A distributed computing resource collaborative deployment method based on multi-scale time delay constraint

Abstract

The application discloses a kind of distributed computing power resource collaborative deployment methods based on multi-scale time delay constraint, comprising: establishing the service queue to be served of each business master station at the beginning of working cycle;Establish the network topology relationship between each business master station, and define routing constraints;For the given computing power deployment scheme and path planning, determine the expected service total time delay of each business, and the expected service total time delay includes business transmission time delay and business computing time delay;Based on expected service total time delay, establish service completion rate constraints;According to the total computing power resource available amount, establish computing power allocation constraints;For the given computing power deployment scheme and path planning, determine the business computing cost and business transmission cost of each business master station, and calculate total cost;With the minimum cumulative cost as the goal, a linear programming problem is established;Solve linear programming problem, obtain optimal computing power deployment scheme, and execute computing power resource deployment according to optimal scheme.

Description

Technical Field

[0001] This invention relates to the fields of communication technology and distributed computing resource management technology, specifically to a method for collaborative deployment of distributed computing resources based on multi-scale latency constraints. Background Technology

[0002] With the rapid development of the Internet of Things (IoT), edge computing, and the Industrial Internet, business demands are becoming increasingly diverse and dynamic, placing higher demands on the real-time performance and reliability of computing services. In a distributed network comprised of multiple business master stations, a key technical challenge has emerged: how to dynamically and efficiently deploy limited total computing resources to various nodes to meet the stringent constraints of different businesses on latency (such as millisecond-level and second-level requirements) and completion rates, while simultaneously controlling the overall operating cost of the system.

[0003] Existing computing resource allocation methods mostly focus on centralized scheduling or static configuration, lacking comprehensive consideration of network topology, routing hop count, data transmission, and computation latency. This leads to a situation where some nodes have excess computing power while others have insufficient computing power under dynamic business loads, failing to guarantee the low latency requirements of critical services and making it difficult to achieve optimal cost at the global level. Furthermore, traditional methods typically treat latency constraints as a single metric, failing to effectively differentiate the multi-scale latency requirements of services, and are ill-suited to the refined resource management needs of complex heterogeneous network environments.

[0004] Therefore, there is an urgent need in this field for a computing resource deployment method that can synergistically consider multi-scale latency constraints, network routing status, and global cost-effectiveness in order to improve the overall service quality and resource utilization efficiency of distributed computing systems. Summary of the Invention

[0005] To achieve the objective of this invention, this application provides a method for collaborative deployment of distributed computing resources based on multi-scale latency constraints, comprising: Step S1: Establish the queue of pending services for each business master station at the beginning of the work cycle; Step S2: Establish the network topology relationship between each business master station and define routing constraints; Step S3: For a given computing power deployment scheme and path planning, determine the expected total service latency for each service, which includes service transmission latency and service computing latency; Step S4: Based on the expected total service latency, establish a business completion rate constraint; Step S5: Establish computing power allocation constraints based on the total available computing power resources; Step S6: For a given computing power deployment scheme and path planning, determine the business computing cost and business transmission cost of each business master station, and calculate the total cost; Step S7: With the goal of minimizing cumulative cost, establish a linear programming problem based on the routing constraints, business completion rate constraints, and computing power allocation constraints; Step S8: Solve the linear programming problem to obtain the optimal computing power deployment scheme, and execute the computing power resource deployment according to the optimal computing power deployment scheme.

[0006] In some specific embodiments, the queue of services to be served in step S1 is determined according to the following formula: Where z is the main business station number, For the set of all main site IDs, The number of pending services on the main site. The first of the main site z Each business.

[0007] In some specific embodiments, the routing constraints in step S2 are determined according to the following formula: in, As a decision variable, if the main station i and the main site j If directly connected, then Conversely, it is 0. Main site i and the main site j Number of jumps between, N This is a constant that limits the number of jumps.

[0008] In some specific embodiments, the service transmission delay in step S3 is determined according to the following formula: in, Main site z business Corresponding data volume Main site i To the main station j Given a path, including multi-hop links excluding the target master station j All main station numbers outside of [the main station] for The relay master station involved in the middle v The historical average bandwidth of its next hop. Main site i and the main site j Number of jumps between intervals.

[0009] In some specific embodiments, the service computation latency in step S3 is determined according to the following formula: in, For computing services k The number of CPU cycles required for one bit of data. D i,k Main site i Chinese business k Corresponding data volume Main site j Assigned to the main site i No. k Computing resources for each business.

[0010] In some specific embodiments, the business completion rate constraint in step S4 is determined according to the following formula: in, This represents the minimum business completion rate over a work cycle. θ The business completion rate within a business cycle T; The business completion rate within a business cycle T is: in, This refers to the volume of business completed by the main business station z within a business cycle. The value can be: in, As decision variables, The value can be: in, d i,j,k This represents the total expected latency for the business.

