Method for dynamically adjusting task priority in software architecture

Abstract

The invention discloses a method for dynamically adjusting task priorities in a software architecture, which comprises the following steps of: monitoring in real time and acquiring performance data to obtain the performance data; performing modular design and seamless integration according to the performance data; and performing intelligent analysis and priority dynamic adjustment. The adaptability and the resource utilization rate of a software system are improved, so that smoother and more reliable service experience is provided for a user.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of software architecture and system design, and particularly relates to a method for dynamically adjusting task priorities in a software architecture. BACKGROUND

[0002] In the field of software architecture and system design, task scheduling and priority management are key mechanisms to ensure system performance and efficient resource utilization. Traditional task scheduling methods usually rely on fixed or static priority settings, which means that each task is assigned a priority when it is created, and this priority remains unchanged throughout the task's lifetime. However, this approach is not flexible enough to deal with dynamic changes in system load and diverse task types, and cannot effectively respond to complex and changing operating environments.

[0003] With the development of cloud computing, big data and artificial intelligence technologies, the number of tasks that modern software systems need to handle has increased dramatically, and the types of tasks have become more complex and diverse. These systems often need to meet the needs of high concurrency requests and real-time data processing under limited resources, which requires task scheduling mechanisms to be more intelligent and adaptive in adjusting the execution order of tasks and resource allocation.

[0004] Some existing solutions attempt to improve the flexibility and responsiveness of the system by introducing dynamic priority adjustment. For example, some real-time operating systems and middleware platforms provide task scheduling strategies based on task importance and urgency. In addition, existing dynamic priority adjustment mechanisms mostly focus on single-node or local-range optimization, without fully considering the global state and long-term running trends of the entire system. This may lead to a situation where local optimization actually reduces the overall system performance and user experience.

[0005] Disadvantages of existing technologies:

[0006] Disadvantages of fixed priority execution strategy

[0007] The fixed setting of priorities often fails to fully adapt to complex and changing business scenarios. When developers configure priorities, they are based on current knowledge and requirements. However, business environments change rapidly, with new demands and unexpected situations emerging frequently, which can quickly make the original priority sequence inappropriate. This rigid execution order cannot flexibly respond to changes in actual situations, thereby affecting the overall efficiency and response speed of the system.

[0008] Over-reliance on priority parameters can lead to "priority inflation", where developers continuously increase the priority of tasks over time to ensure that critical tasks are handled promptly. This can result in most tasks in the system being at a high priority level, effectively undermining the effectiveness of the priority mechanism. This not only increases the burden on the system but can also cause low-priority but equally important tasks to be neglected for a long time, affecting the overall and balanced nature of the system. Therefore, how to flexibly adjust the priority in a dynamic environment and avoid the abuse and failure of the priority mechanism has become a problem that needs to be solved. SUMMARY

[0009] In view of the above problems, the present application is proposed in order to provide a task priority dynamic adjustment method implemented in a software architecture to overcome the above problems or at least partially solve the above problems.

[0010] According to one aspect of the present application, a task priority dynamic adjustment method implemented in a software architecture is provided, the adjustment method comprising:

[0011] Real-time monitoring and performance data collection to obtain performance data;

[0012] Modular design and seamless integration according to the performance data;

[0013] Intelligent analysis and dynamic priority adjustment.

[0014] Optionally, the real-time monitoring and performance data collection to obtain performance data specifically includes:

[0015] A monitoring system is constructed and integrated into the software architecture to collect key performance indicator data;

[0016] The monitoring system identifies and records the type, expected completion time, and user-set priority information of each task.

[0017] Optionally, the key performance indicator data specifically includes: CPU usage, memory usage, network bandwidth utilization.

[0018] Optionally, the modular design and seamless integration according to the performance data specifically includes:

[0019] The dynamic priority adjustment mechanism is a modular structure that can be customized and extended according to actual needs.

[0020] Optionally, the intelligent analysis and dynamic priority adjustment specifically includes:

[0021] Based on real-time performance data and task importance evaluation results, when system resource shortage is detected, the priority order in the task queue is judged and adjusted, and the tasks identified as high priority or urgent are given priority to obtain sufficient computing resources for execution.

[0022] For low-priority tasks, the strategy of reducing execution frequency or temporarily shelving is adopted to optimize overall resource allocation.

