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

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6.3 EVALUATION .............................................................................................. 98 6.3.1 Experimental Settings ........................................................................... 98 6.3.2 Experimental Results........................................................................... 100 6.4 SUMMARY ................................................................................................ 103 COMPONET III TEMPORAL VIOLATION HANDLING ............................ 104 CHAPTER 7 TEMPORAL VIOLATION HANDLING POINT SELECTION ................................................................................................................................. 104 7.1 RELATED WORK AND PROBLEM ANALYSIS .............................................. 105 7.1.1 Related Work....................................................................................... 105 7.1.2 Problem Analysis ................................................................................ 106 7.2 ADAPTIVE TEMPORAL VIOLATION HANDLING POINT SELECTION STRATEGY 107 7.2.1 Probability of Self-Recovery ............................................................... 107 7.2.2 Temporal Violation Handling Point Selection Strategy ...................... 109 7.3 EVALUATION ............................................................................................ 111 7.4 SUMMARY ................................................................................................ 119 CHAPTER 8 TEMPORAL VIOLATION HANDLING ................................... 120 8.1 RELATED WORK AND PROBLEM ANALYSIS .............................................. 121 8.1.1 Related Work....................................................................................... 121 8.1.2 Problem Analysis ................................................................................ 122 8.2 OVERVIEW OF TEMPORAL VIOLATION HANDLING STRATEGIES ................ 123 8.2.1 Temporal Violation Handling of Statistically Recoverable Temporal Violations ........................................................................................................ 124 8.2.2 Temporal Violation Handling of Statistically Non-Recoverable Temporal Violations ........................................................................................ 125 8.3 A GENERAL TWO-STAGE LOCAL WORKFLOW RESCHEDULING STRATEGY FOR RECOVERABLE TEMPORAL VIOLATIONS ....................................................... 128 8.3.1 Description of The General Strategy ................................................... 128 8.3.2 Metaheuristic Algorithm 1: Genetic Algorithm .................................. 132 8.3.3 Metaheuristic Algorithm 2: Ant Colony Optimisation ....................... 134 8.3.4 Other Representative Metaheuristic Algorithms ................................. 136 8.4 THREE-LEVEL TEMPORAL VIOLATION HANDLING STRATEGY ................. 137 8.5 COMPARISON OF WORKFLOW RESCHEDULING STRATEGIES ..................... 142 8.5.1 Experimental Settings ......................................................................... 142 8.5.2 Experimental Results........................................................................... 145 8.6 EVALUATION OF THREE-LEVEL TEMPORAL VIOLATION HANDLING XIII
STRATEGY ............................................................................................................ 154 8.6.1 Violation Rates of Local and Global Temporal Constraints ............... 155 8.6.2 Cost Analysis for The Three-Level Handling Strategy ....................... 157 8.7 SUMMARY ................................................................................................ 160 CHAPTER 9 CONCLUSIONS AND FUTURE WORK ................................... 161 9.1 OVERALL COST ANALYSIS FOR THE TEMPORAL FRAMEWORK ................. 161 9.2 SUMMARY OF THIS THESIS ....................................................................... 163 9.3 CONTRIBUTIONS OF THIS THESIS .............................................................. 166 9.4 FUTURE WORK ......................................................................................... 168 BIBLIOGRAPHY ................................................................................................. 171 APPENDIX: NOTATION INDEX ...................................................................... 181 XIV
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: 4.2 RELATED WORK AND PROBLEM ANALYS
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
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
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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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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
Page 131 and 132:
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
Page 179 and 180:
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
Page 189 and 190:
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
Page 197 and 198:
Distributed Computing Systems", Jou
Page 199 and 200:
Appendix: Notation Index Symbols α
Page 201 and 202:
PTR ( U ( SW ), ( a p + , a p + 1 m
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