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

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on monitoring and detection of temporal violations (i.e. violations of temporal constraints) at workflow runtime, there is still no comprehensive framework which can support the whole lifecycle of time-constrained workflow applications in order to achieve high temporal QoS. Meanwhile, cloud computing adopts a marketoriented resource model, i.e. cloud resources such as computing, storage and network are charged by their usage. Hence, the cost for supporting temporal QoS (including both time overheads and monetary cost) should be managed effectively in scientific cloud workflow systems. This thesis proposes a novel probabilistic temporal framework and its strategies for cost-effective delivery of high QoS in scientific cloud workflow systems (or temporal framework for short in this thesis). By investigating the limitations of conventional temporal QoS related research, our temporal framework can provide a systematic and cost-effective support for time-constrained scientific cloud workflow applications over their whole lifecycles. With a probability based temporal consistency model, there are three major components in the temporal framework: Component 1 – temporal constraint setting; Component 2 – temporal consistency monitoring; Component 3 – temporal violation handling. Based on the investigation and analysis, we propose some new concepts and develop a set of innovative strategies and algorithms towards cost-effective delivery of high temporal QoS in scientific cloud workflow systems. Case study, comparisons, quantitative evaluations and/or mathematical proofs are presented for the evaluation of each component. These demonstrate that our new concepts, innovative strategies and algorithms for the temporal framework can significantly reduce the cost for the detection and handling of temporal violations while achieving high temporal QoS in scientific cloud workflow systems. Specifically, at scientific cloud workflow build time, in Component 1, a statistical time-series pattern based forecasting strategy is first conducted to predict accurate duration intervals of scientific cloud workflow activities. Afterwards, based on the weighted joint normal distribution of workflow activity durations, a probabilistic setting strategy is applied to assign coarse-grained temporal constraints through a negotiation process between service users and service providers, and then fine-grained temporal constraints can be propagated along scientific cloud workflows in an automatic fashion. At scientific cloud workflow runtime, in V
Component 2, the state of scientific cloud workflow execution towards specific temporal constraints, i.e. temporal consistency, is monitored constantly with the following two steps: first, a minimum probability time redundancy based temporal checkpoint selection strategy determines the workflow activities where potential temporal violations take place; second, according to the probability based temporal consistency model, temporal verification is conducted on the selected checkpoints to check the current temporal consistency states and the type of temporal violations. In Component 3, detected temporal violations are handled with the following two steps: first, an adaptive temporal violation handling point selection strategy decides whether a temporal checkpoint should be selected as a temporal violation handling point to trigger temporal violation handling strategies; Second, at temporal violation handling points, different temporal violation handling strategies are executed accordingly to tackle different types of temporal violations. In our temporal framework, we focus on metaheuristics based workflow rescheduling strategies for handling statistically recoverable temporal violations. The major contributions of this research are that we have proposed a novel comprehensive temporal framework which consists of a set of new concepts, innovative strategies and algorithms for supporting time-constrained scientific applications over their whole lifecycles in cloud workflow systems. With these, we can significantly reduce the cost for detection and handling of temporal violations whilst delivering high temporal QoS in scientific cloud workflow systems. This would eventually improve the overall performance and usability of cloud workflow systems because a temporal framework can be viewed as a software service for cloud workflow systems. Consequently, by deploying the new concepts, innovative strategies and algorithms, scientific cloud workflow systems would be able to better support large-scale sophisticated e-science applications in the context of cloud economy. VI
Page 1 and 2: A Novel Probabilistic Temporal Fram
Page 3 and 4: Declaration This thesis contains no
Page 5: Abstract Cloud computing is a lates
Page 9 and 10: http://dx.doi.org/10.1016/j.jpdc.20
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
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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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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
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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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