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

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violation handling points means that the probability of self-recovery (i.e. the time deficit can be automatically compensated by the saved execution time of the subsequent workflow segment) is below a threshold, i.e. the probability of temporal violations is high enough so that temporal violation handling is necessary. If a temporal violation handling point is selected, then temporal violation handling strategies should be executed. In our temporal framework, we mainly focus on those statistically recoverable temporal violations which can be recovered by light-weight temporal violation handling strategies 2 . For such a purpose, representative metaheuristics based workflow rescheduling strategies are investigated, adapted and implemented under a novel general two-stage local workflow rescheduling strategy to handle temporal violations. Since our temporal violation handling strategy is fully automatic and only utilises existing system resources without recruiting additional ones, the cost of temporal violation handling can be significantly reduced compared with many conventional heavy-weight temporal violation handling strategies. In our temporal framework, temporal violation handling is realised through a two-step process. The first step is an adaptive temporal violation handling point selection process. Motivated by software testing, a novel adaptive temporal violation handling point selection strategy is designed which can significantly reduce the overall cost of temporal violation handling while still maintaining satisfactory temporal QoS. In our strategy, a temporal violation handling point is selected only when the probability of self-recovery is below the adaptively adjusted threshold which denotes the requirement for temporal violation handling. After a temporal violation handling point is selected, corresponding handling strategies should been executed. In our framework, a novel temporal violation handling strategy is implemented which consists of three levels of temporal violations (viz. level I, level, II and level III temporal violations) and their corresponding handling strategies (viz. PTDA, ACOWR and PTDA+ACOWR). Our temporal violation handling strategy is mainly based on ACOWR, an ACO based two-stage local workflow rescheduling strategy, which attempts to compensate the time deficits with the reduced workflow execution time through optimising the workflow scheduling plan. Here, “two-stage” means a two-stage searching process designed in our strategy to strike a balance 2 Statistically recoverable and non-recoverable temporal violations are defined in Section 6.2.1 and their corresponding light-weight and heavy-weight handling strategies are overviewed in Section 8.2. 26
etween time deficit compensation and the completion time of other activities while “local” means the rescheduling of “local” workflow segments with “local” resources. To handle temporal violations, the key optimisation objective for workflow rescheduling is to maximise the compensation time. However, if we only focus on the speed-up of the workflow instances which has temporal violations, the completion time of other activities such as the segments of other workflow instances and ordinary non-workflow tasks, could be delayed and may violate temporal constraints of their own, if any. Therefore, a balance between time deficit compensation and the completion time of other activities needs to be considered. Otherwise, the overall efficiency of scientific cloud workflow systems will be significantly deteriorated. As for local rescheduling, it is designed for the requirement of cost effectiveness. Our temporal violation handling strategy only utilises existing resources which are currently deployed in the system instead of recruiting additional resources. Meanwhile, unlike global rescheduling which modifies the global Task-Resource list for the entire workflow instance, our strategy only focuses on the local workflow segment and optimises the integrated Task- Resource list. The detailed process will be introduced in Section 3.4 and the algorithms will be further presented in Chapter 7 and Chapter 8. 3.2 Component 1: Temporal Constraint Setting Component 1 is temporal constraint setting. As depicted in Table 3.1, the input includes process models (cloud workflow process definitions) and system logs for scientific cloud workflows. The output includes both coarse-grained and finegrained upper bound temporal constraints. Temporal constraints mainly include three types, i.e. upper bound, lower bound and fixed-time. An upper bound constraint between two activities is a relative time value so that the duration between them must be less than or equal to it. As discussed in [20], conceptually, a lower bound constraint is symmetric to an upper bound constraint and a fixed-time constraint can be viewed as a special case of upper bound constraint, hence they can be treated similarly. Therefore, in this thesis, we focus on upper bound constraints only. The first step is to forecast workflow activity duration intervals. Activity 27
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: Chapter 4 and Chapter 5 respectivel
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
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
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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
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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
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