Task scheduling method applied to hybrid cloud environment

Abstract

The invention provides a task scheduling method applied to a hybrid cloud environment. The method solves a plurality of problems in the prior art. Firstly, through comprehensive demand analysis and resource evaluation, the target and constraint conditions of task scheduling can be clarified, and blindness and randomness in the scheduling process are avoided. And secondly, through task classification and scheduling strategy formulation, optimal configuration of resources and efficient execution of tasks can be realized, and the stability and performance of the system are improved. In addition, by establishing a task scheduling model and designing a mathematical formula, the task scheduling process can be described more accurately, and the scheduling accuracy and efficiency are improved. And finally, through performance evaluation and optimization steps, problems can be found and solved in time, and the performance and effect of task scheduling are continuously improved.

Description

Technical Field

[0001] The present invention specifically relates to a task scheduling method applied in a hybrid cloud environment Background Art

[0002] Hybrid cloud refers to a cloud computing deployment model that combines public cloud, private cloud and local data center. The task scheduling problem in a hybrid cloud environment is to allocate all tasks submitted by users to the computing nodes in the data cluster in the most reasonable way, taking into account the performance of each computing processing node, network bandwidth and other performance in a data cluster composed of a large number of heterogeneous nodes. Task scheduling in a hybrid cloud environment has the following characteristics:

[0003] Complexity: Hybrid clouds usually contain a large number of cloud computing nodes, and their task scheduling is a non-deterministic polynomial (NP) problem of large-scale optimization.

[0004] Diversity: Since users on hybrid clouds vary greatly, the tasks they submit have diverse requirements. Different tasks have high requirements for one or more of the following: real-time performance, bandwidth, computing cost, etc.

[0005] Dynamicity: User jobs are potentially submitted in a decentralized manner, which means that task scheduling on the cloud platform must consider the dynamic nature of tasks and node resources.

[0006] With the continuous development and popularization of cloud computing technology, hybrid cloud has become one of the important infrastructures for enterprises to achieve digital transformation and business innovation. Hybrid cloud can not only meet the needs of enterprises for data security, compliance and performance, but also achieve more efficient operations through flexible resource scheduling and optimization. However, the task scheduling problem in the hybrid cloud environment also faces many challenges.

[0007] The prior art currently has the following deficiencies:

[0008] 1. High difficulty in resource management: The addition of large-scale heterogeneous resources to hybrid clouds increases the difficulty of resource management. These cloud resources are huge in number and have distinct heterogeneity, which makes resource management and task scheduling more demanding on algorithms.

[0009] 2. Difficulty in optimizing task distribution: It is more difficult to distribute tasks on large-scale heterogeneous resources. When using heuristic algorithms for task scheduling, the computing efficiency is greatly affected; and when using online methods for scheduling, although the speed is fast, it is not easy to obtain the optimal scheduling solution that meets the needs of multiple tasks.

[0010] 3. Multi-task scheduling goals and their dynamics increase scheduling difficulty: In current task scheduling algorithms, since most of them consider a single goal (such as QoS, computing cost, etc.), they often use weight calculation or decision tree methods to optimize task distribution and scheduling together. When multiple goals are considered to be met at the same time, using the previous method will increase the dimension and increase the difficulty of solving the algorithm.

[0011] 4. Lack of unified framework and standards: Although the research technology and framework of public cloud are quite mature, there is no unified framework and standard for resource management and task scheduling strategies in hybrid cloud environments. Summary of the invention

[0012] The purpose of the present invention is to provide a task scheduling method applied in a hybrid cloud environment in view of the deficiencies in the prior art, and the task scheduling method applied in a hybrid cloud environment can well solve the above-mentioned problems.

[0013] In order to achieve the above requirements, the technical solution adopted by the present invention is: to provide a task scheduling method applied in a hybrid cloud environment, and the task scheduling method applied in a hybrid cloud environment includes:

[0014] The advantages of the task scheduling method applied in the hybrid cloud environment are as follows:

[0015] (1) Improve resource management efficiency: By introducing automated tools and multi-cloud management platforms, we can achieve automated scheduling, deployment, and monitoring of resources, thereby simplifying the management of multi-cloud environments and improving operational efficiency. At the same time, by regularly reviewing the use of cloud computing resources, we can ensure the efficient use of cloud resources and avoid resource waste.

