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

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Computation: Practice and Experience, Wiley, to appear, http://www.ict.swin.edu.au/personal/xliu/doc/ConstraintManagement.pdf, accessed on 1 st Nov. 2010. [56] X. Liu, Y. Yang, Y. Jiang, and J. Chen, "Preventing Temporal Violations in Scientific Workflows: Where and How", IEEE Trans. on Software Engineering, 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 1 st Nov. 2010. [57] X. Liu, J. Chen, K. Liu, and Y. Yang, "Forecasting Duration Intervals of Scientific Workflow Activities based on Time-Series Patterns", Proc. 4 th IEEE International Conference on e-Science (e-Science08), pp. 23-30, Indianapolis, USA, Dec. 2008. [58] X. Liu, Y. Yang, J. Chen, Q. Wang, and M. Li, "Achieving On-Time Delivery: A Two-Stage Probabilistic Scheduling Strategy for Software Projects", Proc. 2009 International Conference on Software Process: Trustworthy Software Development Processes, pp. 317-329, Vancouver, Canada, May 2009. [59] 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, B. Furht and A. Escalante (Eds), pp. 309-332, Springer, 2010. [60] 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, Elsevier, vol. 84, no. 3, pp. 492-509, Mar. 2011. [61] 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", Proc. 16 th IEEE International Conference on Parallel and Distributed Systems (ICPADS2010), pp. 43-50, Shanghai, China, Dec. 2010. [62] 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", Proc. 10 th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID10), pp. 534-537, Melbourne, Australia, May 2010. [63] X. Liu, Z. Ni, D. Yuan, Y. Jiang, Z. Wu, J. Chen, and Y. Yang, "A Novel Statistical Time-Series Pattern based Interval Forecasting Strategy for Activity Durations in Workflow Systems," Journal of Systems and Software, 176
Elsevier, vol. 84, no. 3, pp. 354-376, Mar. 2011. [64] O. Marjanovic and M. E. Orlowska, "On Modelling and Verification of Temporal Constraints in Production Workflows", Knowledge and Information Systems, vol. 1, no. 2, pp. 157-192, 1999. [65] A. Martinez, F. J. Alfaro, J. L. Sanchez, F. J. Quiles, and J. Duato, "A New Cost-Effective Technique for QoS Support in Clusters", IEEE Trans. on Parallel and Distributed Systems, vol. 18, no. 12, pp. 1714-1726, 2007. [66] M. Moore, "An Accurate Parallel Genetic Algorithm to Schedule Tasks on a Cluster", Parallel Computing, vol. 30, no. pp. 567–583, 2004. [67] S. Nadarajah and S. Kotz, "Exact distribution of the max/min of two Gaussian random variables", IEEE Trans. On Very Large Scale Integration Systems, vol. 16, no. 2, pp. 210-212, 2008. [68] F. Nadeem and T. Fahringer, "Using Templates to Predict Execution Time of Scientific Workflow Applications in the Grid", Proc. 2009 IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID09), pp. 316-323, Shanghai, China, May 2009. [69] F. Nadeem, M. M. Yousaf, R. Prodan, and T. Fahringer, "Soft Benchmarks- Based Application Performance Prediction Using a Minimum Training Set", Proc. 2 nd IEEE International Conference on e-Science and Grid Computing (e-Science06), Amsterdam, Netherlands, Dec. 2006. [70] J. Oh and C. Wu, "Genetic-Algorithm-Based Real-Time Task Scheduling with Multiple Goals", Journal of Systems and Software, vol. 71, no. pp. 245- 258, 2004. [71] PDSEC, Proc. 10 th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing, in conjunction with 23 rd Parallel and Distributed Processing Symposium (IPDPS09), Rome, Italy, May 2009. [72] R. Prodan and T. Fahringer, "Overhead Analysis of Scientific Workflows in Grid Environments", IEEE Trans. on Parallel and Distributed Systems, vol. 19, no. 3, pp. 378-393, Mar. 2008. [73] Askalon Project, http://www.dps.uibk.ac.at/projects/askalon, accessed on 1 st Nov. 2010. [74] CROWN Project, CROWN portal, http://www.crown.org.cn/en/, accessed on 1 st Nov. 2010. [75] GridBus Project, http://www.gridbus.org, accessed on 1 st Nov. 2010. [76] JOpera Project, http://www.iks.ethz.ch/jopera, accessed on 1 st Nov. 2010. [77] Kepler Project, http://kepler-project.org/, accessed on 1 st Nov. 2010. 177
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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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 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: conjunction with 23 rd Parallel and
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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