Xiao Liu PhD Thesis.pdf - Faculty of Information and Communication ...

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Acknowledgements I sincerely express my deepest gratitude to my coordinate supervisor, Professor Yun Yang, for his seasoned supervision and continuous encouragement throughout my PhD study, especially for his careful reading and appraisal of drafts of this thesis. Without his consistent support, I would not have been able to complete this manuscript. To me, he is indeed “a good mentor and a helpful friend”. I thank Swinburne University of Technology and the Faculty of Information and Communication Technologies for offering me a full Research Scholarship throughout my doctoral program. I also thank the Research Committee of the Faculty of Information and Communication Technologies for research publication funding support and for providing me with financial support to attend conferences. My thanks also go to my associate supervisors Dr. Jinjun Chen and Professor Chengfei Liu, and to staff members, research students and research assistants at previous CITR/CS3 and current SUCCESS for their help, suggestions, friendship and encouragement, in particular, Professor Ryszard Kowalczyk, Robert Merkel, Gillian Foster, Huai Liu, Jianxin Li, Rui Zhou, Jiajie Xu, Minyi Li, Hai Huang, Xiaohui Zhao, Wei Dong, Jing Gao, Bryce Gibson. Of course, previous and current members of the Workflow Technology program: Saeed Nauman, Qiang He, Ke Liu, Dong Yuan, Gaofeng Zhang, Wenhao Li, Dahai Cao and Xuyun Zhang. I also thank my research partners from Hefei University of Technology (China), Professor Zhiwei Ni, Yuanchun Jiang, Zhangjun Wu and Qing Lu. I am deeply grateful to my parents Jianshe Liu and Yousheng Peng for raising me up, teaching me to be a good person, and supporting me to study abroad. Last but not least, I thank my wife Nanyan Jiang, for her love, understanding, encouragement, sacrifice and help. III
Abstract Cloud computing is a latest market-oriented computing paradigm which can provide virtually unlimited scalable high performance computing resources. As a type of high-level middleware services for cloud computing, cloud workflow systems are a research frontier for both cloud computing and workflow technologies. Cloud workflows often underlie many large scale data/computation intensive e-science applications such as earthquake modelling, weather forecast and Astrophysics. At build-time modelling stage, these sophisticated processes are modelled or redesigned as cloud workflow specifications which normally contain the functional requirements for a large number of workflow activities and their non-functional requirements such as Quality of Service (QoS) constraints. At runtime execution stage, cloud workflow instances are executed by employing the supercomputing and data sharing ability of the underlying cloud computing infrastructures. In this thesis, we focus on scientific cloud workflow systems. In the real world, many scientific applications need to be time constrained, i.e. they are required to be completed by satisfying a set of temporal constraints such as local temporal constraints (milestones) and global temporal constraints (deadlines). Meanwhile, task execution time (or activity duration), as one of the basic measurements for system performance, often needs to be monitored and controlled by specific system management mechanisms. Therefore, how to ensure satisfactory temporal correctness (high temporal QoS), i.e. how to guarantee on-time completion of most, if not all, workflow applications, is a critical issue for enhancing the overall performance and usability of scientific cloud workflow systems. At present, workflow temporal verification is a key research area which focuses on time-constrained large-scale complex workflow applications in distributed high performance computing environments. However, existing studies mainly emphasise IV
Page 1 and 2: A Novel Probabilistic Temporal Fram
Page 3: Declaration This thesis contains no
Page 7 and 8: Component 2, the state of scientifi
Page 9 and 10: http://dx.doi.org/10.1016/j.jpdc.20
Page 11 and 12: 17. X. Liu, J. Chen, and Y. Yang, A
Page 13 and 14: 4.2 RELATED WORK AND PROBLEM ANALYS
Page 15 and 16: STRATEGY ..........................