[0016] (2) Optimize task distribution strategy: Design and implement efficient task distribution algorithms, taking into account the impact of task characteristics and resource heterogeneity on task scheduling, so as to achieve task distribution optimization on large-scale heterogeneous resources.

[0017] (3) Supporting multiple task scheduling objectives: Taking into account multiple task scheduling objectives (such as real-time performance, bandwidth, computing cost, etc.), design and implement multi-objective optimization algorithms to meet users' diverse task requirements.

[0018] (4) Provide flexible scheduling solutions: Based on the dynamic nature of the hybrid cloud environment, design flexible scheduling solutions to adapt to changes in tasks and resources. For example, through the automatic scaling function, the size of public cloud resources can be automatically adjusted according to changes in traffic or load to ensure the rational use of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The same reference numerals are used in these drawings to represent the same or similar parts. The exemplary embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 A schematic diagram of a task scheduling method applied in a hybrid cloud environment according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present application more clear, the present application is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0022] In the following description, references to "one embodiment", "an embodiment", "an example", "an example", etc. indicate that the embodiment or example described in this way may include specific features, structures, characteristics, properties, elements or limitations, but not every embodiment or example necessarily includes the specific features, structures, characteristics, properties, elements or limitations. In addition, repeated use of the phrase "according to one embodiment of the present application" may refer to the same embodiment, but does not necessarily refer to the same embodiment.

[0023] For the sake of simplicity, certain technical features well known to those skilled in the art are omitted in the following description.

[0024] According to an embodiment of the present application, a task scheduling method applied in a hybrid cloud environment is provided. Figure 1 As shown, the following steps are included:

[0025] S1: Steps to conduct demand analysis;

[0026] First, a comprehensive analysis of the task scheduling requirements in a hybrid cloud environment is required. This includes understanding key information such as the type, quantity, priority, resource requirements, and execution time of the task. By analyzing the requirements, the goals and constraints of task scheduling can be clarified, providing a basis for subsequent steps.

[0027] S2: Steps for conducting resource assessment;

[0028] Next, evaluate various resources in the hybrid cloud environment. This includes computing power, storage capacity, network bandwidth, and other resources in public clouds, private clouds, and local data centers. By evaluating resources, you can understand the availability and performance characteristics of various resources and provide resource support for task scheduling.

[0029] S3: Steps for task classification;

[0030] Tasks are classified according to their requirements and resource evaluation results. This can be based on factors such as task priority, resource requirements, and execution time. Task classification can better utilize resources in a hybrid cloud environment and improve the efficiency of task scheduling.

[0031] S4: Steps for formulating a scheduling strategy;

[0032] Based on the task classification and resource evaluation results, formulate task scheduling strategies in hybrid cloud environments. This can include load balancing strategies, dynamic resource adjustment strategies, priority scheduling strategies, etc. Through reasonable scheduling strategies, optimal resource allocation and efficient execution of tasks can be achieved.

[0033] S5: Perform the steps of establishing a task scheduling model;

[0034] In order to more accurately describe the task scheduling process, a task scheduling model based on a hybrid cloud environment is established. The model can include key components such as task queues, resource pools, and scheduling algorithms. Through the establishment of the model, the process and effect of task scheduling can be more intuitively understood.

[0035] S6: Perform the steps of designing mathematical formula;

[0036] In the task scheduling model, complex mathematical formulas are designed to improve the overall efficiency. For example, the following formula can be used to calculate the execution time and resource utilization of a task:

[0037]

[0038] Among them, n represents the number of tasks, S_i represents the size of the i-th task, V_i represents the speed of the i-th resource, A_i represents the actual amount of resources occupied by the i-th task, and T_i represents the total amount of the i-th resource. Through these formulas, the execution time and resource utilization of tasks can be calculated more accurately, thereby optimizing the task scheduling strategy.

