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

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for the best total makespan). For the similar reason, we also investigate the optimisation ratio on total cost with best local makespan as presented in Figure 8.10(d). As shown in Figure 8.10(d), for ACO, the maximum, minimum and average optimisation ratios for total cost with the best local makespan are 24.0%, 1.0% and 13.8% respectively. As shown in Figure 8.7(a), the maximum, minimum and mean optimisation ratios for total cost are 23.0%, 1.0% and 13.4% respectively. As for GA, the maximum, minimum and average optimisation ratios on total makespan with best local makespan are 4.0%, 1.0% and 2.5% respectively. While the maximum, minimum and mean optimisation ratios for makespan are 4.0%, 1.0% and 2.3% respectively. The reason for such results can be explained similarly as above for the optimisation rations on makespan. Moreover, by comparing Figure 8.10(d) and Figure 8.9(a), we can also see that the behaviours of the curves for GA and ACO in Figure 8.10(d) are very similar to their behaviours in Figure 8.9(a). Based on such a result, we can claim that our two stage workflow local rescheduling strategy, either with GA or ACO, is capable of compensating the violated workflow segment (searching for the best local makespan) as well as optimising the total cost of the entire integrated Task-Resource list (searching for the best total cost). To give a more direct and clearer view, Figure 8.10(e) shows the best fitness value for the first searching stage and the second searching stage in each round of experiment. It can be easily seen that, for either GA or ACO, the best fitness values for the two searching stages are almost the same in each round of experiment. Meanwhile, the distances between the best fitness values of GA and ACO generated candidates in each round are very close to each other. However, in 6 out of 10 rounds, ACO performs slightly better than GA. Therefore, the results direct show that our two stage workflow local rescheduling strategy can find out the candidates with best compensation rates in the second searching stage to handle temporal violations while maintaining the similar fitness value as the best candidates found in the first searching stage for optimisation of total makespan and total cost. The results of CPU time for both GA and ACO in each round of experiment are presented in Figure 8.11. For ACO, the maximum, minimum and mean CPU times are 9540.5, 1453.1 and 4730.1 milliseconds respectively. As for GA, the maximum, minimum and mean compensation ratios are 4182.8, 2368.9 and 3257.6 milliseconds 152
espectively. It is clearly that ACO requires much more CPU time than GA. The CPU time for ACO follows a steady increasing trend with the growing of number of activities. GA also follows an increasing trend but in a much slower way. In an overall sense, the CPU time for both GA and ACO are all trivial compared with the execution time of scientific workflow activities which normally take minutes or hours. Figure 8.11 CPU Time To summarise, based on the results shown in Figure 8.8 and Figure 8.9, both GA and ACO can effectively optimise the total makespan and total cost of the entire integrated Task-Resource list. Furthermore, based on the results shown in Figure 8.10(c) and Figure 8.10(d), we can claim that our two stage workflow local rescheduling strategy is capable of compensating the violated workflow segment as well as optimising the entire integrated Task-Resource list on both total makespan and total cost. Therefore, the requirements for rescheduling strategies for handling temporal violations as analysed in Section 8.1.2 are successfully met. Strategy Ranking based on User Preference The comprehensive evaluation results have been demonstrated above for both GA and ACO based rescheduling strategy to illustrate the effectiveness of our general two-stage workflow local rescheduling strategy. However, as for which strategy should be employed in the scientific workflow system to handle temporal violations, a ranking function is required to make a decision based on user preference. The four basic measurements (including the optimisation ratio on total 153
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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
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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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Page 121 and 122: Figure 6.3 Checkpoint Selection wit
Page 123 and 124: 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
Page 133 and 134: activities and around 300 violation
Page 135 and 136: Figure 7.3 Experimental Results (No
Page 137 and 138: Figure 7.5 Cost Reduction Rate vs.
Page 139 and 140: 8.1 Related Work and Problem Analys
Page 141 and 142: scientific processes, human interve
Page 143 and 144: alone does not involve any time def
Page 145 and 146: swap slower machines in the active
Page 147 and 148: TD( a p ) L{ ( ai , R j ) | i = p +
Page 149 and 150: For example, in Figure 8.2, local w
Page 151 and 152: value to sample all of the solution
Page 153 and 154: mapping task a i to resource R j fr
Page 155 and 156: have done some simulation experimen
Page 157 and 158: 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
Page 167 and 168: (a) Optimisation Ratio on Total Cos
Page 169: makespan. In our two stage workflow
Page 173 and 174: comparison results on the violation
Page 175 and 176: percentiles. The average global vio
Page 177 and 178: equivalent times of ACOWR are calcu
Page 179 and 180: Chapter 9 Conclusions and Future Wo
Page 181 and 182: the real world, simulation experime
Page 183 and 184: 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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