[0023] Optionally, when system resource shortage is detected, the priority order in the task queue is judged and adjusted, specifically including:

[0024] When the system resource usage rate approaches or reaches the preset threshold, the scheduling algorithm will start the optimization measures;

[0025] For tasks identified as high priority or urgent, the scheduling algorithm will give priority to these tasks to obtain sufficient computing resources for execution;

[0026] Regardless of the current system load, critical tasks will be placed at the front of the task queue and allocated sufficient processing capacity to ensure that they can be completed in the shortest time;

[0027] The algorithm dynamically adjusts the resource allocation ratio of non-critical tasks to release computing resources to high-priority tasks.

[0028] Optionally, for low-priority tasks, the strategy of reducing execution frequency or temporarily shelving is adopted to optimize overall resource allocation, specifically including:

[0029] For low-priority tasks, the scheduling algorithm adopts management strategies;

[0030] In extreme cases, consider temporarily shelving these tasks, and continue processing after the system resource condition improves;

[0031] Based on real-time performance data and task importance evaluation results, the scheduling algorithm reasonably plans the resource competition relationship between various tasks under the premise of ensuring that important work is not affected.

[0032] The present application provides a kind of software architecture to realize task priority dynamic adjustment method, the adjustment method includes: real-time monitoring and performance data acquisition, obtains performance data;According to the performance data, modular design and seamless integration are carried out;Intelligent analysis and priority dynamic adjustment.Improve the adaptability and resource utilization rate of software system, so as to provide more smooth and reliable service experience for users.

[0033] The above description is only a summary of the technical solutions of the present application, in order to more clearly understand the technical means of the present application, and can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0035] Figure 1 A flow chart of a task priority dynamic adjustment method implemented in a software architecture provided by the embodiment of the present application;

[0036] Figure 2 A flow chart of real-time monitoring and performance data acquisition provided by the embodiment of the present application;

[0037] Figure 3 A flow chart of modular design and seamless integration provided by the embodiment of the present application;

[0038] Figure 4 Intelligent analysis and dynamic priority adjustment provided by the embodiment of the present application. DETAILED DESCRIPTION

[0039] The exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0040] The terms "include" and "have" and any variations thereof in the specification embodiments of the present application and claims and drawings are intended to cover non-exclusive inclusion, for example, including a series of steps or units.

[0041] The technical solutions of the present application will be further described in detail below in combination with the drawings and embodiments.

[0042] As Figure 1 shown, a task priority dynamic adjustment method implemented in a software architecture includes:

[0043] Real-time monitoring and performance data acquisition to obtain performance data;

[0044] Modular design and seamless integration based on the performance data;

[0045] Intelligent analysis and dynamic adjustment of priorities.

[0046] The invention as a whole comprises three parts: the first part is real-time monitoring and performance data collection, the second part is modular design and seamless integration, and the third part is intelligent analysis and dynamic adjustment of priorities.

[0047] As shown in Figure 2 , real-time monitoring and performance data collection: a high-efficiency monitoring system is deployed and integrated into the software architecture, responsible for collecting key performance indicator data continuously. These data include but are not limited to CPU usage, memory usage, network bandwidth utilization, etc., which together form the basis for assessing the current resource tension level of the system. In addition, the system should also have the ability to identify and record the type of each task, the expected completion time, and the user-defined priority, etc.

[0048] As shown in Figure 3 , modular design and seamless integration: in order to improve the flexibility and scalability of the system, the dynamic priority adjustment mechanism should be designed as a modular structure, which is easy to customize and extend according to actual needs. This mechanism should be easily integrated into existing software architecture, whether it is a newly developed system or an existing legacy system, and the addition of functions can be achieved through simple configuration or a small amount of code modification.

[0049] As shown in Figure 4 , intelligent analysis and dynamic adjustment of priorities: based on the real-time collected performance data and the task importance evaluation results, the scheduling algorithm will play a role. When detecting system resource tension, the algorithm will intelligently judge and automatically adjust the priority order in the task queue, prioritizing tasks that are identified as high-priority or urgent to ensure they have enough computing resources for execution. At the same time, for low-priority tasks, strategies such as reducing their execution frequency or temporarily shelving may be adopted to optimize overall resource allocation and ensure stable and efficient system operation.