[0039] S7: Initialize the task queue

[0040] Initialize the task queue based on the task classification results. Sort different types of tasks according to factors such as priority and resource requirements, and put them into the corresponding task queue. By initializing the task queue, an orderly task flow can be provided for subsequent task scheduling.

[0041] S8: step of performing resource allocation;

[0042] Allocate resources based on the task queue and resource pool. Select appropriate resources from the resource pool for allocation based on the task priority and resource requirements. At the same time, factors such as resource load balancing and dynamic adjustment need to be considered to ensure optimal allocation and efficient use of resources.

[0043] S9: Steps for executing the task;

[0044] After resource allocation is completed, the task execution begins. According to the task scheduling strategy and resource allocation results, the task is sent to the corresponding resource for execution. At the same time, it is necessary to monitor the execution process of the task and the use of resources to ensure the normal execution of the task and the effective use of resources.

[0045] S10: Steps for performance evaluation and optimization;

[0046] After the task is completed, the performance of the task scheduling is evaluated. This includes indicators such as task execution time, resource utilization, and system stability. By evaluating performance indicators, we can understand the effect and problems of task scheduling. Based on the evaluation results, we can optimize and adjust the task scheduling strategy and resource allocation to improve the efficiency and performance of task scheduling.

[0047] According to an embodiment of the present application, the task scheduling method applicable to a hybrid cloud environment specifically includes the following steps:

[0048] 1. Demand Analysis

[0049] Introduction: Demand analysis is the first step in task scheduling, which aims to fully understand the task scheduling requirements and constraints.

[0050] Implementation: Identify the task type: Clarify whether the task is compute-intensive, I / O-intensive, or other types so that appropriate cloud resources can be selected.

[0051] Determine the number of tasks: Count the total number of tasks that need to be scheduled to facilitate resource planning and allocation.

[0052] Assess task priorities: Assign priorities to tasks based on their importance and urgency, ensuring that high-priority tasks are executed first.

[0053] Analyze resource requirements: Understand the requirements of tasks for computing, storage, network and other resources so as to select appropriate resources in a hybrid cloud environment.

[0054] Consider execution time: Determine the execution time window of the task to avoid conflicts with other tasks when scheduling resources.

[0055] 2. Resource Assessment

[0056] Introduction: Resource assessment is a key step in understanding the availability and performance characteristics of various resources in a hybrid cloud environment.

[0057] Implementation operations: Collect resource information: Collect information on computing, storage, network and other resources from public clouds, private clouds and local data centers.

[0058] Monitor resource performance: Use monitoring tools to monitor resource performance indicators in real time, such as CPU usage, memory usage, network bandwidth, etc.

[0059] Analyze resource availability: Analyze resource availability and stability based on resource performance monitoring results to make reasonable choices when scheduling tasks.

[0060] Evaluate resource costs: Consider the purchase cost, maintenance cost, and usage cost of resources to maximize cost-effectiveness when scheduling resources.

[0061] 3. Task Classification

[0062] Introduction: Task classification is to divide tasks into different types of steps based on task requirements and resource assessment results.

[0063] Implementation: Establish classification standards: Establish task classification standards based on factors such as task type, priority, resource requirements, etc.

[0064] Divide task categories: Divide tasks into different categories according to classification criteria, such as high-priority computing-intensive tasks, low-priority I / O-intensive tasks, etc.

[0065] Tag task attributes: Tag each task with its category, priority, and resource requirements to facilitate resource allocation and task scheduling in subsequent steps.

[0066] 4. Develop a Scheduling Strategy

[0067] Introduction: Formulating a scheduling strategy is a step to formulate a task scheduling strategy in a hybrid cloud environment based on task classification and resource evaluation results.

[0068] Implementation operation: Determine the scheduling goal: Clarify the goal of task scheduling, such as improving resource utilization, reducing task execution time, etc.

[0069] Design scheduling algorithms: Design appropriate scheduling algorithms based on scheduling objectives, such as load balancing algorithms, priority scheduling algorithms, etc.

