云计算中的任务调度算法与虚拟资源优化分析

发布时间：2021-02-10 10:24

　　云计算已经成为一种基于按需定价模型向用户提供计算和存储等资源的新工具。其应用已经转向了外包和移动云计算,如iCloud存储等等。服务器虚拟化的允许在单个物理机器上运行操作系统和相关应用程序的多个实例。分配给这些实例的资源量和它们所使用的存储量可以通过Web接口在任何时间任何地方进行管理。在云计算系统中,任务调度和虚拟资源优化是NP-难优化问题。如何有效地使用云计算资源并获得用户端和云计算服务提供商端的最大利润是云计算服务提供商和云计算研究人员新的挑战。本论文的主要工作和成果如下:（1）为了解决云计算环境中的资源优化问题,本文提出了一种多目标资源调度算法,通过平衡簇内节点间的工作负载,减少任务等待时间和响应时间。该算法可以检测系统状态并做出决定,如果所有节点处于繁忙状态,则提交的任务会保持在队列中,直到接收到继续执行的通知或将其迁移到可用节点为止;（2）为了解决云计算中的任务调度问题,提出了一种改进的粒子群优化（PSO）算法以优化任务调度和云资源。仿真结果表明,该算法能以较低的代价减少总时间,快速、动态地优化虚拟资源;（3）基于进化算法提出了一种云平台任务调度和资源优化算法（EGA-TS... 

【文章来源】：北京科技大学北京市 211工程院校 教育部直属院校

【文章页数】：129 页

【学位级别】：博士

【文章目录】：
Acknowledgement
摘要
Abstract
List of abbreviations and symbols
Glossary
1 Introduction
    1.1 Research background
    1.2 The significance of the research
    1.3 Research Problems and proposed solutions
    1.4 Methodology and results of the study
    1.5 Research content and results
2 Review of existing researches in cloud computing environment
    2.1 Resource Management techniques in Cloud Computing Environment
        2.1.1 Introduction
        2.1.2 The scope of cloud resource management
        2.1.3 Requirements of cloud resource management
        2.1.4 Challenges in cloud resource management
        2.1.5 Strategies in cloud resource management
        2.1.6 Resource Management Techniques
    2.2 Task Scheduling Algorithms in Cloud Computing
        2.2.1 Task scheduling algorithm based on genetic algorithm
        2.2.2 Cloud Task Scheduling Based on Ant Colony Optimization
        2.2.3 Task Scheduling algorithm based on Honey bee behavior
        2.2.4 Task scheduling algorithm based on QoS in cloud computing
        2.2.5 Task Scheduling Based On Differential Evolution Algorithm
        2.2.6 Task Scheduling based on Min-Min algorithm in cloud computingenvironment
        2.2.7 Task Scheduling based on Max-Min algorithm in cloud computing
        2.2.8 Task Scheduling Algorithm based on priority in Cloud Computing
        2.2.9 Task Scheduling Algorithm Based on Load Balancing in CloudComputing
    2.3 Summary
3 Task Scheduling and Virtual Resource Optimization in Cloud Computingenvironment based on Hadoop YARN
    3.1 Introduction
    3.2 Background
    3.3 Problem Description
        3.3.1 Optimized Task Scheduling in Hadoop MapReduce
    3.4 Task Scheduling and Resource Optimization Based on Time Model forHadoop Cloud Computing
    3.5 Performance Evaluation
    3.6 Summary
4 Task Scheduling and Resource Optimization based on Heuristic Algorithms inCloud Computing Environment
    4.1 Introduction
    4.2 Background of cloud resource optimization model
    4.3 Problem Statement
        4.3.1 Assumptions and problem formulation
    4.4 Objective function formulation
    4.5 Particle Swarm Optimization Algorithm
        4.5.1 The implementation of MPSO Algorithm for Task Scheduling andVirtual Resource in Cloud Computing
    4.6 Algorithms Descriptions
    4.7 Simulation and Analysis of the Results
        4.7.1 Simulation Environment
    4.8 Summary
5 Task Scheduling and Cloud Resources Optimization based on EvolutionaryAlgorithms
    5.1 Introduction
    5.2 Background
        5.2.1 Task Scheduling Algorithm
        5.2.2 Basic Genetic Algorithm
        5.2.3 Task Model
        5.2.4 Single machine scheduling problem
        5.2.5 Multiple machine scheduling Problem
    5.3 Algorithm design
        5.3.1 Evaluation of space and time complexity
        5.3.2 Performance comparison and analysis
    5.4 Simulation and Results Analysis
    5.5 Summary
6 Conclusion
References
作者简历及在学研究成果
学位论文数据集



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