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

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The Author’s Publications Book Chapters: • X. Liu, D. Yuan, G. Zhang, J. Chen and Y. Yang, SwinDeW-C: A Peer-to-Peer Based Cloud Workflow System. Handbook of Cloud Computing, pages 309-332, Springer, ISBN: 978-1-4419-6523-3, 2010. Journal Papers: 1. X. Liu, Z. Ni, D. Yuan, Y. Jiang, Z. Wu, J. Chen, Y. Yang, A Novel Statistical Time-Series Pattern based Interval Forecasting Strategy for Activity Durations in Workflow Systems, Journal of Systems and Software (JSS), Elsevier, vol. 84, no. 3, pp. 354-376, Mar. 2011. 2. X. Liu, Z. Ni, Z. Wu, D. Yuan, J. Chen and Y. Yang, A Novel General Framework for Automatic and Cost-Effective Handling of Recoverable Temporal Violations in Scientific Workflow Systems, Journal of Systems and Software (JSS), Elsevier, vol. 84, no. 3, pp. 492-509, Mar. 2011. 3. Z. Wu, X. Liu, Z. Ni, D, Yuan and Y. Yang, A Market‐Oriented Hierarchical Scheduling Strategy in Cloud Workflow Systems, Journal of Supercomputing (JSC), http://www.ict.swin.edu.au/personal/xliu/papers/JSC-Scheduling.pdf, to appear (accepted on Sept. 7, 2010), accessed on 1 st Dec. 2010. 4. D. Yuan, Y. Yang, X. Liu and J. Chen, On-demand Minimum Cost Benchmarking for Intermediate Datasets Storage in Scientific Cloud Workflow Systems. Journal of Parallel and Distributed Computing (JPDC), in press, VII
http://dx.doi.org/10.1016/j.jpdc.2010.09.003, 2010. 5. X. Liu, Y. Yang, Y. Jiang and J. Chen, Preventing Temporal Violations in Scientific Workflows: Where and How, IEEE Transactions on Software Engineering (TSE), in press, http://doi.ieeecomputersociety.org/10.1109/ TSE.2010.99, (http://www.ict.swin.edu.au/personal/xliu/papers/TSE-2010-01- 0018.pdf), accessed on 1st Nov. 2010. 6. D. Yuan, Y. Yang, X. Liu, G. Zhang and J. Chen, A Data Dependency Based Strategy for Intermediate Data Storage in Scientific Cloud Workflow Systems, Concurrency and Computation: Practice and Experience (CCPE), in press, http://onlinelibrary.wiley.com/doi/10.1002/cpe.1636/pdf, 2010. 7. D. Yuan, Y. Yang, X. Liu and J. Chen, A Data Placement Strategy in Cloud Scientific Workflows, Future Generation Computer Systems (FGCS), Elsevier, Vol. 76, No. 6, pp. 464-474, Sept. 2010. 8. X. Liu, Z. Ni, J. Chen and Y. Yang, A Probabilistic Strategy for Temporal Constraint Management in Scientific Workflow Systems, Concurrency and Computation: Practice and Experience (CCPE), Wiley, http://www.ict.swin.edu.au/personal/xliu/doc/ConstraintManagement.pdf, to appear (accepted on June 17, 2009), accessed on 1 st Nov. 2010. 9. K. Liu, H. Jin, J. Chen, X. Liu, D. Yuan and Y. Yang, A Compromised-Time- Cost Scheduling Algorithm in SwinDeW-C for Instance-Intensive Cost- Constrained Workflows on Cloud Computing Platform, International Journal of High Performance Computing Applications (IJHPC), vol. 24, no. 4, pp. 445-456, Nov. 2010. 10. Y. Jiang, Y. Liu, X. Liu and S. Yang, Integrating classification capability and reliability in associative classification: A β-stronger model, Expert Systems with Applications (ESA), vol. 37, no. 5, pp. 3953-3961, May 2010. VIII
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: Component 2, the state of scientifi
Page 11 and 12: 17. X. Liu, J. Chen, and Y. Yang, A
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
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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
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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
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equivalent times of ACOWR are calcu
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Chapter 9 Conclusions and Future Wo
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the real world, simulation experime
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ased temporal consistency model. Ac
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the whole lifecycle support for hig
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which can be further investigated i
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Bibliography [1] W. M. P. van der A
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Systems", ACM Trans. on Software En
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conjunction with 23 rd Parallel and
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Elsevier, vol. 84, no. 3, pp. 354-3
Page 197 and 198:
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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