A distributed asset task scheduling method

Abstract

The present application discloses a distributed asset task scheduling method, including default parameter setting-task splitting-task scheduling-task execution-execution confirmation, which can efficiently schedule and manage a large number of tasks and effectively prevent interference between tasks, and at the same time has higher fault tolerance and faster task response, high task execution efficiency, strong overall processing capability, and its implementation method is simple and easy to understand, and can be applied to various scenarios.

Description

Technical Field

[0001] The present application relates to a distributed asset task scheduling method, which belongs to the field of network asset detection. Background Art

[0002] With the rapid development of network technology, more and more enterprises and organizations deploy their businesses and services on the cloud or in the intranet. These assets and systems deployed on the network are the infrastructure that constitutes the entire cyberspace.

[0003] Among them, network assets, as the building blocks of cyberspace, are an indispensable part of cyberspace. With the continuous addition of services and equipment, the number of assets in cyberspace is becoming increasingly large, and problems such as slow asset updates, low asset identification accuracy, and slow identification and discovery are gradually emerging.

[0004] The main problem currently faced by the asset detection field is that with such a huge and wide range of assets, traditional detection task scheduling methods have problems such as slow detection, single point failure, performance bottlenecks and insufficient scalability, and cannot meet the needs of large-scale task processing. Summary of the invention

[0005] In order to solve the above-mentioned various defects of the traditional detection task scheduling method, according to one aspect of the present application, a distributed asset task scheduling method is provided, comprising the following steps:

[0006] S1: Default parameter settings. Setting the default parameters of the program according to the performance parameters of the worker node can give full play to the performance.

[0007] S2: Task splitting and allocation, reading the task data on each computing node, performing asset task splitting and load balancing, and allocating them to each computing node.

[0008] Through task splitting and load balancing algorithms, the task pressure of a single node can be effectively reduced, while fully utilizing the processing power of each computing node to improve task processing performance and efficiency.

[0009] S3: Task scheduling. Each computing node establishes a task scheduling graph and generates a task list based on task parameters, and designs a scheduling strategy.

[0010] By establishing a task scheduling graph and formulating corresponding scheduling strategies on each computing node, different task types can be optimized to improve processing efficiency.

[0011] S4: Task execution: each computing node traverses the task list and executes the task.

[0012] By generating a task linked list, the orderliness and correctness of tasks can be guaranteed, thus avoiding interference between tasks.

[0013] S5: Execution confirmation. Each asset task is confirmed by the Broker after being executed. By confirming the task execution results, it is guaranteed that the task can be successfully executed.

[0014] Optionally, in step S1, the performance parameters include at least physical performance parameters of the worker node.

[0015] Optionally, in step S1, the default parameters include at least one of the number of concurrent tasks, the timeout waiting time, and the number of packets sent.

[0016] Optionally, in step S2, the tasks are split according to at least one of the following factors: task type, number of task targets, task target network conditions, computer node configuration, and computer node real-time performance.

[0017] Optionally, in step S2, the split tasks are specifically distributed to different computing nodes through a load balancing algorithm.

[0018] Optionally, in step S3, the scheduling strategy is optimized for a specific task type and takes into account the coordination and cooperation between various computing nodes.

[0019] Optionally, in step S3, the task parameter includes at least one of a task type and a task amount.

[0020] Optionally, in step S4, each computing node coordinates and synchronizes task completion status through a message middleware or a message queue.

[0021] Optionally, in step S4, a multi-threading mechanism is used during task execution to improve task execution efficiency.

[0022] Optionally, in step S5, if the task is confirmed to have failed or is abnormal, it will be consumed again by other workers, that is, there is no single point of failure. When a slave node fails, the system can automatically migrate the task to other nodes for execution to ensure the completion of the task.

[0023] The beneficial effects of this application include:

[0024] A distributed asset task scheduling method provided in the present application effectively reduces the task pressure of a single node through task splitting and allocation and load balancing, thereby improving task processing performance and efficiency; at the same time, the scheduling method provided in the present application can efficiently schedule and manage a large number of tasks, has higher fault tolerance, and can effectively prevent interference between tasks. It can make full use of the processing power of each computing node to achieve faster task response, and its implementation method is simple and easy to understand, and can be applied to various scenarios. DETAILED DESCRIPTION

[0025] The present application is described in detail below with reference to embodiments, but the present application is not limited to these embodiments.