Page 17 and 18: FIGURE 7.4 EXPERIMENTAL RESULTS (NO
Page 19 and 20: Chapter 1 Introduction This thesis
Page 21 and 22: Clearly, if the execution time of t
Page 23 and 24: to seek all the candidates. Further
Page 25 and 26: e unnecessary. Hence, an effective
Page 27 and 28: temporal violations, viz. recoverab
Page 29 and 30: In Chapter 2, we introduce the rela
Page 31 and 32: a two-stage searching process with
Page 33 and 34: QoS requirements. Generally speakin
Page 35 and 36: handling strategies employed in a s
Page 37 and 38: activity points to conduct temporal
Page 39 and 40: compensation, is believed as a suit
Page 41 and 42: successful completion. Specifically
Page 43 and 44: Chapter 4 and Chapter 5 respectivel
Page 45 and 46: etween time deficit compensation an
Page 47 and 48: service user’s requirements and t
Page 49 and 50: conducted for three times, i.e. sta
Page 51 and 52: PTDA+ACOWR as an example to introdu
Page 53 and 54: currently running. It is built on t
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workflow instance to determine whet
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framework for cost-effective delive
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pattern based forecasting strategy.
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4.2 Related Work and Problem Analys
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4.2.2 Problem Analysis In cloud wor
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significantly deteriorate the overa
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activity durations: characteristics
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specified with different means and
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Table 4.1 Notations Used in K-MaxSD
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1) Duration series building As ment
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Table 4.5 Algorithm 4: Pattern Matc
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to search for those activities whic
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cycle. Here, we also trace back the
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ecognition. Sliding Window ranks in
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predicted value will be the one pre
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segmentation algorithm is capable o
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distributed soft real-time system,
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Table 5.1 Overview on the Support o
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2 2 can be denoted as N ( µ , σ )
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mean iteration times or with some p
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Figure 5.4 Choice Building Block No
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activities may be omitted for the e
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5.3.1 Calculating Weighted Joint Di
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constraints until the constraint is
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Therefore, it can be expressed as n
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Table 5.3 Specification of the Work
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and the constraint for activity X 1
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ease of discussion and to avoid the
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Unfortunately, conventional discret
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The probability consistency range w
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6.2.3 Temporal Checkpoint Selection
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100 times each. All the activity du
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normal distribution and correlated
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Figure 6.3 Checkpoint Selection wit
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work and problem analysis. Section
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expected time redundancy of subsequ
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the probability for self-recovery i
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skipped if self-recovery applies. A
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To evaluate the average performance
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activities and around 300 violation
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Figure 7.3 Experimental Results (No
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Figure 7.5 Cost Reduction Rate vs.
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8.1 Related Work and Problem Analys
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scientific processes, human interve
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alone does not involve any time def
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swap slower machines in the active
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TD( a p ) L{ ( ai , R j ) | i = p +
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For example, in Figure 8.2, local w
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value to sample all of the solution
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mapping task a i to resource R j fr
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have done some simulation experimen
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violations can still be recovered b
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PTDA+ACOWR for Level III Violations
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an integer from 1 to 5 where the ex
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D: Definition for Fitness Value Fit
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strategy; and End(Rescheduling) is
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(a) Optimisation Ratio on Total Cos
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makespan. In our two stage workflow
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espectively. It is clearly that ACO
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comparison results on the violation
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percentiles. The average global vio
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equivalent times of ACOWR are calcu
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Chapter 9 Conclusions and Future Wo
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the real world, simulation experime
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ased temporal consistency model. Ac
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the whole lifecycle support for hig
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which can be further investigated i
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Bibliography [1] W. M. P. van der A
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Systems", ACM Trans. on Software En
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conjunction with 23 rd Parallel and
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Elsevier, vol. 84, no. 3, pp. 354-3
Page 197 and 198:
Distributed Computing Systems", Jou
Page 199 and 200:
Appendix: Notation Index Symbols α
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PTR ( U ( SW ), ( a p + , a p + 1 m
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