發(fā)布時(shí)間：2021-04-17 18:01

　　科學(xué)工作流是多個(gè)結(jié)構(gòu)化活動(dòng)和細(xì)粒度計(jì)算任務(wù)組成的集合,隨著科研信息化的出現(xiàn),工作流調(diào)度作為核心組件,已用于描述復(fù)雜的多重依賴型任務(wù)以及任務(wù)之間控制流的表示。與其他應(yīng)用類似,科學(xué)工作流也得益于基礎(chǔ)設(shè)施即服務(wù)云（IaaS云）,基于需求而彈性提供的可擴(kuò)展性資源可經(jīng)由IaaS云訪問和獲取并按需付費(fèi),然而大數(shù)據(jù)應(yīng)用在云服務(wù)資源上的高效調(diào)度仍然面臨眾多亟待解決的挑戰(zhàn),科學(xué)工作流任務(wù)之間蕪雜的關(guān)聯(lián)性增加了問題的復(fù)雜度,這促使研究者們不斷探尋啟發(fā)式、元啟發(fā)式和混合的方法,以期找到工作流調(diào)度問題的最優(yōu)解,因?yàn)榈托У馁Y源分配和調(diào)度除了導(dǎo)致時(shí)間和開銷增加外毫無裨益。本文針對(duì)云計(jì)算環(huán)境下資源分配和工作流調(diào)度提出新的優(yōu)化方法,這些優(yōu)化方法使得用戶和云服務(wù)提供商的許多服務(wù)質(zhì)量（Qo S）指標(biāo)均得到優(yōu)化,比如運(yùn)行時(shí)長、運(yùn)行開銷、負(fù)載均衡以及資源利用率。本文的主要工作包括:（i）定義和描述云計(jì)算環(huán)境下調(diào)度算法必須解決的問題;（ii）對(duì)云計(jì)算環(huán)境下科學(xué)工作流調(diào)度問題目前最前沿的先進(jìn)技術(shù)和方法進(jìn)行全面的分類和總結(jié);（iii）針對(duì)在動(dòng)態(tài)可擴(kuò)展虛擬機(jī)組上的任務(wù)調(diào)度問題,結(jié)合啟發(fā)式和混合整數(shù)規(guī)劃（MIP）模型,提出一種滿足最新... 

【文章來源】：華南理工大學(xué)廣東省 211工程院校 985工程院校 教育部直屬院校

【文章頁數(shù)】：122 頁

【學(xué)位級(jí)別】：博士

【文章目錄】：
摘要
ABSTRACT
CHAPTER 1 INTRODUCTION
    1.1 Introduction
    1.2 Background
        1.2.1 Cloud computing
        1.2.2 Scientific workflows
    1.3 Problem definition
    1.4 Motivation
    1.5 Thesis contributions
    1.6 Thesis overview
CHAPTER 2 WORKFLOW SCHEDULING IN THE CLOUD
    2.1 Introduction
    2.2 Scientific workflow scheduling problem in cloud
    2.3 Taxonomies of cloud workflow scheduling problem and techniques
        2.3.1 Computing Environment
        2.3.2 Application model
        2.3.3 Scheduling technique
        2.3.4 Scheduling objective
        2.3.5 Optimization criteria
        2.3.6 Optimization method
        2.3.7 Workload type
        2.3.8 Resource provisioning
        2.3.9 Pricing model
    2.4 Analysis of the workflow scheduling schemes in the cloud
    2.5 Conclusion
CHAPTER 3 SCIENTIFIC WORKFLOW SCHEDULING UNDER DEADLINE CONSTRAINTS
    3.1 Introduction
    3.2 Related work
    3.3 System model
        3.3.1 Scientific workflow application model
        3.3.2 Cloud resource model
        3.3.3 Workflow execution model
    3.4 The proposed DSB workflow scheduling algorithm
        3.4.1 Assumptions
        3.4.2 Problem statement
        3.4.3 Basic definitions
        3.4.4 Proposed algorithm
        3.4.5 Computational complexity
    3.5 Performance analysis and discussion
        3.5.1 Experiment environment
        3.5.2 Performance metric
        3.5.3 Evaluation results
        3.5.4 Sensitivity of overheads, VM performance variations and task failures
        3.5.5 Analysis of Variance （ANOVA） test
    3.6 Conclusions
CHAPTER 4 HYBRID METAHEURISTIC FOR MULTI-OBJECTIVE WORKFLOW SCHEDULING
    4.1 Introduction
    4.2 Related work
    4.3 Problem description for the proposed methodology
        4.3.1 System model
        4.3.2 Assumptions
        4.3.3 Multi-objective optimization
        4.3.4 Problem formulation
    4.4 Proposed work
        4.4.1 Initialization
        4.4.2 Fitness evaluation
        4.4.3 Optimization
        4.4.4 Selection of best fit solutions
        4.4.5 Termination condition
    4.5 Performance evaluation
        4.5.1 Experimental setup
        4.5.2 Evaluation metrics
        4.5.3 Simulation results
        4.5.4 Analysis of Variance （ANOVA） test
    4.6 Conclusions and future work
CHAPTER 5 Structure-aware and budget-aware workflow scheduling
    5.1 Introduction
    5.2 Related work
    5.3 Models and problem definition
        5.3.1 Application model
        5.3.2 Cloud model
        5.3.3 Definitions
        5.3.4 Problem formulation
    5.4 Proposed algorithm
        5.4.1 Task ranking
        5.4.2 DAG Partitioning
        5.4.3 Task grouping
        5.4.4 Budget propagation
        5.4.5 Dynamic resource provisioning and scheduling
    5.5 Performance evaluation
        5.5.1 Experimental setup
        5.5.2 Performance metric
    5.6 Evaluation results
    5.7 Conclusion
CONCLUSIONS AND FUTURE WORK
    Conclusions
    Future work
REFERENCES
攻讀博士學(xué)位期間取得的研究成果
ACKNOWLEDGEMENTS
附件



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