[0070] Consider dynamic adjustment: Consider the dynamic adjustment of resources and dynamic changes of tasks in the scheduling algorithm to enable real-time adjustment and optimization during task execution.

[0071] Develop a fault recovery strategy: Design a fault recovery strategy so that you can quickly recover and continue execution when a failure occurs during task execution.

[0072] 5. Establishing a task scheduling model

[0073] Introduction: The purpose of establishing a task scheduling model is to describe the task scheduling process more accurately so as to facilitate mathematical analysis and optimization.

[0074] Implementation operations: Define model elements: Define model elements such as tasks, resources, scheduling algorithms, and clarify the relationships and attributes between them.

[0075] Establish a mathematical model: Based on the model elements and scheduling strategies, establish a mathematical model for task scheduling, including components such as task queues, resource pools, and scheduling algorithms.

[0076] Verify model validity: Verify the validity and accuracy of the model through simulation experiments or actual tests so that it can be used in subsequent steps.

[0077] 6. Design mathematical formula (optimization scheduling)

[0078] Introduction: Mathematical formulas are designed to improve the efficiency and accuracy of task scheduling and to optimize resource allocation and task scheduling through mathematical methods.

[0079] Implementation operation: Determine the optimization goal: Clarify the optimization goal, such as minimizing task execution time, maximizing resource utilization, etc.

[0080] Design mathematical formulas: Based on the optimization objectives, design appropriate mathematical formulas to describe the task scheduling process, such as the calculation formulas for task execution time and resource utilization mentioned above.

[0081] Solving mathematical formulas: Use mathematical methods to solve formulas and obtain the optimal resource allocation and task scheduling solutions.

[0082] Verify formula effect: Verify the effect and accuracy of the formula through actual testing or simulation experiments so that it can be used in subsequent steps.

[0083] 7. Initialize the task queue

[0084] Introduction: The purpose of initializing the task queue is to sort the tasks according to factors such as priority and resource requirements so as to perform orderly task scheduling.

[0085] Implementation operation: Create task queue: Create different task queues according to the task classification results, such as high priority task queue, low priority task queue, etc.

[0086] Sort tasks: Sort tasks based on factors such as task priority and resource requirements, and place them in corresponding task queues.

[0087] Update task status: Update task status information in real time, such as whether resources have been allocated to the task, whether it is being executed, etc., so as to facilitate monitoring and management in subsequent steps.

[0088] 8. Resource Allocation

[0089] Introduction: Resource allocation is the step of allocating appropriate resources to tasks based on the status of task queues and resource pools.

[0090] Implementation operation: Select resources: Select appropriate resources from the resource pool for allocation based on factors such as task priority and resource requirements.

[0091] Assign resources: Assign the selected resources to tasks and record the resource allocation.

[0092] Monitor resource usage: Real-time monitoring of resource usage, such as CPU usage, memory usage, etc., to enable dynamic adjustments when resources are insufficient or overloaded.

[0093] IX. Task Execution

[0094] Introduction: Task execution is the step of sending tasks to corresponding resources for execution after resource allocation is completed.

[0095] Implementation operation: Start task: Start the task and execute it according to the resource allocation results.

[0096] Monitor task execution: monitor task execution in real time, such as task progress, resource usage, etc.

[0097] Handling abnormal situations: During the task execution process, if an abnormal situation occurs (such as insufficient resources, task failure, etc.), it is necessary to handle it in a timely manner and resume task execution.

[0098] 10. Performance Evaluation and Optimization

[0099] Introduction: Performance evaluation and optimization is the step of evaluating the performance of task scheduling after the task is completed and optimizing it based on the evaluation results.

[0100] Implementation operations: Collect performance data: Collect performance data such as task execution time, resource utilization, and system stability.

[0101] Analyze performance data: Analyze and evaluate the collected performance data to understand the effects of task scheduling and existing problems.

[0102] Develop optimization plans: Based on the performance evaluation results, develop optimization plans, such as adjusting resource allocation strategies, optimizing scheduling algorithms, etc.

[0103] Implement the optimization plan: Apply the optimization plan to the task scheduling system and conduct actual tests to verify the optimization effect.