[0050] The architecture of the invention needs to implement a high-efficiency monitoring system that can collect real-time data on CPU usage, memory usage, network bandwidth, and other key performance indicators. Through these data, the system can assess the current resource tension level. At the same time, a mechanism is needed to evaluate the importance or urgency of each task, which may be based on factors such as the type of task, expected completion time, user-defined priority, etc.

[0051] Based on the collected performance data and task importance assessment, the scheduling algorithm in the architecture will decide how to adjust the priority of each task. For example, when system resources are tight, the scheduling algorithm may automatically reduce the execution frequency of those tasks identified as low-priority tasks to ensure that high-priority tasks can obtain sufficient resources and be executed first. It needs to be highly automated and transparent to users to reduce manual intervention and improve response speed.

[0052] To ensure the flexibility and scalability of the system, this dynamic priority adjustment mechanism should be designed to be modular and easily integrated into existing software architecture. Through such technical implementation, the adaptability and resource utilization of the software system are improved, providing users with a more smooth and reliable service experience.

[0053] Benefits:

[0054] Improve resource utilization and system performance: Through real-time monitoring and intelligent scheduling, this solution can ensure that high-priority tasks are executed first when resources are tight, effectively avoiding inefficient resource allocation. This dynamic adjustment mechanism enables the system to flexibly allocate resources according to the current load, significantly improving overall resource utilization and system performance.

[0055] Enhance user experience and system stability: This solution reduces the need for manual intervention through automated priority management, improving response speed and processing efficiency. At the same time, modular design ensures the flexibility and scalability of the system, making it easy to add or upgrade future features. These features work together to provide users with a smoother, more reliable service experience, while also enhancing the stability and maintainability of the system.

[0056] The above specific embodiments further detail the purpose, technical solutions and benefits of the present invention. It should be understood that the above is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for dynamically adjusting task priorities in a software architecture, characterized in that: The adjustment method includes: Real-time monitoring and performance data collection to obtain performance data; Modular design and seamless integration based on the performance data; Intelligent analysis and dynamic priority adjustment.

2. The method for dynamically adjusting task priorities in a software architecture according to claim 1, wherein: The real-time monitoring and performance data collection to obtain performance data specifically includes: Build a monitoring system that is integrated into the software architecture and collects key performance indicator data; The monitoring system identifies and records the type, expected completion time, and user-set priority information of each task.

3. The method for dynamically adjusting task priorities in a software architecture according to claim 2, wherein: The key performance indicator data specifically includes: CPU usage, memory usage, and network bandwidth utilization.

4. The method for dynamically adjusting task priorities in a software architecture according to claim 1, wherein: The modular design and seamless integration based on the performance data specifically includes: The dynamic priority adjustment mechanism has a modular structure and can be customized and expanded according to actual needs.

5. The method for dynamically adjusting task priorities in a software architecture according to claim 1, wherein: The intelligent analysis and dynamic priority adjustment specifically include: Based on real-time performance data and task importance assessment results, when system resource constraints are detected, the priority order of the task queue is determined and adjusted to ensure that high-priority or urgent tasks receive sufficient computing resources for execution. For low-priority tasks, adopt strategies such as reducing execution frequency or temporarily shelving them to optimize overall resource allocation.

6. The method for dynamically adjusting task priorities in a software architecture according to claim 5, wherein: When system resource shortage is detected, determining and adjusting the priority order of the task queue specifically includes: When it detects that the system resource usage is approaching or reaching the preset threshold, the scheduling algorithm will initiate optimization measures; For tasks identified as high priority or urgent, the scheduling algorithm will prioritize ensuring that these tasks have sufficient computing resources to execute; Regardless of the current system load, critical tasks will be placed at the front of the task queue and allocated sufficient processing power to ensure that they can be completed in the shortest possible time; The algorithm dynamically adjusts the resource allocation ratio of non-critical tasks and releases computing resources for high-priority tasks.

7. The method for dynamically adjusting task priorities in a software architecture according to claim 5, wherein: For low-priority tasks, the strategy of reducing the execution frequency or temporarily shelving them to optimize overall resource allocation specifically includes: For low-priority tasks, the scheduling algorithm adopts a management strategy; In extreme cases, consider temporarily shelving these tasks and resuming them when system resources improve. The scheduling algorithm based on real-time performance data and task importance assessment results rationally plans the resource competition relationship between various tasks while ensuring that important work is not affected.

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