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A Novel Probabilistic Temporal Fram
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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 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
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- 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
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- Page 61 and 62: 4.2 Related Work and Problem Analys
- Page 63 and 64: 4.2.2 Problem Analysis In cloud wor
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- Page 67 and 68: activity durations: characteristics
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- 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
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- 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
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- 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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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