[0104] The above-mentioned embodiments only represent several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the claims.

Claims

1. A task scheduling method applied in a hybrid cloud environment, characterized in that: The steps include: S1: Steps to conduct demand analysis; Comprehensively analyze the task scheduling requirements in the hybrid cloud environment, including understanding key information such as task type, quantity, priority, resource requirements, and execution time. By analyzing the requirements, clarify the goals and constraints of task scheduling, and provide a basis for subsequent steps; S2: Steps for conducting resource assessment; Evaluate various resources in the hybrid cloud environment, including the computing power, storage capacity, and network bandwidth resources of public clouds, private clouds, and local data centers. Through resource evaluation, understand the availability and performance characteristics of various resources and provide resource support for task scheduling; S3: Steps for task classification; According to the task requirements and resource evaluation results, tasks are classified based on task priority, resource requirements, and execution time factors. Through task classification, resources in the hybrid cloud environment can be better utilized to improve the efficiency of task scheduling; S4: Steps for formulating a scheduling strategy; Based on the task classification and resource evaluation results, formulate task scheduling strategies in the hybrid cloud environment, including load balancing strategies, dynamic resource adjustment strategies, and priority scheduling strategies. Through reasonable scheduling strategies, optimal resource allocation and efficient task execution can be achieved; S5: Perform the steps of establishing a task scheduling model; In order to describe the task scheduling process more accurately, a task scheduling model based on a hybrid cloud environment is established. The model includes task queues, resource pools, and key components of scheduling algorithms. Through the establishment of the model, the process and effect of task scheduling can be understood more intuitively. S6: Perform the steps of designing mathematical formula; In the task scheduling model, formulas are designed to improve the overall efficiency. The following formulas are used to calculate the execution time and resource utilization of tasks: Among them, n represents the number of tasks, S_i represents the size of the i-th task, V_i represents the speed of the i-th resource, A_i represents the amount of resources actually occupied by the i-th task, and T_i represents the total amount of the i-th resource. Through the formula, the execution time and resource utilization of the task can be calculated more accurately, thereby optimizing the task scheduling strategy; S7: Perform the step of initializing the task queue; According to the task classification results, the task queue is initialized, and different types of tasks are sorted according to priority and resource requirement factors, and put into the corresponding task queue. By initializing the task queue, an orderly task flow is provided for subsequent task scheduling; S8: step of performing resource allocation; Allocate resources based on the situation of task queues and resource pools. Select appropriate resources from the resource pool for allocation based on task priority and resource requirements. Ensure optimal configuration and efficient use of resources based on factors such as resource load balancing and dynamic adjustment. S9: Steps for executing the task; After resource allocation is completed, tasks are started to be executed. According to the task scheduling strategy and resource allocation results, tasks are sent to corresponding resources for execution. The execution process of tasks and the use of resources are monitored to ensure the normal execution of tasks and the effective use of resources. S10: Steps for performance evaluation and optimization; After the task is completed, the performance of task scheduling is evaluated, including task execution time, resource utilization, and system stability indicators. By evaluating the performance indicators, the effects and problems of task scheduling can be understood. Based on the evaluation results, the task scheduling strategy and resource allocation are optimized and adjusted to improve the efficiency and performance of task scheduling.

2. The task scheduling method applied in a hybrid cloud environment according to claim 1 is characterized in that: Step S1 specifically includes: Identify the task type: clarify whether the task is compute-intensive, I / O-intensive, or other types, so as to select appropriate cloud resources; Determine the number of tasks: Count the total number of tasks that need to be scheduled in order to plan and allocate resources; Assess task priorities: Assign priorities to tasks based on their importance and urgency, ensuring that high-priority tasks are executed first; Analyze resource requirements: Understand the requirements of tasks for computing, storage, network and other resources so as to select appropriate resources in a hybrid cloud environment; Consider execution time: determine the execution time window of the task to avoid conflicts with other tasks during resource scheduling; Step S2 specifically includes: Implementation operations: Collect resource information: Collect information on computing, storage, and network resources from public clouds, private clouds, and local data centers; Monitor resource performance: Use monitoring tools to monitor resource performance indicators in real time; Analyze resource availability: Analyze resource availability and stability based on resource performance monitoring results to make reasonable choices when scheduling tasks; Evaluate resource costs: Consider the purchase cost, maintenance cost, and usage cost of resources to maximize cost-effectiveness when scheduling resources; Step S3 specifically includes: Implementation: Establish classification standards: Establish task classification standards based on task type, priority, and resource requirement factors; Classify tasks: According to the classification criteria, tasks are divided into different categories, including high-priority computing-intensive tasks and low-priority I / O-intensive tasks; Tag task attributes: Tag each task with its category, priority, and resource requirements to facilitate resource allocation and task scheduling in subsequent steps.

