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

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machine breakdown and network outage. These serious technical problems are normally controlled by effective system design which would only happen very infrequently (e.g. outside the “3σ ” probability range), and hence the frequency for statistically non-recoverable temporal violations is kept very low in a running scientific cloud workflow system. However, to deliver high temporal QoS, temporal violation handling strategies for both recoverable and non-recoverable temporal violations are required. In this chapter, we introduce some representative temporal violation handling strategies for dealing with both recoverable and non-recoverable temporal violations. 8.2.1 Temporal Violation Handling of Statistically Recoverable Temporal Violations Some common temporal violation handling strategies for handling recoverable temporal violations include NIL (no action), TDA (Time Deficit Allocation), workflow rescheduling and hybrid strategies. NIL(no action): NIL is not an actual temporal violation handling strategy but works well in the dynamic system environments where minor delays often take place. The reason is the same as the motivation for our adaptive temporal violation handling point selection strategy, i.e. the probability for self-recovery. Since some minor delays can probably be automatically compensated by the saved execution time of the subsequent workflow activities. However, the NIL strategy cannot perform alone since there is the risk for not handling minor temporal violations. Therefore, it should be accompanied by such as our adaptive temporal violation handling point selection strategy to ensure its effectiveness. Otherwise, it is not applicable for the “risk-free” system management purpose. TDA (Time Deficit Allocation): TDA is based on the similar idea of NIL but with a more active action which allocates the existing time deficit proportionally to the subsequent workflow activities. The purpose of such an action is to postpone the current temporal violation and determine the time deficit which should be compensated by each of the subsequent workflow activities so as to recover the current temporal violations. Given the allocated time deficit, some local optimisation may be applied to decrease the current scheduled execution time. However, TDA 124
alone does not involve any time deficit compensation strategies. Therefore, similar to NIL, TDA has no effectiveness on handling temporal violations if performed without real time deficit compensation processes. Workflow rescheduling: workflow rescheduling is to optimise the current workflow scheduling plan to adapt to the changes of the current system environments, especially the violation of temporal constraints. Different from workflow scheduling which aims to optimise some global objectives for the entire workflow instance such as cost and makespan, workflow rescheduling normally gives priority to some specific workflow activities so as to optimise some local objectives such as decreasing the cost or makespan of a local workflow segment. Therefore, workflow rescheduling usually targets at local optimised solutions. There are many workflow rescheduling strategies and among them the most popular ones are metaheuristics based workflow rescheduling strategies [6, 100]. In this chapter, GA and ACO based rescheduling strategies will be discussed in detail. Hybrid strategies: as analysed above, some light-weight temporal violation handling strategies such as NIL and TDA cannot perform well without the complementary of other strategies which can indeed compensate time deficits. Therefore, some hybrid strategies are designed. For example, the hybrid strategy of TDA and workflow rescheduling strategy, or specifically TDA and ACO based workflow rescheduling strategy (TDA+ACO for short), is proposed by us in [61] to deal with major but recoverable temporal violations. The experimental results in [61] have shown that the performance of the TDA+ACO is better than the standalone TDA and also standalone ACO based workflow rescheduling strategy. 8.2.2 Temporal Violation Handling of Statistically Non-Recoverable Temporal Violations Some common temporal violation handling strategies for handling non-recoverable temporal violations include resource recruitment, stop and restart, processor swapping and workflow restructure. Resource recruitment: resource recruitment is to add new resources at workflow runtime. It is a very common solution for speeding up workflow execution so as to compensate existing time delays. Adding a new resource is also very 125
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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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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
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
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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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Page 111 and 112: Unfortunately, conventional discret
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Page 121 and 122: Figure 6.3 Checkpoint Selection wit
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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.
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Page 141: scientific processes, human interve
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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
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Page 179 and 180: Chapter 9 Conclusions and Future Wo
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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
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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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