Distributed computing power resource scheduling method and system

Abstract

The invention relates to the technical field of computing power resource scheduling, in particular to a distributed computing power resource scheduling method and system. The method comprises the following steps: step 1) querying a computing power resource service identifier in a computing power resource request message in a mapping relationship between resource states of locally maintained computing power resource nodes and forwarding nodes, and determining a group of candidate computing power resource nodes; 2) determining a corresponding computing power matching strategy based on a resource demand preference field in the computing power resource request message; based on a corresponding computing power matching strategy and a computing power resource demand field and a network resource demand field in the computing power resource request message, selecting a computing power resource node meeting a user resource demand from the group of candidate computing power resource nodes; and 3) modifying a destination address of the computing power resource request message into a next hop forwarding address of the message corresponding to the selected computing power resource node. According to the method, flexible scheduling of the computing power resources is realized, and the overall utilization rate and the scheduling success rate of the computing power resources are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computing power resource scheduling, and in particular to a distributed computing power resource scheduling method and system. Background Art

[0002] The development of the digital economy has promoted the generation of massive data. The processing of massive data requires the powerful computing power of cloud, edge, and end collaboration and widely covered network connections; computing network integration has become an important trend. In this context, a new network paradigm that integrates computing resource scheduling capabilities into the network itself, the "computing network", has been proposed. It is a further in-depth integration of computing power and network, aiming to connect distributed computing nodes, coordinate computing resources, network resources, and user needs, and realize the integrated scheduling of computing network resources.

[0003] Various network service providers represented by operators have proposed their own computing power network architecture, and have put forward their own ideas on the coordination of computing power and network resources, and have scheduled computing power resources in a centralized or distributed manner.

[0004] However, with the rapid increase in distributed cloud, network, edge, and end computing nodes and users' diverse computing demands, the existing centralized computing resource scheduling mechanism in the computing network faces scalability limitations and is not suitable for large-scale network deployment; especially in scenarios where some computing nodes are subject to sudden high-load tasks or computing nodes frequently join and exit, resulting in highly dynamic changes in the state of computing resources, a single centralized scheduling platform faces a huge burden of communication and computing scheduling, which can easily lead to single point failures, processing scheduling performance bottlenecks, and other problems. Distributed scheduling has advantages in scalability and reliability.

[0005] Most of the existing computing network distributed resource scheduling frameworks, such as CFN (computing first network) and CAN (ComputingAware Network), are based on a flat architecture, which fully notifies and schedules resource status information among all nodes with scheduling functions (referred to as resource scheduling nodes) globally. In the face of the above-mentioned highly dynamic scenarios, it is difficult to quickly synchronize computing resource status information. Delayed resource status information can easily lead to inefficient resource utilization and scheduling failures. Moreover, the existing distributed resource scheduling framework is based on the ordinary TCP / IP (Transmission Control Protocol / Internet Protocol) network for scheduling. Its end-to-end routing method lacks flexibility and programmability. Especially in highly dynamic scenarios where the resource status of some computing nodes fluctuates violently, the computing demand message cannot dynamically adjust the scheduling purpose according to the real-time computing resource status changes during the routing process, which is also easy to cause inefficient resource utilization and scheduling failure. In addition, most of the resource scheduling frameworks in the existing computing network adopt a single, static computing resource scheduling strategy, such as optimizing node resource utilization or balance rate and minimizing resource usage cost, etc.; it does not have a special design for scheduling based on user resource preferences and cannot meet the diverse needs of users. Summary of the invention

[0006] The purpose of the present invention is to overcome the problem that in the existing computing power resource scheduling method, the computing power resource status information is difficult to synchronize quickly in the scenario of high dynamic change of computing power resource status, resulting in inefficient resource utilization and scheduling failure, and there is no computing power resource scheduling design based on user resource preferences, resulting in the inability to meet the diverse user needs, thereby providing a distributed computing power resource scheduling method.

[0007] In order to solve the above technical problems, the distributed computing resource scheduling method provided by the technical solution of the present invention is implemented based on distributed computing routing nodes, computing resource access nodes and computing resource nodes, and includes the following steps:

[0008] Step 1) The computing power routing node queries the computing power resource service identifier in the computing power resource request message in the mapping relationship between the computing power resource node resource status and the forwarding node maintained locally to determine a group of candidate computing power resource nodes;

[0009] Step 2) The computing power routing node determines the corresponding computing power matching strategy based on the resource requirement preference field in the computing power resource request message; based on the corresponding computing power matching strategy and the computing power resource requirement field and the network resource requirement field in the computing power resource request message, selects a computing power resource node that meets the user's resource requirements from the group of candidate computing power resource nodes;

[0010] Step 3) The computing power routing node modifies the destination address of the computing power resource request message to the next hop forwarding address of the computing power resource request message corresponding to the selected computing power resource node, and responds to the user through the computing power resource access node with the modified computing power resource request message. The user accesses the selected computing power resource node to call the relevant computing power resources.

