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

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time-series pattern based forecasting strategy (presented in Chapter 4) and the four basic building blocks (presented in Section 5.3.1), high quality coarse-grained temporal constraints can be assigned through effective negotiation process between service users and service providers. Afterwards, fine-grained temporal constraints can be derived and propagated along scientific cloud workflow in an automatic fashion. Therefore, given a set of high quality coarse-grained and fine-grained temporal constraints, the time overheads and computation cost for temporal constraints setting are much smaller compared with conventional manual settings. Therefore, we can claim that Component I is cost effective. For Component II (temporal consistency monitoring), with the novel probability based temporal consistency model (presented in Section 5.2) and the definition of statistically recoverable and non-recoverable temporal violations (presented in Section 6.2), only one type of checkpoint needs to be selected and only one type of temporal consistency state needs to be verified. Therefore, given the same necessity and sufficiency, the overall cost for checkpoint selection and temporal verification can be significantly reduced comparing with conventional multi-state based temporal consistency based strategies. Therefore, we can claim that Component II is cost effective. For Component III (temporal violation handling), with the novel adaptive temporal violation handling point selection strategy (presented in Chapter 7) which only selects a small subset of necessary and sufficient checkpoints, and the threelevel temporal violation handling strategy (presented in Chapter 8) which consists of three light-weight handling strategies, viz. PTDA, ACOWR (based on the proposed general two-stage local workflow rescheduling strategy) and PTDA+ACOWR, the overall cost for temporal violation handing can be significantly reduced while maintaining high temporal QoS comparing with conventional temporal violation handling which is conducted at every necessary and sufficient checkpoint. Therefore, we can claim that Component III is cost effective. Note that the performance of the temporal framework may vary in different scientific cloud workflow system environments. Actually, in order to achieve the best performance, the settings and parameters for the components in our temporal framework can all be modified according to the real world system environments. Therefore, based on the statistics of system logs or other source of historical data in 162
the real world, simulation experiments should be conducted first to specify the best parameters for these components in our framework instead of using default values. To conclude, based on systematic analysis for the lifecycle support of temporal QoS in scientific cloud workflow systems, a series of new concepts, innovative strategies and algorithms have been designed to successfully fulfil the system requirements of each component in a cost-effective fashion. Therefore, we can claim that our probabilistic temporal framework can indeed achieve the cost-effective delivery of high QoS in scientific cloud workflow systems. 9.2 Summary of This Thesis The research objective described in this thesis is to investigate a series of new concepts, innovative strategies and algorithms for a temporal framework in order to achieve cost-effective delivery of high temporal QoS in scientific cloud workflow systems. The thesis was organised as follows: • Chapter 1 introduced temporal QoS in scientific cloud workflow systems. A motivating example in Astrophysics has been demonstrated to analyse the problems for delivering high temporal QoS in scientific cloud workflow systems. Chapter 1 also described the aims of this work, the key issues to be addressed in this thesis and the structure of this thesis. • Chapter 2 overviewed the general related work on temporal QoS in workflow systems and analysed their limitations. Specifically, a temporal consistency model is used to define temporal consistency states; temporal constraint setting is to assign both global and local temporal constraints at workflow build time; temporal checkpoint selection and temporal verification are the main steps for the monitoring of workflow execution and the detection of temporal violations; temporal violation handling is to compensate the existing time deficits by temporal violation handling strategies. • Chapter 3 presented the overview of our probabilistic framework. The basis of the framework is a novel probability based temporal consistency model where 163
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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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Page 131 and 132: To evaluate the average performance
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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 147 and 148: TD( a p ) L{ ( ai , R j ) | i = p +
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: Chapter 9 Conclusions and Future Wo
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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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