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

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Adaptive Modification Process for Probability Threshold: Given current probability threshold PT ( 0 < PT < 1) and checkpoint a p , i.e. a temporal violation is detected at a p , PT is updated as PT * (1 + γ ) . Afterwards, based on our handling point selection rule, if a p is not selected as a handling point, then PT is updated as PT * (1 − γ ) ; otherwise, PT remains unchanged. Here, γ stands for the update rate which is a small percentile. Table 7.1 Adaptive Temporal Violation Handling Point Selection Strategy The adaptive modification process is to increase the probability threshold PT , i.e. the probability of temporal violation handling, where temporal violation handling is triggered; or to decrease PT where temporal violation handling is 110
skipped if self-recovery applies. As shown in Table 7.1, our temporal violation handling point selection strategy is to apply the adaptive modification process for probability threshold to the handling point selection rule. The input parameters for the strategy include the necessary and sufficient checkpoint, the probability time deficit, the probability time redundancy and the probability threshold. Since handling point selection is based on the results of the state-of-the-art checkpoint selection strategy, i.e. the minimum time redundancy based checkpoint selection strategy where the time overhead is negligible, the computation of the probability time redundancy according to Definition 7.2 can be included in the checkpoint selection process [56]. Therefore, since PT is a variant, the only extra cost for computing the input parameters is for the probability time deficit according to Definition 7.1. After the input parameters are specified, the current probability of self-recovery is obtained according to Definition 7.3 and compared with the adaptively updated probability threshold to further decide whether the current checkpoint needs to be selected as a temporal violation handling point or not. The time overhead for our temporal violation handling point selection strategy can be viewed negligible since it only requires several steps of simple calculation. In the next section, we will evaluate the performance of our strategy comprehensively. 7.3 Evaluation In the existing work, temporal violation handling point selection is regarded the same as checkpoint selection, hence the existing state-of-the-art checkpoint selection strategy (with the minimum number of selected checkpoints) CSS TD [22] is implemented as the benchmark for comparison with ours. The detailed description for CSS TD and its performance better than the other 8 representative strategies are omitted in this chapter which can be found in [22]. In addition, to evaluate the effectiveness of our adaptive strategy, a pure random handling point selection strategy denoted as RA is also implemented. RA selects handling points at every necessary and sufficient checkpoint in a pure random fashion. The selection rule of RA is as follows: at a necessary and sufficient checkpoint a p , a random value 111
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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
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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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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 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 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: the probability for self-recovery i
Page 131 and 132: To evaluate the average performance
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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
Page 173 and 174: comparison results on the violation
Page 175 and 176: percentiles. The average global vio
Page 177 and 178: 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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