[0011] As an improvement of the above method, in step 1), the computing power resource service identifier in the computing power resource request message is used to identify the type of the computing power resource node, wherein one computing power resource service identifier corresponds to a group of computing power resource nodes of the same type; the types of computing power resource nodes include: stand-alone computing power nodes, edge cluster computing power nodes, cloud data center computing power nodes and supercomputing center computing power nodes.

[0012] As an improvement of the above method, the specific components of the mapping relationship in step 1) include: a computing resource service identifier, a computing resource node resource status, and a next-hop forwarding address of a computing resource request message corresponding to the computing resource node;

[0013] The mapping relationship is generated in a distributed manner by classifying each computing resource node in units of computing resource service identifiers through the computing resource routing node based on the computing resource notification messages received from the computing resource access nodes and the locally perceived network status.

[0014] As an improvement of the above method, step 1) specifically includes: the computing power routing node selects a group of candidate computing power resource nodes by comparing the computing power resource service identifier in the computing power resource request message with the computing power resource service identifier in the mapping relationship.

[0015] As an improvement of the above method, the step 2) specifically includes:

[0016] The computing power routing node determines a corresponding computing power matching strategy based on a resource demand preference field in the computing power resource request message;

[0017] The computing power routing node compares the computing power resource requirement field and the network resource requirement field in the computing power resource request message with the resource status of the computing power resource node in the mapping relationship based on the corresponding computing power matching strategy, and selects a computing power resource node that meets the user's resource requirements.

[0018] As an improvement of the above method, in step 2),

[0019] The resource requirement preference field in the computing resource request message is used to identify the user's preference when selecting resources, including: load balancing priority, CPU priority, memory priority, storage priority, price priority and distance priority;

[0020] The computing resource requirement field in the computing resource request message is used to identify the computing resource requirement, including: CPU requirement, memory requirement and storage requirement; the method of identifying the computing resource requirement includes: using a level identifier;

[0021] The network resource requirement field in the computing power resource request message is used to identify the network resource requirement, including: latency requirement and bandwidth requirement; the method of identifying the network resource requirement includes: using a level identifier.

[0022] As an improvement of the above method, the computing power matching strategy in step 2) includes: load balancing priority matching, CPU priority matching, memory priority matching, storage priority matching, price priority matching and distance priority matching.

[0023] To achieve another object of the present invention, the present invention further provides a distributed computing resource scheduling system, the system comprising: distributed computing routing nodes, computing resource access nodes and computing resource nodes, wherein:

[0024] The computing power routing node is used to query the computing power resource service identifier in the computing power resource request message in the mapping relationship between the computing power resource node resource status and the forwarding node maintained locally to determine a group of candidate computing power resource nodes; to determine the corresponding computing power matching strategy based on the resource requirement preference field in the computing power resource request message; to select a computing power resource node that meets the user's resource requirements from the group of candidate computing power resource nodes based on the corresponding computing power matching strategy and the computing power resource requirement field and the network resource requirement field in the computing power resource request message; to modify the destination address of the computing power resource request message to the next hop forwarding address of the computing power resource request message corresponding to the selected computing power resource node, and also to respond to the modified computing power resource request message to the user through the computing power resource access node;

[0025] The user accesses the selected computing resource node to call the relevant computing resources.

[0026] As an improvement of the above system, the computing power routing node is also used to classify each computing power resource node in units of computing power resource service identifiers based on the computing power resource notification messages received from the computing power resource access nodes and the locally perceived network status, and to generate the mapping relationship in a distributed manner.

[0027] Compared with the prior art, the distributed computing resource scheduling method provided by the present invention has the following advantages:

[0028] 1. The present invention proposes a distributed computing resource scheduling method, which improves scalability and reliability compared with centralized scheduling.

