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

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fine-grained temporal consistency states can be quantitatively measured by probability confidence values. In order to facilitate the lifecycle support of high temporal QoS for cloud workflow applications, our framework is composed of three main components including temporal constraint setting, temporal consistency monitoring and temporal violation handling, which were further illustrated in three separate components. The main technical details of the framework were presented separately in three components, including COMPONENT I for temporal constraint setting (consisting of Chapter 4 and Chapter 5), COMPONENT II for temporal consistency monitoring (consisting of Chapter 6) and COMPONENT III for temporal violation handling (consisting of Chapter 7 and Chapter 8). • Chapter 4 presented a statistical time-series pattern based forecasting strategy for cloud workflow activity duration intervals. Specifically, based on a novel timeseries segmentation algorithm, statistical time-series patterns and their associated turning points can be discovered to facilitate the forecasting of activity duration intervals. Comparison experiments with representative forecasting strategies shown the better accuracy of our forecasting strategy. • Chapter 5 presented a novel probabilistic temporal constraint setting strategy. Based on the joint weighted normal distribution model, the execution time of the workflow instance or workflow segments are estimated effectively and efficiently with the support of four basic building blocks. Afterwards, through a negotiation process between service users and service providers based on either a time oriented or probability oriented process, the coarse-grained temporal constraints are assigned first. With those coarse-grained temporal constraints, the fine-grained temporal constraints for individual workflow activities are derived in an automatic fashion. We demonstrated the effectiveness of our strategy through a case study. • Chapter 6 presented the existing state-of-the-art checkpoint selection and temporal verification strategies which were modified to adapt to our probability 164
ased temporal consistency model. Accordingly, new definitions and the minimum probability time redundancy based checkpoint selection strategy and probability temporal consistency based temporal verification strategy are provided. Based on our improvement, only one type of checkpoints needs to be selected instead of previous multiple ones, and only one type of temporal consistency states needs to be verified instead of previous multiple ones. The theoretical proof has demonstrated that our adapted strategy is of the same necessity and sufficiency as the existing state-of-the-art checkpoint selection strategy but with better cost effectiveness. • Chapter 7 presented an adaptive temporal violation handling point selection strategy which is a novel idea proposed in this thesis. Based on the selected necessary and sufficient checkpoints, we further select a subset of them where the probability of temporal violations is above specific threshold, i.e. temporal violation handling is indeed necessary, as temporal violation handling points. The simulation experiments demonstrated that our temporal violation handling point selection strategy can select much fewer temporal violation handling points than the conventional strategies while maintaining satisfactory temporal QoS. • Chapter 8 presented an overview of temporal violation handling strategies for temporal violations. Given the basic requirements of automation and costeffectiveness, we proposed a general two-stage local workflow rescheduling strategy which features a two-stage searching process with metaheuristic algorithms. Furthermore, two metaheuristics algorithms, viz. genetic algorithm (GA) and ant colony optimisation (ACO), are adapted and implemented in the general strategy, and then their performances are compared comprehensively. Furthermore, the three-level temporal violation handling strategy which consists of three levels of temporal violations , viz. level I, level II and level III, and their corresponding handling strategies, viz. PTDA, ACOWR and PTDA+ACOWR, is proposed. The experimental results have shown that our strategy can ensure close to 0% global and local temporal violation rates, thus achieve high temporal QoS in scientific cloud workflow systems. In summary, wrapping up all chapters, we can conclude that with the research 165
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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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Page 135 and 136: Figure 7.3 Experimental Results (No
Page 137 and 138: 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
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Page 149 and 150: For example, in Figure 8.2, local w
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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
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Page 167 and 168: (a) Optimisation Ratio on Total Cos
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Page 171 and 172: espectively. It is clearly that ACO
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Page 175 and 176: percentiles. The average global vio
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Page 179 and 180: Chapter 9 Conclusions and Future Wo
Page 181: the real world, simulation experime
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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