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

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COMPONENT III Temporal Violation Handling Chapter 7 Temporal Violation Handling Point Selection In scientific cloud workflow systems, temporal violation handling points are those workflow activity points where temporal violation handling strategies are triggered to tackle detected temporal violations. The existing work on temporal verification adopts the philosophy of temporal violation handling required whenever a temporal violation is detected. Therefore, a checkpoint is regarded the same as a temporal violation handling point. However, the probability of self-recovery which utilises the time redundancy of subsequent workflow activities to automatically compensate for the time deficit is ignored, and hence would impose a high temporal violation handling cost. To address such a problem, this thesis presents a novel adaptive temporal violation handling point selection strategy where the probability of selfrecovery is effectively utilised in temporal violation handling point selection to avoid unnecessary handling for temporal violations. This chapter is organised as follows. Section 7.1 presents the specifically related 104
work and problem analysis. Section 7.2 presents our novel adaptive temporal violation handling point selection strategy. Section 7.3 demonstrates the evaluation results. Finally Section 7.4 summarises this chapter. This chapter is partly based on our work presented in [56]. 7.1 Related Work and Problem Analysis 7.1.1 Related Work Due to the dynamic nature of distributed computing infrastructures (e.g. p2p, grid and cloud) and the requirement of QoS, scientific workflow temporal verification has attracted increasing interests in recent years. Many efforts have been dedicated to different tasks involved in temporal verification. As mentioned earlier, a runtime checkpoint selection strategy aims at selecting activity points on the fly to conduct temporal verification so as to improve the efficiency of monitoring large scale scientific workflows and reduce the computation cost [21]. Meanwhile, to reduce the temporal violation handling cost, multiple-state based temporal verification is proposed to detect multiple fine-grained temporal violations so that different temporal violations (with different levels of time deficits) can be tackled by different temporal violation handling strategies [17]. In recent years, many checkpoint selection strategies, from intuitive rule based to sophisticated model based, have been proposed. Most of them are introduced in Section 6.1.1 and hence omitted here. Among them, the state-of-the-art checkpoint selection strategies are those satisfy the requirements of necessity and sufficiency [21]. As mentioned before, the common philosophy in the studies on temporal verification is that a checkpoint is equal to a temporal violation handling point. Therefore, all these checkpoint selection strategies can be regarded as temporal violation handling point selection strategies. That is, the process of temporal violation handling point selection is actually overlooked in the existing temporal verification and temporal violation handling processes. Therefore, to the best of our knowledge, the issue of temporal violation handling point selection has being neglected and so far not been well addressed. 105
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A Novel Probabilistic Temporal Fram
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Declaration This thesis contains no
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Abstract Cloud computing is a lates
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Component 2, the state of scientifi
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http://dx.doi.org/10.1016/j.jpdc.20
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17. X. Liu, J. Chen, and Y. Yang, A
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4.2 RELATED WORK AND PROBLEM ANALYS
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STRATEGY ..........................
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FIGURE 7.4 EXPERIMENTAL RESULTS (NO
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Chapter 1 Introduction This thesis
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Clearly, if the execution time of t
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to seek all the candidates. Further
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e unnecessary. Hence, an effective
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temporal violations, viz. recoverab
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In Chapter 2, we introduce the rela
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a two-stage searching process with
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QoS requirements. Generally speakin
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handling strategies employed in a s
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activity points to conduct temporal
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compensation, is believed as a suit
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successful completion. Specifically
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Chapter 4 and Chapter 5 respectivel
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etween time deficit compensation an
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service user’s requirements and t
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conducted for three times, i.e. sta
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PTDA+ACOWR as an example to introdu
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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
Page 71 and 72: Table 4.1 Notations Used in K-MaxSD
Page 73 and 74: 1) Duration series building As ment
Page 75 and 76: Table 4.5 Algorithm 4: Pattern Matc
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Page 79 and 80: cycle. Here, we also trace back the
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Page 83 and 84: predicted value will be the one pre
Page 85 and 86: segmentation algorithm is capable o
Page 87 and 88: distributed soft real-time system,
Page 89 and 90: Table 5.1 Overview on the Support o
Page 91 and 92: 2 2 can be denoted as N ( µ , σ )
Page 93 and 94: mean iteration times or with some p
Page 95 and 96: Figure 5.4 Choice Building Block No
Page 97 and 98: activities may be omitted for the e
Page 99 and 100: 5.3.1 Calculating Weighted Joint Di
Page 101 and 102: constraints until the constraint is
Page 103 and 104: Therefore, it can be expressed as n
Page 105 and 106: Table 5.3 Specification of the Work
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Page 109 and 110: ease of discussion and to avoid the
Page 111 and 112: Unfortunately, conventional discret
Page 113 and 114: The probability consistency range w
Page 115 and 116: 6.2.3 Temporal Checkpoint Selection
Page 117 and 118: 100 times each. All the activity du
Page 119 and 120: normal distribution and correlated
Page 121: Figure 6.3 Checkpoint Selection wit
Page 125 and 126: expected time redundancy of subsequ
Page 127 and 128: the probability for self-recovery i
Page 129 and 130: skipped if self-recovery applies. A
Page 131 and 132: To evaluate the average performance
Page 133 and 134: activities and around 300 violation
Page 135 and 136: Figure 7.3 Experimental Results (No
Page 137 and 138: Figure 7.5 Cost Reduction Rate vs.
Page 139 and 140: 8.1 Related Work and Problem Analys
Page 141 and 142: scientific processes, human interve
Page 143 and 144: alone does not involve any time def
Page 145 and 146: swap slower machines in the active
Page 147 and 148: TD( a p ) L{ ( ai , R j ) | i = p +
Page 149 and 150: For example, in Figure 8.2, local w
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Page 153 and 154: mapping task a i to resource R j fr
Page 155 and 156: have done some simulation experimen
Page 157 and 158: violations can still be recovered b
Page 159 and 160: PTDA+ACOWR for Level III Violations
Page 161 and 162: an integer from 1 to 5 where the ex
Page 163 and 164: D: Definition for Fitness Value Fit
Page 165 and 166: strategy; and End(Rescheduling) is
Page 167 and 168: (a) Optimisation Ratio on Total Cos
Page 169 and 170: makespan. In our two stage workflow
Page 171 and 172: 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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