[0011] In some specific embodiments, the computing power allocation constraint in step S5 is determined according to the following formula: in, To deploy on the main business site's computing resources, Total deployable computing power resources.

[0012] In some specific embodiments, the business computing cost in step S6 is determined according to the following formula: in, To determine the unit computing resource cost of deployment, This is for deploying computing resources on the main business site.

[0013] In some specific embodiments, the objective function of the linear programming problem in step S7 is: In some specific embodiments, in step S8, the interior point method or genetic algorithm is used to solve the linear programming problem.

[0014] The beneficial effects of the above technical solution are as follows: Compared with existing technologies, the distributed computing resource collaborative deployment method based on multi-scale latency requirement constraints provided by this invention has the following significant advantages: (1) This application establishes a comprehensive constraint model that includes service transmission delay, computing delay and service completion rate, and optimizes under the model with the goal of minimizing total cost. Under the premise of strictly ensuring the multi-scale delay requirements and completion rate of various services, this invention can achieve efficient utilization of total computing power resources and effectively avoid over-configuration or under-configuration of resources.

[0015] (2) This application incorporates network topology, routing hop count constraints, computing power allocation, and business costs into a unified linear programming framework for collaborative optimization. This global optimization perspective overcomes the limitations of local decision-making and can derive the optimal or near-optimal computing power deployment scheme at the system level, thereby significantly reducing the overall operating cost of the system.

[0016] (3) This application considers the historical average bandwidth and dynamic service queues along the path, enabling the computing power deployment scheme to adapt to changes in network status and service load. By meeting the preset service completion rate constraints, the reliability and stability of the system service quality are ensured, making it particularly suitable for application scenarios with strict requirements for service level agreements. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for collaborative deployment of distributed computing resources based on multi-scale latency constraints, provided as an embodiment of the present invention; Figure 2 This is a network topology diagram illustrating a distributed computing resource collaborative deployment method based on multi-scale latency constraints, as provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar symbols denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the invention, and should not be construed as limiting the invention.

[0021] Example 1 One embodiment of the present invention provides a method for collaborative deployment of distributed computing resources based on multi-scale latency constraints, referring to... Figure 1 As shown, it includes: Step S1: Establish the queue of pending services for each business master station at the beginning of the work cycle; In a specific embodiment of the present invention, the service queue to be served in step S1 is determined according to the following formula: Where z is the main business station number, For the set of all main site IDs, The number of pending services on the main site. The first of the main site z Each business.

[0022] Step S2: Refer to Figure 2 As shown, establish the network topology relationship between each business master station and define routing constraints; In a specific embodiment of the present invention, the routing constraint in step S2 is determined according to the following formula: in, As a decision variable, if the main station i and the main site j If directly connected, then Conversely, it is 0. Main site i and the main site j Number of jumps between, N This is a constant that limits the number of jumps.

[0023] Step S3: For a given computing power deployment scheme and path planning, determine the expected total service latency for each service, which includes service transmission latency and service computing latency; In a specific embodiment of the present invention, the service transmission delay in step S3 is determined according to the following formula: in, Main site z business Corresponding data volume Main site i To the main stationj Given a path, including multi-hop links excluding the target master station j All main station numbers outside of [the main station] for The relay master station involved in the middle v The historical average bandwidth of its next hop. Main site i and the main site j Number of jumps between intervals.

[0024] In a specific embodiment of the present invention, the service calculation delay in step S3 is determined according to the following formula: in, For computing services k The number of CPU cycles required for one bit of data. D i,k Main site i Chinese business k Corresponding data volume Main site j Assigned to the main site i No. k Computing resources for each business.

[0025] Step S4: Based on the expected total service latency, establish a business completion rate constraint; In a specific embodiment of the present invention, the business completion rate constraint in step S4 is determined according to the following formula: in, This represents the minimum business completion rate over a work cycle. θ The business completion rate within a business cycle T; The business completion rate within a business cycle T is: in, This refers to the volume of business completed by the main business station z within a business cycle. The value can be: in, As decision variables, The value can be: in, d i,j,k This represents the total expected latency for the business.

[0026] Step S5: Establish computing power allocation constraints based on the total available computing power resources; In a specific embodiment of the present invention, the computing power allocation constraint in step S5 is determined according to the following formula: in, To deploy on the main business site's computing resources, Total deployable computing power resources.

[0027] Step S6: For a given computing power deployment scheme and path planning, determine the business computing cost and business transmission cost of each business master station, and calculate the total cost based on this; In a specific embodiment of the present invention, the business calculation cost in step S6 is determined according to the following formula: in, To determine the unit computing resource cost of deployment, This is for deploying computing resources on the main business site.