[0026] According to an embodiment of the present application, a distributed asset task scheduling method is provided, comprising the following steps:

[0027] S1: Default parameter settings. Setting the default parameters of the program according to the performance parameters of the worker node can give full play to the performance.

[0028] The performance parameters are physical performance parameters of the worker node.

[0029] The default parameters are the number of concurrent tasks, timeout waiting time, and number of packets sent.

[0030] S2: Task splitting and allocation, reading the task data on each computing node, performing asset task splitting and load balancing, and allocating them to each computing node.

[0031] Through task splitting and load balancing algorithms, the task pressure of a single node can be effectively reduced, while fully utilizing the processing power of each computing node to improve task processing performance and efficiency.

[0032] Specifically: split the tasks according to the task type, the number of task targets, the task target network conditions, the computer node configuration and the computer node real-time performance factors;

[0033] The split tasks are allocated to different computing nodes through the load balancing algorithm.

[0034] S3: Task scheduling. Each computing node establishes a task scheduling graph and generates a task list based on task parameters, and designs a scheduling strategy.

[0035] By establishing a task scheduling graph and formulating corresponding scheduling strategies on each computing node, different task types can be optimized to improve processing efficiency.

[0036] The scheduling strategy is optimized for specific task types and takes into account the coordination and cooperation between various computing nodes.

[0037] The task parameters include task type and task amount.

[0038] S4: Task execution: each computing node traverses the task list and executes the task.

[0039] By generating a task linked list, the orderliness and correctness of tasks can be guaranteed, thus avoiding interference between tasks.

[0040] The task completion status is coordinated and synchronized between the computing nodes through the message middleware.

[0041] During task execution, a multi-threading mechanism is used to improve task execution efficiency.

[0042] S5: Execution confirmation. Each asset task is confirmed by the Broker after being executed. The task execution result is confirmed to ensure that the task can be successfully executed. If the task is confirmed to have failed or abnormal execution, it will be consumed again by other workers. In other words, there is no single point of failure. When a slave node fails, the system can automatically migrate the task to other nodes for execution to ensure the completion of the task.

[0043] The above description is only a partial embodiment of the present application and does not constitute any form of limitation to the present application. Although the present application is disclosed as above with preferred embodiments, it is not intended to limit the present application. Any technician familiar with the profession, without departing from the scope of the technical solution of the present application, using the above disclosed technical content to make slight changes or modifications are equivalent to equivalent implementation cases and fall within the scope of the technical solution.

Claims

1. A distributed asset task scheduling method, characterized in that: The steps include: S1: Default parameter setting, setting the default parameters of the program according to the performance parameters of the worker node; The default parameters include at least one of the number of concurrent tasks, timeout waiting time, and number of packets sent; S2: Task splitting and allocation, reading the task data on each computing node, splitting the asset tasks and load balancing, and assigning them to each computing node; Split the tasks according to the network conditions of the task targets, computer node configuration, and computer node real-time performance; Specifically, the split tasks are allocated to different computing nodes through a load balancing algorithm; S3: Task scheduling: Each computing node establishes a task scheduling graph and generates a task list based on task parameters, and designs a scheduling strategy; The scheduling strategy is optimized for specific task types and takes into account the coordination and cooperation between various computing nodes; The task parameter includes at least one of a task type and a task amount; S4: Task execution, each computing node traverses the task list and executes the task; S5: Execution confirmation. Each asset task is confirmed by the Broker after being executed.

2. A distributed asset task scheduling method according to claim 1, characterized in that: In the step S1, the performance parameters include at least the physical performance parameters of the worker node.

3. A distributed asset task scheduling method according to claim 1, characterized in that: In step S4, each computing node coordinates and synchronizes task completion status through message middleware or message queue.

4. A distributed asset task scheduling method according to claim 1, characterized in that: In step S4, a multi-threading mechanism is used during task execution to improve task execution efficiency.

5. A distributed asset task scheduling method according to claim 1, characterized in that: In step S5, if the task is confirmed to have failed or is abnormal, it will be consumed again by other workers.