3. The task scheduling method applied in a hybrid cloud environment according to claim 1 is characterized in that: Step S4 specifically includes: Implementation: Determine the scheduling goals: clarify the goals of task scheduling, including improving resource utilization and reducing task execution time; Design scheduling algorithms: Design appropriate scheduling algorithms based on scheduling objectives, including load balancing algorithms and priority scheduling algorithms; Consider dynamic adjustment: Consider the dynamic adjustment of resources and the dynamic changes of tasks in the scheduling algorithm, so as to make real-time adjustments and optimizations during task execution; Develop a fault recovery strategy: Design a fault recovery strategy so that when a fault occurs during task execution, the task can be quickly recovered and continued; Step S5 specifically includes: Implementation operations: Define model elements: define tasks, resources, and scheduling algorithm model elements, and clarify the relationships and attributes between them; Establishing mathematical model: According to the model elements and scheduling strategy, establish the mathematical model of task scheduling, including task queue, resource pool, and scheduling algorithm components; Verify model validity: Verify the validity and accuracy of the model through simulation experiments or actual tests so that it can be used in subsequent steps; Step S6 specifically includes: Implementation: Determine optimization goals: Clarify optimization goals, including minimizing task execution time and maximizing resource utilization; Design mathematical formulas: According to the optimization objectives, design appropriate mathematical formulas to describe the task scheduling process, such as the calculation formulas for task execution time and resource utilization; Solving mathematical formulas: Using mathematical methods to solve formulas and obtain optimal resource allocation and task scheduling solutions; Verify formula effect: Verify the effect and accuracy of the formula through actual testing or simulation experiments so that it can be used in subsequent steps.

4. The task scheduling method applied in a hybrid cloud environment according to claim 1 is characterized in that: Step S7 specifically includes: Implementation operation: Create task queues: Create different task queues according to the task classification results, including high-priority task queues and low-priority task queues; Sorting tasks: Sort tasks according to factors such as task priority and resource requirements, and put them into corresponding task queues; Update task status: Update task status information in real time, including whether resources have been allocated to the task and whether it is being executed, so as to facilitate monitoring and management in subsequent steps; Step S8 specifically includes: Implementation operation: select resources and allocate them from the resource pool according to the task priority and resource demand factors; Allocate resources: Allocate selected resources to tasks and record resource allocation; Monitor resource usage: Real-time monitoring of resource usage, including CPU usage and memory usage, to enable dynamic adjustments when resources are insufficient or overloaded.

5. The task scheduling method applied in a hybrid cloud environment according to claim 1 is characterized in that: Step S9 specifically includes: Implementation operation: Start the task and execute it according to the resource allocation result; Monitor task execution: monitor task execution in real time, including task progress and resource usage; Handling abnormal situations: During the task execution process, if abnormal situations occur, such as insufficient resources or task failure, they need to be handled in a timely manner and task execution needs to be resumed; Step S10 specifically includes: Implementation operations: Collect performance data, such as task execution time, resource utilization, system stability, etc. Analyze performance data: Analyze and evaluate the collected performance data to understand the effects and problems of task scheduling; Formulate optimization plans: According to the performance evaluation results, formulate optimization plans, including adjusting resource allocation strategies and optimizing scheduling algorithms; Implement the optimization plan: Apply the optimization plan to the task scheduling system and conduct actual tests to verify the optimization effect.

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