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

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[102] L. Zhang, Y. Chen, and B. Yang, "Task Scheduling based on PSO Algorithm in Computational Grid", Proc. 6 th International Conference on Intelligent Systems Design and Applications (ISDA2006), vol. 2, pp. 696-701, Jinan, China, 2006. [103] Y. Zhang, W. Sun, and Y. Inoguchi, "Predict Task Running Time in Grid Environments based on CPU Load Predictions", Future Generation Computer Systems, vol. 24, no. 6, pp. 489-497, 2008. [104] Y. Y. Zhang, W. Sun, and Y. Inoguchi, "Predicting Running Time of Grid Tasks based on CPU Load Predictions", Proc. 2006 IEEE International Conference on Grid Computing, pp. 286-292, Barcelona, Spain, Sept. 2006. [105] H. Zhuge, T. Cheung, and H. Pung, "A Timed Workflow Process Model”, Journal of Systems and Software, vol. 55, no. 3, pp. 231-243, 2001. 180
Appendix: Notation Index Symbols α% C a i AC ACO ACOWR AD AI B ij cτ ij Compensati onRatio CPUTime CSS CSS TD dτ ij Denotations α % Consistency (in the probability based model) th The i activity point of a scientific cloud workflow Absolute Consistency (in the probability based model) Ant Colony Optimisation Ant Colony Optimisation based twostage local Workflow Rescheduling The adaptive temporal violation handling point selection strategy Absolute Inconsistency (in the probability based model) The bias for mapping task a i to resource R j The pheromone for the desirability of mapping task a i to resource R j from the perspective of execution cost The compensation ratio of a rescheduling strategy The CPU time of a rescheduling strategy Checkpoint Selection Strategy The Temporal Dependency based Checkpoint Selection Strategy The pheromone for the desirability of mapping task a i to resource R j from 181
Page 1 and 2:
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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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
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 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: Distributed Computing Systems", Jou
Page 201 and 202: PTR ( U ( SW ), ( a p + , a p + 1 m
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