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

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IEEE Trans. on Software Engineering, vol. 26, no. 9, pp. 820-836, 2000. [10] R. Buyya and K. Bubendorfer, Market Oriented Grid and Utility Computing: Wiley Press, New York, USA, 2009. [11] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, "Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility", Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009. [12] R.-S. Chang, J.-S. Chang, and P.-S. Lin, "An Ant Algorithm for Balanced Job Scheduling in Grids", Future Generation Computer Systems, vol. 25, no. 1, pp. 20-27, 2009. [13] C. Chatfield, The Analysis of Time Series: An Introduction (Sixth Edition): Chapman and Hall/CRC, 2004. [14] H. Che-Lun, Y. Don-Lin, C. Yeh-Ching, and H. Ming-Chuan, "A Novel Mining Algorithm for Periodic Clustering Sequential Patterns", Proc. 19 th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE2006), pp. 1299-1308, Annecy, France, June 2006. [15] J. Chen, "Towards Effective and Efficient Temporal Verification in Grid Workflow Systems", PhD Thesis, Swinburne University of Technology, http://researchbank.swinburne.edu.au/vital/access/manager/Repository/swin: 7425, accessed on 1 st Nov. 2010. [16] J. Chen and Y. Yang, "Localising Temporal Constraints in Scientific Workflows", Journal of Computer and System Sciences, vol. 76, No. 6, pp. 464-474, 2010. [17] J. Chen and Y. Yang, "Multiple States based Temporal Consistency for Dynamic Verification of Fixed-time Constraints in Grid Workflow Systems", Concurrency and Computation: Practice and Experience, Wiley, vol. 19, no. 7, pp. 965-982, 2007. [18] J. Chen and Y. Yang, "A Taxonomy of Grid Workflow Verification and Validation", Concurrency and Computation: Practice and Experience, vol. 20, no. 4, pp. 347-360, 2008. [19] J. Chen and Y. Yang, "Activity Completion Duration Based Checkpoint Selection for Dynamic Verification of Temporal Constraints in Grid Workflow Systems", International Journal of High Performance and Computing Applications, vol. 22, no. 3, pp. 319-329, 2008. [20] J. Chen and Y. Yang, "Temporal Dependency based Checkpoint Selection for Dynamic Verification of Temporal Constraints in Scientific Workflow 172
Systems", ACM Trans. on Software Engineering and Methodology, in press, http://www.swinflow.org/papers/TOSEM.pdf, accessed on 1 st Nov. 2010. [21] J. Chen and Y. Yang, "Adaptive Selection of Necessary and Sufficient Checkpoints for Dynamic Verification of Temporal Constraints in Grid Workflow Systems", ACM Trans. on Autonomous and Adaptive Systems, vol. 2, no. 2, article 6, 2007. [22] J. Chen and Y. Yang, "Temporal Dependency based Checkpoint Selection for Dynamic Verification of Fixed-time Constraints in Grid Workflow Systems", Proc. 30th International Conference on Software Engineering (ICSE2008), pp. 141-150, Leipzig, Germany, May 2008. [23] J. Chen, Y. Yang, and T. Y. Chen, "Dynamic Verification of Temporal Constraints on-the-fly for Workflow Systems", Proc. 11 th Asia-Pacific Software Engineering Conference, pp. 30-37, Busan, Korea, Dec. 2004. [24] T. Y. Chen and R. Merkel, "An Upper Bound on Software Testing Effectiveness", ACM Trans. Software Engineering and Methodology, vol. 17, no. 3, pp. 1-27, 2008. [25] W.-N. Chen and J. Zhang, "An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements", IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 39, no. 1, pp. 29-43, 2009. [26] W.-N. Chen, J. Zhang, and Y. Yu, "Workflow Scheduling in Grids: An Ant Colony Optimization Approach", Proc. 2007 IEEE Congress on Evolutionary Computation (CEC2007), pp. 3308-3315, Singapore, Sept. 2007. [27] P. Choudhury, R. Kumar, and P. P. Chakrabarti, "Hybrid Scheduling of Dynamic Task Graphs with Selective Duplication for Multiprocessors under Memory and Time Constraints", IEEE Trans. on Parallel and Distributed Systems, vol. 19, no. 7, pp. 967-980, 2008. [28] K. Cooper, A. Dasgupta, K. Kennedy, C. Koelbel, and A. Mandal, "New Grid Scheduling and Rescheduling Methods in the GrADS Project", Proc. 18 th International Parallel and Distributed Processing Symposium (IPDPS2004), pp. 199-206, Santa Fe, New Mexico, USA, Apr. 2004. [29] DAGMan, Condor Project, http://www.cs.wisc.edu/condor/, accessed on 1 st Nov. 2010. [30] E. Deelman, D. Gannon, M. Shields, and I. Taylor, "Workflows and e- Science: An Overview of Workflow System Features and Capabilities", Future Generation Computer Systems, vol. 25, no. 6, pp. 528-540, 2008. 173
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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
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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.
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
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: Bibliography [1] W. M. P. van der A
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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