云計算已經(jīng)成為一種基于按需定價模型向用戶提供計算和存儲等資源的新工具。其應用已經(jīng)轉向了外包和移動云計算,如iCloud存儲等等。服務器虛擬化的允許在單個物理機器上運行操作系統(tǒng)和相關應用程序的多個實例。分配給這些實例的資源量和它們所使用的存儲量可以通過Web接口在任何時間任何地方進行管理。在云計算系統(tǒng)中,任務調度和虛擬資源優(yōu)化是NP-難優(yōu)化問題。如何有效地使用云計算資源并獲得用戶端和云計算服務提供商端的最大利潤是云計算服務提供商和云計算研究人員新的挑戰(zhàn)。本論文的主要工作和成果如下:（1）為了解決云計算環(huán)境中的資源優(yōu)化問題,本文提出了一種多目標資源調度算法,通過平衡簇內(nèi)節(jié)點間的工作負載,減少任務等待時間和響應時間。該算法可以檢測系統(tǒng)狀態(tài)并做出決定,如果所有節(jié)點處于繁忙狀態(tài),則提交的任務會保持在隊列中,直到接收到繼續(xù)執(zhí)行的通知或將其遷移到可用節(jié)點為止;（2）為了解決云計算中的任務調度問題,提出了一種改進的粒子群優(yōu)化（PSO）算法以優(yōu)化任務調度和云資源。仿真結果表明,該算法能以較低的代價減少總時間,快速、動態(tài)地優(yōu)化虛擬資源;（3）基于進化算法提出了一種云平臺任務調度和資源優(yōu)化算法（EGA-TS... 

【文章來源】：北京科技大學北京市 211工程院校 教育部直屬院校

【文章頁數(shù)】：129 頁

【學位級別】：博士

【文章目錄】：
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
作者簡歷及在學研究成果
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