System I / O queue dynamic intelligent control system

Abstract

A system I / O queue dynamic intelligent control system disclosed by the present invention comprises a data acquisition module, an intelligent analysis module, a decision control module and an execution module, the data acquisition module is responsible for collecting various performance data of the system, and the data acquisition module transmits the collected data to the intelligent analysis module; the intelligent analysis module carries out deep analysis on the collected data, evaluates the current system state and business requirements, and transmits a result to the decision control module after completing the analysis; the decision control module formulates a control strategy of the I / O queue according to the analysis result; the intelligent analysis algorithm adopted by the invention solves the problem of inaccurate judgment on the system state and the service demand in the prior art, and provides a basis for accurate control; a flexible strategy making rule engine overcomes the defects of fixed strategy and poor adaptability in the prior art; a closed-loop feedback mechanism overcomes the defect that real-time adjustment cannot be achieved in the prior art, and it is guaranteed that the system is always in the optimal state.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent control systems, and in particular relates to a system I / O queue dynamic intelligent control system. Background Art

[0002] In the field of large-scale cluster storage products, the I / O performance of the system is crucial to the efficiency and response speed of the overall system. With the continuous growth of data volume and the increasing complexity of business processing, the requirements for system I / O processing capabilities are getting higher and higher.

[0003] Related key technologies include I / O scheduling algorithms, cache management technologies, etc. The I / O scheduling algorithm determines the processing order of I / O requests to optimize disk access performance; cache management technology is used to increase data access speed and reduce direct access to the disk.

[0004] In the prior art, common system I / O queue management usually adopts a fixed priority strategy or a simple polling method. For example, some systems set the I / O requests of key businesses to high priority and other businesses to low priority, and process I / O requests in order of priority; The defects of the prior art are: poor flexibility: fixed priority settings cannot be dynamically adjusted according to the real-time load and resource usage of the system, which may cause high-priority tasks to wait for a long time in some cases, while low-priority tasks occupy too many resources.

[0005] Lack of adaptability: The simple polling method cannot effectively distinguish I / O requests of different types and urgency, and cannot adapt to complex and changing business scenarios.

[0006] Low resource utilization: The performance of the storage system cannot be fully utilized, which may cause storage resources to be idle or overused. For this reason, the present invention proposes a system I / O queue dynamic intelligent control system. Summary of the invention

[0007] The purpose of the present invention is to provide a system I / O queue dynamic intelligent control system to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions: a system I / O queue dynamic intelligent control system, comprising a data acquisition module, an intelligent analysis module, a decision control module and an execution module, wherein the data acquisition module is responsible for collecting various performance data of the system, and the data acquisition module transmits the collected data to the intelligent analysis module;

[0009] The intelligent analysis module performs in-depth analysis on the collected data to evaluate the current system status and business requirements. After completing the analysis, the intelligent analysis module passes the results to the decision control module;

[0010] The decision control module formulates a control strategy for the I / O queue based on the analysis results, generates the strategy and sends it to the execution module;

[0011] The execution module is responsible for implementing the strategy generated by the decision control module and adjusting the I / O queue, including changing the priority of the request and allocating resources.

[0012] Preferably, the data acquisition module adopts a distributed data acquisition architecture, and acquires accurate performance data in real time by deploying sensors at key nodes of the system, and the collected performance data includes CPU utilization, memory usage, and I / O load.

[0013] Preferably, the intelligent analysis module performs in-depth mining and analysis of data based on machine learning algorithms and data analysis models to determine the current system load status, resource usage, and business urgency.

[0014] Preferably, the decision control module has a flexible strategy formulation rule engine, which can generate customized control strategies according to different system states.

[0015] Preferably, the execution module adopts an efficient instruction execution mechanism to ensure the rapid and accurate implementation of the strategy.

[0016] Preferably, the data collection period of the data collection module is usually set between 1 and 10 seconds according to system performance and business requirements.

[0017] Preferably, the learning rate and regularization parameters in the machine learning algorithm of the intelligent analysis module need to be tuned according to actual data.

