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

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List of Tables TABLE 3.1 COMPONENT 1: TEMPORAL CONSTRAINT SETTING ................................... 28 TABLE 3.2 COMPONENT 2: TEMPORAL CONSISTENCY MONITORING .......................... 30 TABLE 3.3 COMPONENT 3: TEMPORAL VIOLATION HANDLING ................................... 32 TABLE 4.1 NOTATIONS USED IN K-MAXSDEV ........................................................... 53 TABLE 4.2 ALGORITHM 1: K-MAXSDEV TIME-SERIES SEGMENTATION .................... 53 TABLE 4.3 ALGORITHM 2: REPRESENTATIVE TIME-SERIES BUILDING ....................... 55 TABLE 4.4 ALGORITHM 3: PATTERN VALIDATION AND TURNING POINTS DISCOVERY ........................................................................................................................... 56 TABLE 4.5 ALGORITHM 4: PATTERN MATCHING AND DURATION INTERVAL FORECASTING ..................................................................................................... 57 TABLE 4.6 RESULTS FOR SEGMENTATION AND PATTERN VALIDATION ...................... 61 TABLE 4.7 PATTERN DESCRIPTION AND ASSOCIATED TURNING POINTS .................... 61 TABLE 5.1 OVERVIEW ON THE SUPPORT OF TEMPORAL QOS CONSTRAINTS .............. 71 TABLE 5.2 PROBABILISTIC SETTING STRATEGY ......................................................... 80 TABLE 5.3 SPECIFICATION OF THE WORKFLOW SEGMENT ......................................... 87 TABLE 5.4 SETTING RESULTS ..................................................................................... 88 TABLE 7.1 ADAPTIVE TEMPORAL VIOLATION HANDLING POINT SELECTION STRATEGY ......................................................................................................................... 110 TABLE 7.2 EXPERIMENTAL SETTINGS....................................................................... 112 TABLE 8.1 EXPERIMENTAL SETTINGS....................................................................... 143 TABLE 8.2 COMPARISON OF BASIC MEASUREMENTS ............................................... 154 TABLE 8.3 EXPERIMENTAL RESULTS ON THE TIMES OF TEMPORAL VIOLATION HANDLING ........................................................................................................ 158 XVII
Chapter 1 Introduction This thesis proposes a novel probabilistic temporal framework to address the limitations of conventional temporal research and the new challenges for lifecycle support of time-constrained e-science applications in cloud workflow systems. The novel research reported in this thesis is concerned with the investigation of how to deliver high temporal QoS (Quality of Service) from the perspective of cost effectiveness, especially at workflow runtime. A set of new concepts, innovative strategies and algorithms are designed and developed to support temporal QoS over the whole lifecycles of scientific cloud workflow applications. Case study, comparisons, quantitative evaluations and/or theoretical proofs are conducted for each component of the temporal framework. This would demonstrate that with our new concepts, innovative strategies and algorithms, we can significantly improve the overall temporal QoS in scientific cloud workflow systems. This chapter introduces the background, motivations and key issues of this research. It is organised as follows. Section 1.1 gives a brief introduction to temporal QoS in cloud workflow systems. Section 1.2 presents a motivating example from a scientific application area. Section 1.3 outlines the key issues of this research. Finally, Section 1.4 presents an overview for the remainder of this thesis. 1.1 Temporal QoS in Scientific Cloud Workflow Systems Cloud computing is a latest market-oriented computing paradigm [11, 37]. It refers to a variety of services available on the Internet that delivers computing functionality on the service provider’s infrastructure. A cloud is a pool of virtualised computer resources and may actually be hosted on such as grid or utility computing environments [7, 59]. It has many potential advantages which include the ability to 1
Page 1 and 2: A Novel Probabilistic Temporal Fram
Page 3 and 4: Declaration This thesis contains no
Page 5 and 6: Abstract Cloud computing is a lates
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: FIGURE 7.4 EXPERIMENTAL RESULTS (NO
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
Page 55 and 56: workflow instance to determine whet
Page 57 and 58: framework for cost-effective delive
Page 59 and 60: pattern based forecasting strategy.
Page 61 and 62: 4.2 Related Work and Problem Analys
Page 63 and 64: 4.2.2 Problem Analysis In cloud wor
Page 65 and 66: significantly deteriorate the overa
Page 67 and 68: 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
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Distributed Computing Systems", Jou
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Appendix: Notation Index Symbols α
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PTR ( U ( SW ), ( a p + , a p + 1 m
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