-readable program instructions are used to execute the scheduling method for computing network resources in the above embodiment.

[0103] The computer-readable storage medium provided in the embodiment of the present invention may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0104] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0105] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the electronic device executes: obtaining the computing network resource scheduling information input by the computing network operation layer, the computing network resource scheduling information includes resource scheduling requirements and resource business topology information; constructing a resource business knowledge graph based on the resource business topology information, and determining a computing network scheduling strategy based on the resource business knowledge graph and the resource scheduling requirements; and scheduling computing network resources according to the computing network scheduling strategy.

[0106] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the client computer, partially on the client computer, as a separate software package, partially on the client computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the client computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0107] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0108] The modules involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of the module does not limit the unit itself in some cases.

[0109] The computer-readable storage medium provided by the present invention stores computer-readable program instructions for executing the above-mentioned method for scheduling computing network resources, which solves the technical problem that the scheduling accuracy of computing network resources is not high. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the embodiment of the present invention are the same as the beneficial effects of the method for scheduling computing network resources provided by the above-mentioned embodiment, and will not be repeated here.

[0110] Embodiment 7

[0111] The present application also provides a program product, including a computer program, which implements the steps of the above-mentioned method for scheduling computing network resources when executed by a processor.

[0112] The program product provided by this application solves the technical problem of low scheduling accuracy of computing network resources. Compared with the prior art, the beneficial effects of the program product provided by the embodiment of the present invention are the same as the beneficial effects of the scheduling method of computing network resources provided by the above embodiment, which will not be repeated here.

[0113] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. A method for scheduling computing network resources, characterized in that: The computing network resource scheduling method is applied to a computing network resource scheduling system, the computing network resource scheduling system includes a computing network operation layer, and the computing network resource scheduling method includes: Acquire computing network resource scheduling information input by the computing network operation layer, wherein the computing network resource scheduling information includes resource scheduling requirements and resource service topology information; Constructing a resource business knowledge graph according to the resource business topology information, and determining a computing network scheduling strategy according to the resource business knowledge graph and the resource scheduling requirements; The computing network resources are scheduled according to the computing network scheduling strategy.

2. The method for scheduling computing network resources according to claim 1, characterized in that: The resource business topology information includes resource topology information and business topology information, the resource topology information includes the initial cost of multiple resource nodes and the edge cost of each of the resource nodes communicating, and the step of constructing a resource business knowledge graph according to the resource business topology information includes: For each of the resource nodes, determining the sum of the initial cost and the edge cost as the resource point cost; A resource business knowledge graph is constructed according to the resource point cost of each resource node and the business topology information.

3. The method for scheduling computing network resources as claimed in claim 2, characterized in that: The business topology information includes the cost ratio of the downstream sub-cost of each of the resource nodes, and the step of constructing a resource business knowledge graph according to the resource point cost of each of the resource nodes and the business topology information includes: For each of the resource node ownership relationships, determine the product of the resource point cost and the cost ratio as the downstream node cost; A resource business knowledge graph is constructed based on the downstream node cost, the resource point cost and the ownership relationship of the resource node.

4. The method for scheduling computing network resources according to claim 1, characterized in that: The step of determining the computing network scheduling strategy according to the resource business knowledge graph and the resource scheduling requirement includes: Determine a scheduling basis in the resource scheduling requirement, and select a matching schedulable subgraph in the resource business knowledge graph based on the scheduling basis; When there is one schedulable subgraph, using the scheduling strategy corresponding to the schedulable subgraph as the computing network scheduling strategy; When there are multiple schedulable subgraphs, a target schedulable subgraph with the smallest weight among the schedulable subgraphs is determined, and a scheduling strategy corresponding to the target schedulable subgraph is used as a computing network scheduling strategy.

5. The method for scheduling computing network resources according to any one of claims 1 to 4, characterized in that: The scheduling system for computing network resources includes a computing network scheduling layer connected to the computing network operation layer, and an infrastructure layer connected to the computing network scheduling layer. The step of scheduling computing network resources according to the computing network scheduling strategy includes: Determine the target computing network resources that need to be scheduled in the computing network scheduling strategy, and control the computing network scheduling layer to call the target computing network resources in the infrastructure layer.

6. The method for scheduling computing network resources according to any one of claims 1 to 4, characterized in that: The step of obtaining the computing network resource scheduling information input by the computing network operation layer includes: Acquire computing network resource information input by the computing network operation layer, wherein the computing network resource information includes resource cost status and resource scheduling status; When the resource cost state is a preset cost change state, and / or the resource scheduling state is a preset demand resource state, the resource scheduling requirements and resource business topology information corresponding to the target scheduling object are used as the computing network resource scheduling information, wherein the target scheduling object includes a scheduling object whose resource cost state is a preset cost change state, and / or the resource scheduling state is a preset demand resource state.

7. A scheduling system for computing network resources, characterized in that: The scheduling system for computing network resources includes a computing network operation layer and a controller connected to the computing network operation layer, and the controller includes: An information acquisition module, used to acquire computing network resource scheduling information input by the computing network operation layer, wherein the computing network resource scheduling information includes resource scheduling requirements and resource service topology information; A strategy determination module, used to construct a resource business knowledge graph according to the resource business topology information, and determine a computing network scheduling strategy according to the resource business knowledge graph and the resource scheduling requirements; The resource scheduling module is used to schedule computing network resources according to the computing network scheduling strategy.

8. A computing network resource scheduling device, characterized in that: The scheduling device for computing network resources includes: at least one processor; a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the method for scheduling computing network resources described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a program for implementing a method for scheduling computing network resources, and the program for implementing a method for scheduling computing network resources is executed by a processor to implement the steps of the method for scheduling computing network resources as described in any one of claims 1 to 6.

10. A program product, characterized in that The program product includes a scheduling program for computing network resources, and when the scheduling program for computing network resources is executed by a processor, the steps of the scheduling method for computing network resources as described in any one of claims 1 to 6 are implemented.

Images