[0029] 2. The present invention utilizes the distributed scheduling capabilities of computing power routing nodes that integrate forwarding, storage, and computing, so that during the routing process of computing power resource request messages, the scheduling purpose can be timely adjusted according to real-time computing power resource status information, thereby realizing flexible scheduling of computing power resources and improving the utilization rate, balance rate, and success rate of resource scheduling of computing power resources.

[0030] 3. The present invention designs a variety of computing power matching strategies based on the resource demand preferences of various users; in the process of distributed routing scheduling, each computing power routing node implements intelligent decision-making and forwarding based on the preference field in the resource request message header, thereby meeting the diverse needs of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A schematic diagram of a distributed computing resource scheduling method provided by an embodiment of the present invention;

[0032] Figure 2 A flowchart of a distributed computing resource scheduling method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0034] Example 1

[0035] like Figure 1 As shown, this embodiment provides a distributed computing power resource scheduling method, which is implemented based on widely distributed computing power routing nodes, computing power resource access nodes and computing power resource nodes.

[0036] like Figure 2 As shown, the method provided in this embodiment includes the following steps:

[0037] The computing power routing node receives the computing power resource request message, reads the computing power resource service identifier, computing power resource requirement, and network resource requirement fields in the message, queries the mapping relationship between the locally maintained resource status and the message forwarding node, and determines a group of candidate forwarding nodes;

[0038] Based on the resource demand preference carried in the message, the corresponding computing power matching strategy is determined, and a forwarding node that meets the user's resource demand is selected based on the strategy;

[0039] Modify the destination address of the computing resource request message to the network address of the forwarding node and forward the message.

[0040] Preferably, in addition to basic routing and forwarding capabilities, the computing power routing node also has functions including but not limited to: reading, filling in and modifying relevant fields in the message header, and the modifiable fields include but are not limited to: destination address; based on the received computing power resource notification message, distributed generation and maintenance of the mapping relationship between resource status and message forwarding nodes; the implementation architecture of the computing power routing node includes but is not limited to: implementation based on programmable switches and locally deployed controllers.

[0041] Preferably, the functions of the computing power resource access node include but are not limited to: sensing, monitoring and managing the resource status of the computing power resource node, generating a computing power resource service identifier, generating a computing power resource notification message based on the computing power resource service identifier and computing power resource status of the resources managed by this node, and notifying the computing power routing node.

[0042] Preferably, the computing power resource node types include but are not limited to: stand-alone computing power nodes, edge cluster computing power nodes, cloud data center computing power nodes, and supercomputing center computing power nodes.

[0043] Preferably, the computing power resource service identifier in the computing power resource request message header is used to represent a group of computing power resources with the same scale. Computing power resources of different scales use different computing power resource service identifiers. The method of dividing the computing power resource scale includes but is not limited to: single-machine computing power, edge cluster computing power, cloud data center computing power, and supercomputing center computing power.

[0044] Preferably, the computing power resource requirement field in the computing power resource request message header is used to identify specific computing power resource requirements, including but not limited to: CPU requirements, memory requirements, storage requirements; methods for identifying requirements include but are not limited to: using a level identifier.

[0045] Preferably, the network resource field in the computing power resource request message header is used to identify specific network resource requirements, including but not limited to: latency requirements, bandwidth requirements; methods for identifying requirements include but are not limited to: using a level identifier.

[0046] Preferably, the resource requirement preference field in the computing power resource request message header is used to identify the user's specific preferences when selecting resources, including but not limited to: load balancing priority, CPU priority, memory priority, storage priority, price priority, and distance priority.

[0047] Preferably, the specific components of the locally maintained mapping relationship between resource status and message forwarding nodes include but are not limited to: computing power resource service identifier, computing power resource node resource status, and the next-hop forwarding address of the message corresponding to the node; each computing power resource service identifier corresponds to a group of computing power resource nodes of the identifier type and their status and address information. Based on the resource notification message it receives and the locally perceived network status, the computing power routing node classifies each computing power resource node in units of computing power resource service identifiers and generates a mapping relationship. The computing power routing node compares the computing power resource service identifier in the resource request message with the identification field in the mapping relationship to find a group of candidate computing power resource nodes; based on the computing power matching strategy corresponding to the demand preference in the message, compares the resource demand in the message with the node resource status in the mapping relationship, and finds a message next-hop forwarding node address that meets the resource demand. The mapping relationship implementation method includes but is not limited to: implementation based on the flow table method.

[0048] Preferably, the computing power matching strategy is used to select a forwarding node from a group of candidate forwarding nodes, and the optional strategies include but are not limited to: load balancing priority matching, CPU priority matching, memory priority matching, storage priority matching, price priority matching, and distance priority matching.

