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

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8.3 A Novel General Two-Stage Local Workflow Rescheduling Strategy for Recoverable Temporal Violations 8.3.1 Description of the General Strategy Given the two fundamental requirements of automation and cost-effectiveness, the temporal violation handling strategies that we mainly investigate in this chapter are metaheuristics based workflow rescheduling algorithms. With metaheuristics based rescheduling algorithms, temporal violations are tackled by rescheduling the current Task-to-Resource assignment. Note that in cloud computing environments, computing resources are generally delivered as virtual machines (VMs). Therefore, in this chapter, Task-to-Resource assignment and Task-Resource list are interchangeable with Task-to-VM assignment or Task-VM list respectively. For workflow rescheduling, the following two challenges need to be considered: 1) the balance between the handling of temporal violations and the on-time completion of other workflow instances; 2) the suitable size of the Task-Resource list for costeffective rescheduling. The reason why we do not consider heuristics based scheduling algorithms in this scenario is mainly as follows: 1) Heuristics based scheduling algorithms can only optimise one objective at a time. Meanwhile, it is based on local optimisation where it can choose the best candidates at each local step for the optimisation objective. However, the generated solution is not necessarily the best solution for the optimisation objective in an overall sense. 2) From the viewpoint of metaheuristics based scheduling algorithms, the process of heuristics based scheduling algorithms is to construct valid solutions. Since metaheuristics based scheduling algorithms can use the valid solutions generated by heuristic scheduling algorithms as the initial input solution, the optimisation capability of metaheuristics based scheduling algorithm is theoretically guaranteed to be better than heuristic scheduling algorithms. To address the above two challenges, a novel general two-stage workflow local rescheduling strategy is designed for handing temporal violations. The pseudo-code for the general strategy is presented in Figure 8.1. 128
TD( a p ) L{ ( ai , R j ) | i = p + 1,... p + n, j = 1,2,.. K}; DAG{ Gi | a j ≤ am} 2 M{ µ , σ } i i R{ Ri , ES( Ri ), Cost( Ri ) | i = 1,2,... K} INITILISATION(); Run( Metaheuristic); Return( Solution, SolutionSet); COMPARE( Solutionct . , TD( a p )); BestSoluti on = Min( Solution. cost); Deploy(L); Figure 8.1 Two-Stage Local Workflow Rescheduling Strategy Here, “two-stage” means a two-stage searching process to strike a balance between the handling of temporal violations and the on-time completion of other workflow instances while “local” means the rescheduling of “local” workflow segments with existing resources. To handle temporal violations, the key optimisation objective is to maximise the compensation time, i.e. the difference of the scheduled execution time before and after rescheduling, in order to decrease the time deficit. After rescheduling, the activities for the violated workflow instance will be allocated with resources of higher performance and/or given earlier time slots in the job queue for execution. However, if we only focus on the violated workflow instance, the execution time of other workflow instances could be delayed and may violate temporal constraints of their own, if any. Therefore, a balance between the 129
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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
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2 2 can be denoted as N ( µ , σ )
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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 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 115 and 116: 6.2.3 Temporal Checkpoint Selection
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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 159 and 160: PTDA+ACOWR for Level III Violations
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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 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
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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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