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

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Specifically, the optimisation ratio on total makespan is defined as follows: OptMakespan = 100 % − AfterTotalMakespan BeforeTotalMakespan where AfterTotalMakespan is the total makespan, i.e. the completion time, of the entire activity set in the integrated Task-Resource list, after workflow rescheduling; and BeforeTotalMakespan is the total makespan before workflow rescheduling. Here, in each round of experiment, for comparison purpose, the average total makespan of all the best candidates in each generation of GA based rescheduling strategy is specified as the value for BeforeTotalMakespan. The optimisation ratio on total cost is defined as follows: OptCost = 100 % − AfterTotalCost BeforeTotalCost where AfterTotalCost is the total cost, i.e. the running cost, of the entire activity set in the integrated Task-Resource list, after workflow rescheduling; and BeforeTotalCost is the total running cost before workflow rescheduling. Similar to the total makespan, for the comparison purpose, the average total cost of all the best candidates in each generation of GA based rescheduling strategy is specified as the value for BeforeTotalCost. The compensation ratio is defined as follows: CompentationRatio = 100 % − AfterLocalMakespan BeforeLocalMakespan where AfterLocalMakespan is the execution time of the activities in the local workflow segment of the violated workflow instance, after workflow rescheduling (with either GA or ACO); and BeforeLocalMakespan is the corresponding execution time before workflow rescheduling (with either GA or ACO). Please note that here, the average makespan before workflow rescheduling with GA is not used as the benchmark. Since we want to investigate the capability of these strategies in the handling of temporal violations, and thus we need to compare the local makespan of the violated workflow segment before and after the same rescheduling strategy in the same workflow instance. The CPU time is defined as follows: CPUTime = Start( Rescheudling) − End( Rescheduling) where Start(Rescheduling) is the system time at the start of the rescheduling 146
strategy; and End(Rescheduling) is the system time when the rescheduling strategy is successfully completed, i.e. a satisfactory regenerated scheduling plan is found and ready to be implemented. Note that, in order to find out balanced solution with total makespan and cost, the best solution in our experiments are defined as the solution with the largest fitness value. Therefore, the best total makespan for each round of experiment is the total makespan of the solution with the best fitness value, and its cost is thus defined as the best total cost. The same rule is also applied to the best local makespan for calculating the compensation ratio. In the rest of this section, for the ease of presentation, we use ACO as the short for ACO based rescheduling strategy and GA as the short for GA based rescheduling strategy. The results of optimisation ratio on makespan are presented in Figure 8.8. Figure 8.8(a) depicts the optimisation ratio on total makespan for the total 10 rounds of experiments. For ACO, the maximum, minimum and mean optimisation ratios for makespan are 29.0%, 10.0% and 15.9% respectively. As for GA, the maximum, minimum and mean optimisation ratios for makespan are 29.0%, 13.0% and 19.9% respectively. The optimisation ratio for both GA and ACO is on a roughly decreasing trend with the growing number of activities (including both workflow activities and general tasks). In comparison, GA has a relatively more stable performance and a higher optimisation ratio than that of ACO. Figure 8.8(b) depicts the best total makespan for each round of experiment measured in basic time units, and the average total makespan for the candidates generated by GA is also shown in the figure for the comparison purpose. It can be easily seen that the best total makespan for the candidates generated by GA and ACO in each round is not far from each other, and 6 out of the 10 rounds of experiments are very close. Note that due to different settings as in Table 8.1, the total makespan for each round is not simply on the rise with the growing number of activities since the number of resources also increases although not in a linear way. 147
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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
Page 113 and 114: The probability consistency range w
Page 115 and 116: 6.2.3 Temporal Checkpoint Selection
Page 117 and 118: 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
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Page 129 and 130: skipped if self-recovery applies. A
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.
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
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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: D: Definition for Fitness Value Fit
Page 167 and 168: (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
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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