[0049] The computing power routing node in this embodiment is implemented based on a programmable switch and a controller deployed locally. In addition to the basic routing and forwarding functions, it also has the following functions:

[0050] (1) Read, fill in and modify relevant fields of passing messages;

[0051] (2) receiving and processing the status information of the computing resource nodes reported by the computing resource access nodes;

[0052] (3) Notify the status information of the local computing resource node to other computing routing nodes, and receive notification messages from the computing resource nodes of other computing routing nodes;

[0053] (4) Perceive network status information between other computing power routing nodes;

[0054] (5) Based on the notification messages received from the computing resource nodes and the perceived network status information, a resource scheduling status information table is formed; based on the resource scheduling status information table, a mapping relationship between resource status and message forwarding nodes is generated;

[0055] The resource scheduling status information table contains the relevant status and address information of each computing power routing node, including: computing power resource service identifier, forwarding address information corresponding to the computing power resource node, number of available CPU cores, available memory size, available disk size, CPU utilization, memory utilization, disk utilization, communication delay and bandwidth between computing power routing nodes.

[0056] The computing resource access node in this embodiment has the following functions:

[0057] (1) Sense, monitor and manage the resource status of computing resource nodes and generate computing resource service identifiers, and report the resource status information and computing resource service identifiers of computing resource nodes to computing routing nodes;

[0058] (2) Providing user access and computing resource access functions;

[0059] The types of computing resource nodes in this embodiment include: stand-alone computing nodes, edge cluster computing nodes, cloud data center computing nodes, and supercomputing center computing nodes, each with a different computing resource service identifier. Among them, stand-alone computing nodes only provide computing resources and have no organizational structure between each other; edge cluster computing nodes, cloud data center computing nodes, and supercomputing center computing nodes have native orchestration capabilities and do not need to care about details such as cluster internal scheduling and management.

[0060] The computing resource request message in this embodiment is implemented based on a self-designed protocol stack. The computing resource requirement field and the network resource requirement field in the message header use levels to identify the specific resource requirement size; among them, the computing resource requirement field includes: CPU requirement level, memory requirement level, and storage requirement level, each occupying 4 bits and divided into 16 levels; the network resource requirement field includes: latency requirement level and bandwidth requirement level, each occupying 2 bits and divided into 4 levels; the corresponding relationship between the level and the size of the specific requirement is as follows:

[0061] Requirement Type (Unit) Requirement level Specific requirements for the corresponding level Occupied bits CPU requirements (cores) i(0-15) <![CDATA[(floor(2 i-1 ),2 i ]]]> 4 Memory requirement (G) j(0-15) <![CDATA[(floor(2 j-1 ),2 j ]]]> 4 Storage requirements (G) k(0-15) <![CDATA[(floor(2 k-1 ),2 k ]]]> 4 Bandwidth requirement (M) p(0-3) <![CDATA[(floor(2 p-1 ),2 p ]]]> 2 Delay requirement (ms) 0-3 <50ms / <200ms / <500ms / unlimited 2

[0062] The resource requirement preference field in the message header occupies a total of 8 bits. The specific options include: load balancing priority, CPU priority, memory priority, storage priority, price priority and distance priority. The corresponding relationship between the fields is as follows:

[0063]

[0064] In the embodiment, the mapping relationship between the resource status maintained locally by the computing power routing node and the message forwarding node is implemented by the computing power routing flow table. The computing power routing flow table is composed of a matching domain and an instruction block. The matching domain content includes: computing power resource service identification and resource demand preference; the instruction block content includes: computing power resource node resource status and the next hop forwarding address of the message corresponding to the computing power resource node, and the executable instructions include: resource demand level comparison jump instruction, message field modification instruction and flow table operation instruction; that is, in the computing power routing flow table, the combination of different computing power resource service identification and resource demand preference corresponds to a group of candidate computing power resource nodes and their related status and address information.

[0065] The computing power routing node classifies each computing power resource node based on the computing power resource service identifier based on the resource scheduling status information table it maintains; based on the computing power resource node evaluation function corresponding to each resource demand preference, each computing power resource node is calculated and sorted to form different computing power resource node sequences under different computing power resource service identifiers and resource demand preference combinations, thereby generating a computing power routing flow table.

[0066] The evaluation indicators of the computing resource node evaluation function include: load balancing index, CPU size, memory size, storage size, price and distance.

