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

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11. Y. Liu, Y. Jiang, X. Liu and S. L. Yang, CSMC: a combination strategy for multi-class classification based on multiple association rules, Knowledge-Based Systems (KBS), vol. 21, no. 8, pp. 786-793, Dec. 2008. Conference Papers: 12. X. Liu, Z. Ni, Z. Wu, D. Yuan, J. Chen and Y. Yang, An Effective Framework of Light-Weight Handling for Three-Level Fine-Grained Recoverable Temporal Violations in Scientific Workflows, Proceedings of the 16 th IEEE International Conference on Parallel and Distributed Systems (ICPADS10), pp. 43-50, Shanghai, China, Dec. 2010. 13. Z. Wu, Z. Ni, L. Gu and X. Liu, A Revised Discrete Particle Swarm Optimisation for Cloud Workflow Scheduling, Proceedings of the 2010 International Conference on Computational Intelligence and Security (CIS2010), pp. 184-188, Nanning, China, Dec. 2010. 14. X. Liu, J. Chen, Z. Wu, Z. Ni, D. Yuan and Y. Yang, Handling Recoverable Temporal Violations in Scientific Workflow Systems: A Workflow Rescheduling Based Strategy, Proceedings of the 10 th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid10), pp. 534-537, May 2010, Melbourne, Victoria, Australia. 15. D. Yuan, Y. Yang, X. Liu and J. Chen, A Cost-Effective Strategy for Intermediate Data Storage in Scientific Cloud Workflow Systems, Proceedings of the 24 th IEEE International Parallel & Distributed Processing Symposium (IPDPS10), Atlanta, USA, Apr. 2010. 16. X. Liu, Y. Yang, J. Chen, Q. Wang and M. Li, Achieving On-Time Delivery: A Two-Stage Probabilistic Scheduling Strategy for Software Projects, Proceedings of the 2009 International Conference on Software Process (ICSP09), Lecture Notes in Computer Science, Vol. 5543, pp. 317-329, Vancouver, Canada, May 2009. IX
17. X. Liu, J. Chen, and Y. Yang, A Probabilistic Strategy for Setting Temporal Constraints in Scientific Workflows, Proceedings of the 6 th International Conference on Business Process Management (BPM2008), Lecture Notes in Computer Science, Vol. 5240, pp. 180-195, Milan, Italy, Sept. 2008. 18. X. Liu, J. Chen, K. Liu and Y. Yang, Forecasting Duration Intervals of Scientific Workflow Activities based on Time-Series Patterns, Proceedings of the 4 th IEEE International Conference on e-Science (e-Science08), pp. 23-30, Indianapolis, USA, Dec. 2008. 19. Y. Yang, K. Liu, J. Chen, X. Liu, D. Yuan and H. Jin, An Algorithm in SwinDeW-C for Scheduling Transaction-Intensive Cost-Constrained Cloud Workflows, Proceedings of the 4 th IEEE International Conference on e-Science (e-Science08), pp. 374-375, Indianapolis, USA, Dec. 2008. 20. K. Ren, X. Liu, J. Chen, N. Xiao, J. Song, W. Zhang, A QSQL-based Efficient Planning Algorithm for Fully-automated Service Composition in Dynamic Service Environments, Proceedings of the 2008 IEEE International Conference on Services Computing (SCC08), pp. 301-308, Honolulu, Hawaii, USA, July 2008. X
Page 1 and 2: A Novel Probabilistic Temporal Fram
Page 3 and 4: Declaration This thesis contains no
Page 5 and 6: Abstract Cloud computing is a lates
Page 7 and 8: Component 2, the state of scientifi
Page 9: http://dx.doi.org/10.1016/j.jpdc.20
Page 13 and 14: 4.2 RELATED WORK AND PROBLEM ANALYS
Page 15 and 16: STRATEGY ..........................
Page 17 and 18: FIGURE 7.4 EXPERIMENTAL RESULTS (NO
Page 19 and 20: Chapter 1 Introduction This thesis
Page 21 and 22: Clearly, if the execution time of t
Page 23 and 24: to seek all the candidates. Further
Page 25 and 26: e unnecessary. Hence, an effective
Page 27 and 28: temporal violations, viz. recoverab
Page 29 and 30: In Chapter 2, we introduce the rela
Page 31 and 32: a two-stage searching process with
Page 33 and 34: QoS requirements. Generally speakin
Page 35 and 36: handling strategies employed in a s
Page 37 and 38: activity points to conduct temporal
Page 39 and 40: compensation, is believed as a suit
Page 41 and 42: successful completion. Specifically
Page 43 and 44: Chapter 4 and Chapter 5 respectivel
Page 45 and 46: etween time deficit compensation an
Page 47 and 48: service user’s requirements and t
Page 49 and 50: conducted for three times, i.e. sta
Page 51 and 52: PTDA+ACOWR as an example to introdu
Page 53 and 54: currently running. It is built on t
Page 55 and 56: workflow instance to determine whet
Page 57 and 58: framework for cost-effective delive
Page 59 and 60: pattern based forecasting strategy.
Page 61 and 62:
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
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TD( a p ) L{ ( ai , R j ) | i = p +
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For example, in Figure 8.2, local w
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value to sample all of the solution
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mapping task a i to resource R j fr
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have done some simulation experimen
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violations can still be recovered b
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PTDA+ACOWR for Level III Violations
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an integer from 1 to 5 where the ex
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D: Definition for Fitness Value Fit
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strategy; and End(Rescheduling) is
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(a) Optimisation Ratio on Total Cos
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makespan. In our two stage workflow
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espectively. It is clearly that ACO
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comparison results on the violation
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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
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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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