[0018] Preferably, a threshold is set for the policy adjustment, and the policy adjustment is triggered when the CPU utilization exceeds a set threshold of 80% or the I / O waiting time exceeds a set duration.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: the intelligent analysis algorithm adopted by the present invention solves the problem of inaccurate judgment of system status and business requirements in the prior art, and provides a basis for precise control;

[0020] The flexible policy-making rule engine overcomes the shortcomings of existing technologies, such as fixed policies and poor adaptability;

[0021] The closed-loop feedback mechanism improves the deficiency of existing technology that cannot be adjusted in real time, ensuring that the system is always in the optimal state. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0024] See also Figure 1 , provides a technical solution: a system I / O queue dynamic intelligent control system, including a data acquisition module, an intelligent analysis module, a decision control module and an execution module, the data acquisition module is responsible for collecting various performance data of the system, and the data acquisition module transmits the collected data to the intelligent analysis module;

[0025] The intelligent analysis module conducts in-depth analysis of the collected data to evaluate the current system status and business needs. After completing the analysis, the intelligent analysis module passes the results to the decision control module;

[0026] The decision control module formulates the control strategy of the I / O queue according to the analysis results. The decision control module generates the strategy and sends it to the execution module;

[0027] The execution module is responsible for implementing the strategies generated by the decision control module and adjusting the I / O queue, including changing the priority of requests and allocating resources.

[0028] In this embodiment, preferably, the data acquisition module adopts a distributed data acquisition architecture, and acquires accurate performance data in real time by deploying sensors at key nodes of the system, and the collected performance data includes CPU utilization, memory usage, and I / O load.

[0029] In this embodiment, preferably, the intelligent analysis module performs in-depth mining and analysis of data based on machine learning algorithms and data analysis models to determine the current system load status, resource usage, and business urgency.

[0030] In this embodiment, preferably, the decision control module has a flexible strategy formulation rule engine, which can generate customized control strategies according to different system states.

[0031] In this embodiment, preferably, the execution module adopts an efficient instruction execution mechanism to ensure the rapid and accurate implementation of the strategy.

[0032] In this embodiment, preferably, the data collection period of the data collection module is usually set between 1 and 10 seconds according to system performance and business requirements.

[0033] In this embodiment, preferably, the learning rate and regularization parameters in the machine learning algorithm of the intelligent analysis module need to be optimized according to actual data.

[0034] In this embodiment, preferably, a threshold is set for the policy adjustment, and the policy adjustment is triggered when the CPU utilization exceeds a set threshold of 80% or the I / O waiting time exceeds a set duration.

[0035] Step 1: The data acquisition module is started to periodically collect system performance data.

[0036] Step 2: The intelligent analysis module receives the data, performs analysis and processing, and generates a system status report.

[0037] Step 3: The decision control module formulates a control strategy based on the system status report.

[0038] Step 4: The execution module executes the control strategy and adjusts the I / O queue.

[0039] Step 5: The whole process is repeated continuously to realize dynamic intelligent control of the I / O queue.

[0040] Although embodiments of the present invention have been shown and described (see the above detailed description for details), it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A system I / O queue dynamic intelligent control system, characterized by: It includes a data acquisition module, an intelligent analysis module, a decision control module and an execution module. The data acquisition module is responsible for collecting various performance data of the system, and the data acquisition module transmits the collected data to the intelligent analysis module; The intelligent analysis module performs in-depth analysis on the collected data to evaluate the current system status and business requirements. After completing the analysis, the intelligent analysis module passes the results to the decision control module; The decision control module formulates a control strategy for the I / O queue based on the analysis results, generates the strategy and sends it to the execution module; The execution module is responsible for implementing the strategy generated by the decision control module and adjusting the I / O queue, including changing the priority of requests and allocating resources.

2. A system I / O queue dynamic intelligent control system according to claim 1, characterized in that: The data acquisition module adopts a distributed data acquisition architecture and acquires accurate performance data in real time by deploying sensors at key nodes of the system. The acquired performance data includes CPU utilization, memory usage, and I / O load.

3. A system I / O queue dynamic intelligent control system according to claim 1, characterized in that: The intelligent analysis module conducts in-depth mining and analysis of data based on machine learning algorithms and data analysis models to determine the current system load status, resource usage, and business urgency.

4. A system I / O queue dynamic intelligent control system according to claim 1, characterized in that: The decision control module has a flexible strategy formulation rule engine that can generate customized control strategies according to different system states.

5. The system I / O queue dynamic intelligent control system according to claim 1, characterized in that: The execution module adopts an efficient instruction execution mechanism to ensure the rapid and accurate implementation of the strategy.

6. A system I / O queue dynamic intelligent control system according to claim 2, characterized in that: The data collection cycle of the data collection module is usually set between 1 and 10 seconds according to system performance and business requirements.

7. A system I / O queue dynamic intelligent control system according to claim 1, characterized in that: The learning rate and regularization parameters in the machine learning algorithm of the intelligent analysis module need to be optimized according to actual data.

8. A system I / O queue dynamic intelligent control system according to claim 2, characterized in that: The policy adjustment has a threshold, which is triggered when the CPU utilization exceeds the set threshold of 80% or the I / O waiting time exceeds the set duration.

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