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

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sufficient checkpoints for temporal violation handling. Those temporal violation handling points are selected when the probability of self-recovery is below the adaptively adjusted threshold which represents the necessity for temporal violation handling. Details about the temporal violation handling point selection strategy will be presented in Chapter 7. Table 3.3 Component 3: Temporal Violation Handling The second step is the execution of temporal violation handling strategy at the selected handling point. As will be introduced in Section 8.4, in our temporal framework, three temporal violation handling strategies are employed to tackle three levels of temporal violations, viz. PTDA (Probability Time Deficit Allocation) for level I temporal violations, ACOWR (Ant Colony Optimisation based Workflow Rescheduling) for level II temporal violations, and PTDA+ACOWR (the hybrid PTDA and ACOWR) for level III temporal violations. Here, in Table 3.3, we take 32
PTDA+ACOWR as an example to introduce the temporal violation handling process. The details about these strategies will be presented in Chapter 8. For PTDA+ACOWR, Step 2.1 is to allocate probability time deficit. Based on PTDA, the occurred time deficit is allocated proportionally to the workflow activities of the subsequent local workflow segment. The purpose of time deficit allocation is to ensure the balance among the subsequent workflow activities to share the time deficit. Unlike the conventional strategy which assigns the time deficit to all the workflow activities remained in the workflow instance [17], our strategy only considers the subsequent local workflow segment after the selected handling point. Therefore, the number of workflow activities involved in the workflow rescheduling process is significantly reduced and thus can save the temporal violation handling cost. Step 2.2 is to generate an integrated Task-VM list. The integrated Task-VM list represents the current scheduling plan for the activities in the local workflow segment and their co-allocated tasks. Since the rescheduling of workflow activities in the local workflow segment will not only change the task assignment of their own but also inevitably change the task assignment of other co-allocated tasks. Therefore, if we do not consider the other co-allocated tasks, their execution time will probably be delayed and may even violate their own temporal constraints, if any. The purpose for the integrated Task-VM list is to consider all the tasks and virtual machines that will be affected in the rescheduling process. In such a case, a balance between the time compensation of violated workflow instance and the overall efficiency of the cloud workflow system could be achieved. Details about the integrated Task-VM list will also be presented in Section 8.3.1. Step 2.3 is to optimise the workflow local scheduling plan. The target of the optimisation process is to reduce the execution time of the activities in the local workflow segment so that the occurred time deficit can be compensated. In our twostage workflow local rescheduling strategy, there are two searching processes. The first searching process is a metaheuristics based searching process where the overall makespan and cost for the integrated Task-VM list are being optimised. Here, two representative metaheuristics including GA and ACO are investigated. As for the second searching process, selected valid candidates (scheduling plans) are compared in order to find the best solution where the execution time of the local workflow 33
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 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
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: conducted for three times, i.e. sta
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 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
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Table 5.3 Specification of the Work
Page 107 and 108:
and the constraint for activity X 1
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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
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100 times each. All the activity du
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normal distribution and correlated
Page 121 and 122:
Figure 6.3 Checkpoint Selection wit
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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
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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.
Page 139 and 140:
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
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
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(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
Page 175 and 176:
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
Page 185 and 186:
the whole lifecycle support for hig
Page 187 and 188:
which can be further investigated i
Page 189 and 190:
Bibliography [1] W. M. P. van der A
Page 191 and 192:
Systems", ACM Trans. on Software En
Page 193 and 194:
conjunction with 23 rd Parallel and
Page 195 and 196:
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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