[0067] In this embodiment, the computing resource node evaluation function based on the load balancing indicator is specifically implemented as follows:

[0068]

[0069] BalancedResourseScore=(1-σ(CoreUsage,MemUsage,StoreUsage))*10

[0070] NodeScore=(Leas RequestedScore+BalancedResourseScore) / 2

[0071] Among them, LeastRequestedScore represents the resource load score, BalancedResourseScore represents the resource balance score, CoreUsage represents CPU utilization, MemUsage represents memory utilization, and StoreUsage represents disk utilization; NodeScore represents the node score. The higher the node score, the better the load balancing index of the computing resource node.

[0072] In this embodiment, the computing power routing node matches the computing power resource service identifier and resource demand preference combination in the computing power resource request message with the computing power resource service identifier and resource demand preference combination in the matching domain of the computing power routing flow table to find a group of candidate computing power resource nodes; in the instruction block of the computing power routing flow table, based on the computing power matching strategy corresponding to the resource demand preference, executes the resource demand level comparison jump instruction, compares the resource demand in the message with the computing power resource node resource status in the computing power routing flow table in turn, finds a message next hop forwarding node that meets the resource demand, and modifies the destination field of the message for forwarding. The computing power matching strategy corresponds to the resource demand preference and the computing power resource node evaluation function one by one, and is used to select a forwarding node from a group of candidate forwarding nodes. The optional strategies include: load balancing priority matching, CPU priority matching, memory priority matching, storage priority matching, price priority matching, and distance priority matching.

[0073] like Figure 2 As shown, the specific steps of the distributed computing resource scheduling method provided in this embodiment are summarized as follows:

[0074] (1) The computing power routing node maintains the resource status and network status information of each computing power resource node in its domain.

[0075] (2) The computing power routing node calculates and generates a mapping relationship between the resource status and the message forwarding node based on the resource status and network status information of the computing power resource node.

[0076] (3) The user sends a computing resource request message to the computing routing node.

[0077] (4) The computing power routing node receives the computing power resource request message, reads the computing power resource service identifier, computing power resource requirement, and network resource requirement fields in the computing power resource request message, queries the mapping relationship between the locally maintained resource status and the message forwarding node, and determines a group of candidate forwarding nodes.

[0078] (5) The computing power routing node determines the corresponding computing power matching strategy based on the resource demand preference carried in the computing power resource request message, and selects a forwarding node that meets the user's resource demand based on the computing power matching strategy.

[0079] (6) The computing power routing node modifies the destination address of the computing power resource request message as the network address of the forwarding node and forwards the message.

[0080] (7) Repeat steps 4 to 6 until the computing resource request message is forwarded to a computing resource access node that meets the user's resource requirements.

[0081] (8) The computing resource access node receives the computing resource request message and returns a response message to the user.

[0082] (9) The user accesses the computing resource node according to the relevant information contained in the response message and calls the relevant computing resources.

[0083] Example 2

[0084] This embodiment provides a distributed computing resource scheduling system. Figure 1 As shown, the system includes: distributed computing power routing nodes, computing power resource access nodes and computing power resource nodes;

[0085] like Figure 2As shown, the computing power routing node is used to query the computing power resource service identifier in the computing power resource request message in the mapping relationship between the computing power resource node resource status and the forwarding node maintained locally to determine a group of candidate computing power resource nodes; based on the resource requirement preference field in the computing power resource request message, determine the corresponding computing power matching strategy; based on the corresponding computing power matching strategy and the computing power resource requirement field and the network resource requirement field in the computing power resource request message, select a computing power resource node that meets the user's resource requirements from the group of candidate computing power resource nodes; used to modify the destination address of the computing power resource request message to the next hop forwarding address of the selected computing power resource request message, and also used to respond to the modified computing power resource request message to the user through the computing power resource access node; the user accesses the selected computing power resource node to call the relevant computing power resources. Preferably, the computing power routing node is also used to classify each computing power resource node based on the computing power resource notification message received from the computing power resource access node and the locally perceived network status, and generate the mapping relationship in a distributed manner.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that any modification or equivalent replacement of the technical solutions of the present invention does not depart from the spirit and scope of the technical solutions of the present invention and should be included in the scope of the claims of the present invention.