[0028] Step S7: With the goal of minimizing cumulative cost, establish a linear programming problem, which is constrained by the routing constraints, business completion rate constraints, and computing power allocation constraints; In a specific embodiment of the present invention, the objective function of the linear programming problem in step S7 is: Step S8: Solve the linear programming problem to obtain the optimal computing power deployment scheme, and execute the computing power resource deployment according to the optimal computing power deployment scheme.

[0029] In a specific embodiment of the present invention, in step S8, the linear programming problem is solved using the interior point method or a genetic algorithm.

[0030] This application establishes a comprehensive constraint model that includes service transmission latency, computing latency, and service completion rate, and optimizes under this model with the goal of minimizing total cost. Under this model, the invention can achieve efficient utilization of total computing power resources while strictly ensuring the multi-scale latency requirements and completion rates of various services, effectively avoiding over-configuration or under-configuration of resources.

[0031] This invention integrates network topology, routing hop count constraints, computing power allocation, and service costs into a unified linear programming framework for collaborative optimization. This global optimization perspective overcomes the limitations of local decision-making, enabling the determination of optimal or near-optimal computing power deployment schemes at the system level, thereby significantly reducing the overall operating costs of the system.

[0032] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0033] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the functions specified in one or more boxes. Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the invention. Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0034] The methods and apparatus provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

[0035] In the description of this specification, references to terms such as "an embodiment," "some embodiments," "example," "specific example," or "a specific embodiment" or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for collaborative deployment of distributed computing resources based on multi-scale latency constraints, characterized in that, include: Step S1: Determine the queue of pending services at the beginning of each business master station's work cycle; Step S2: Establish the network topology relationship between each business master station and define routing constraints; Step S3: For a given computing power deployment scheme and path planning, determine the expected total service latency for each service, which includes service transmission latency and service computing latency; Step S4: Based on the expected total service latency, establish a business completion rate constraint; Step S5: Establish computing power allocation constraints based on the total available computing power resources; Step S6: For a given computing power deployment scheme and path planning, determine the business computing cost and business transmission cost of each business master station, and calculate the total cost; Step S7: With the goal of minimizing cumulative cost, establish a linear programming problem based on the routing constraints, business completion rate constraints, and computing power allocation constraints; Step S8: Solve the linear programming problem to obtain the optimal computing power deployment scheme, and execute the computing power resource deployment according to the optimal computing power deployment scheme; The queue of services to be served in step S1 is determined according to the following formula: Where z is the main business station number, For the set of all main site IDs, The number of pending services on the main site. The first of the main site z One business; The routing constraints in step S2 are determined according to the following formula: in, As a decision variable, if the main station i and the main site j If directly connected, then Conversely, it is 0. Main site i and the main site j Number of jumps between, N A constant that limits the number of hops; The business completion rate constraint mentioned in step S4 is determined according to the following formula: in, This represents the minimum business completion rate over a work cycle. θ The business completion rate within a business cycle T; The business completion rate within a business cycle T is: in, This refers to the volume of business completed by the main business station z within a business cycle. The value can be: in, As decision variables, The value can be: in, d i,j,k This represents the total expected latency for the business. The computing power allocation constraint mentioned in step S5 is determined according to the following formula: in, To deploy on the main business site's computing resources, Total deployable computing power resources.

2. The method for collaborative deployment of distributed computing resources based on multi-scale delay constraints according to claim 1, characterized in that, The service transmission delay in step S3 is determined according to the following formula: in, Main site z business Corresponding data volume Main site i To the main station j Given a path, including multi-hop links excluding the target master station j All main station numbers outside of [the main station] for The relay master station involved in the middle v The historical average bandwidth of its next hop. Main site i and the main site j Number of jumps between intervals.

3. The method for collaborative deployment of distributed computing resources based on multi-scale delay constraints according to claim 1, characterized in that, The service calculation latency mentioned in step S3 is determined according to the following formula: in, For computing services k The number of CPU cycles required for one bit of data. D i,k Main site i Chinese business k Corresponding data volume Main site j Assigned to the main site i No. k Computing resources for each business.

4. The method for collaborative deployment of distributed computing resources based on multi-scale delay constraints according to claim 1, characterized in that, The business calculation cost mentioned in step S6 is determined according to the following formula: in, To determine the unit computing resource cost for deployment, This is for deploying computing resources on the main business site.

5. The method for collaborative deployment of distributed computing resources based on multi-scale delay constraints according to claim 1, characterized in that, The objective function of the linear programming problem described in step S7 is: 。 6. The method for collaborative deployment of distributed computing resources based on multi-scale delay constraints according to claim 1, characterized in that, In step S8, the linear programming problem is solved using the interior point method or a genetic algorithm.

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