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

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the previous one and the service provider re-evaluates the consistency state, until it reaches or is above the minimum probability threshold. In contrast, the probability oriented negotiation process begins with the user’s initial suggestion of a probability value of α % , the service provider evaluates the execution time R(SW ) of the entire workflow process SW by the sum of all activity n durations as ∑ w i ( µ i + λσ i ) , where λ is the α % percentile. If R(SW ) is above the i= 1 maximum upper bound constraint of max(SW ) for the user, the probability value needs to be adjusted, otherwise the negotiation process terminates. The following process is the iteration that the user proposes a new probability value which is lower than the previous one and the service provider re-evaluates the workflow duration, until it reaches or is lower than the upper bound constraint. Figure 5.6 Negotiation Process for Setting Coarse-Grained Temporal Constraints As depicted in Figure 5.6, with the probability based temporal consistency, the time oriented negotiation process is normally where increasing upper bound constraints are proposed and evaluated with their temporal probability consistency states until the probability is above the user’s bottom-line confidence, while the probability oriented negotiation process is normally where decreasing temporal probability consistency states are proposed and estimated with their upper bound 82
constraints until the constraint is below the user’s acceptable latest completion time. In real practice, the user and service provider can choose either of the two negotiation processes, or even interchange dynamically if they want. However, on one hand, for users who have some background knowledge about the execution time of the entire workflow or some of the workflow segments, they may prefer to choose time oriented negotiation process since it is relatively easier for them to estimate and adjust the coarse-grained constraints. On the other hand, for users who have no sufficient background knowledge, probability oriented negotiation process is a better choice since they can make the decision by comparing the probability values of temporal consistency states with their personal bottom-line confidence values. 5.3.3 Setting Fine-grained Temporal Constraints The third step is to set fine-grained temporal constraints. In fact, this process is straightforward with the probability based temporal consistency model. Since our temporal consistency actually defines that if all the activities are executed with the duration of α % probability and their total weighted duration equals their upper bound constraint, we say that the workflow process is α % consistency at build-time. For example, if the obtained probability consistency is 90% with the confidence percentile λ of 1.28 (the percentile value can be obtained from any normal distribution table or most statistical program [87]), it means that all activities are expected for the duration of 90% probability. However, to ensure that the coarsegrained and fine-grained temporal constraints are consistent with the overall workflow execution time, the sum of weighted fine-grained temporal constraints should be approximate to their coarse-grained temporal constraint. Otherwise, even the duration of every workflow activity satisfies its fine-grained temporal constraint, there is still a good chance that the overall coarse-grained temporal constraints will be violated, i.e. the workflow cannot complete on time. Therefore, based on the same percentile value, the fine-grained temporal constraint for each activity is defined with Formula 5.2 to make them consistent with their overall coarse-grained temporal constraint. For a scientific workflow or workflow segment SW which has a coarse-grained temporal constraint of U (SW ) with α % consistency of λ percentile, if 83
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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
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
Page 69 and 70: 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
Page 77 and 78: to search for those activities whic
Page 79 and 80: cycle. Here, we also trace back the
Page 81 and 82: ecognition. Sliding Window ranks in
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: 5.3.1 Calculating Weighted Joint Di
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 and 122: Figure 6.3 Checkpoint Selection wit
Page 123 and 124: work and problem analysis. Section
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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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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