Claims

1. A distributed computing resource scheduling method, based on distributed computing routing nodes, computing resource access nodes and computing resource nodes, comprises the following steps: Step 1) The computing power routing node queries the computing power resource service identifier in the computing power resource request message in the mapping relationship between the computing power resource node resource status and the forwarding node maintained locally to determine a group of candidate computing power resource nodes; Step 2) The computing power routing node determines the corresponding computing power matching strategy based on the resource requirement preference field in the computing power resource request message; based on the corresponding computing power matching strategy and the computing power resource requirement field and the network resource requirement field in the computing power resource request message, selects a computing power resource node that meets the user's resource requirements from the group of candidate computing power resource nodes; Step 3) The computing power routing node modifies the destination address of the computing power resource request message to the next hop forwarding address of the computing power resource request message corresponding to the selected computing power resource node, and responds to the user through the computing power resource access node with the modified computing power resource request message. The user accesses the selected computing power resource node to call the relevant computing power resources.

2. The distributed computing resource scheduling method according to claim 1, characterized in that: In the step 1), The computing power resource service identifier in the computing power resource request message is used to identify the type of the computing power resource node, wherein one computing power resource service identifier corresponds to a group of computing power resource nodes of the same type; the types of computing power resource nodes include: stand-alone computing power nodes, edge cluster computing power nodes, cloud data center computing power nodes and supercomputing center computing power nodes.

3. The distributed computing resource scheduling method according to claim 1, characterized in that: The specific components of the mapping relationship in step 1) include: a computing resource service identifier, a computing resource node resource status, and a next-hop forwarding address of a computing resource request message corresponding to the computing resource node; The mapping relationship is generated in a distributed manner by classifying each computing resource node in units of computing resource service identifiers through the computing resource routing node based on the computing resource notification messages received from the computing resource access nodes and the locally perceived network status.

4. The distributed computing resource scheduling method according to claim 3 is characterized in that: The step 1) specifically includes: the computing power routing node selects a group of candidate computing power resource nodes by comparing the computing power resource service identifier in the computing power resource request message with the computing power resource service identifier in the mapping relationship.

5. The distributed computing resource scheduling method according to claim 4, characterized in that: The step 2) specifically includes: The computing power routing node determines a corresponding computing power matching strategy based on a resource demand preference field in the computing power resource request message; The computing power routing node compares the computing power resource requirement field and the network resource requirement field in the computing power resource request message with the resource status of the computing power resource node in the mapping relationship based on the corresponding computing power matching strategy, and selects a computing power resource node that meets the user's resource requirements.

6. The distributed computing resource scheduling method according to claim 1 or 5, characterized in that: In the step 2), The resource requirement preference field in the computing resource request message is used to identify the user's preference when selecting resources, including: load balancing priority, CPU priority, memory priority, storage priority, price priority and distance priority; The computing resource requirement field in the computing resource request message is used to identify the computing resource requirement, including: CPU requirement, memory requirement and storage requirement; the method of identifying the computing resource requirement includes: using a level identifier; The network resource requirement field in the computing power resource request message is used to identify the network resource requirement, including: latency requirement and bandwidth requirement; the method of identifying the network resource requirement includes: using a level identifier.

7. The distributed computing resource scheduling method according to claim 1 or 5, characterized in that: The computing power matching strategies in step 2) include: load balancing priority matching, CPU priority matching, memory priority matching, storage priority matching, price priority matching and distance priority matching.

8. A distributed computing resource scheduling system, characterized in that: The system includes: distributed computing power routing nodes, computing power resource access nodes and computing power resource nodes, wherein: The computing power routing node is used to query the computing power resource service identifier in the computing power resource request message in the mapping relationship between the computing power resource node resource status and the forwarding node maintained locally to determine a group of candidate computing power resource nodes; to determine the corresponding computing power matching strategy based on the resource requirement preference field in the computing power resource request message; to select a computing power resource node that meets the user's resource requirements from the group of candidate computing power resource nodes based on the corresponding computing power matching strategy and the computing power resource requirement field and the network resource requirement field in the computing power resource request message; to modify the destination address of the computing power resource request message to the next hop forwarding address of the computing power resource request message corresponding to the selected computing power resource node, and also to respond to the modified computing power resource request message to the user through the computing power resource access node; The user accesses the selected computing resource node to call the relevant computing resources.

9. The distributed computing resource scheduling system according to claim 8, characterized in that: The computing power routing node is also used to classify each computing power resource node in units of computing power resource service identifiers based on the computing power resource notification messages received from the computing power resource access nodes and the locally perceived network status, and to generate the mapping relationship in a distributed manner.

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