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

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Chapter 3 Novel Probabilistic Temporal Framework and Simulation Environment This thesis proposes a novel comprehensive probabilistic temporal framework to address the limitations of conventional temporal verification and the new challenges for cost-effective lifecycle support of time-constrained scientific workflow applications in cloud workflow systems. This chapter presents a high level description of our framework and its three major components with technical details described in the subsequent chapters. This chapter is organised as follows. Section 3.1 presents the framework overview. Section 3.2 introduces the design of the first component, i.e. temporal constraint setting. Section 3.3 introduces the design of the second component, i.e. temporal consistency monitoring. Section 3.4 introduces the design of the third component, i.e. temporal violation handing. Section 3.5 introduces our SwinCloud cloud computing infrastructure and SwinDeW-C cloud workflow prototype which serve as the simulation environment. Finally, Section 3.6 summarises this chapter. 3.1 Framework Overview The aim of our novel temporal framework is to provide a cost-effective solution to deliver high temporal QoS in scientific cloud workflow systems. The temporal framework is designed to support the whole lifecycle of workflow instances, i.e. from build time modelling stage to workflow runtime execution stage until 22
successful completion. Specifically, in our temporal framework, the foundation is the probability based temporal consistency model, and its capability is realised by three components which supports workflow instances throughout their lifecycles, including temporal constraints setting, temporal consistency monitoring and temporal violation handling. Figure 3.1 The Probabilistic Framework for Temporal QoS As depicted in Figure 3.1, our probabilistic framework consists of three components which can provide lifecycle support for high temporal QoS in scientific cloud workflow systems. The three inner cycles stand for the three important factors involved in the scientific cloud workflows: real world applications, scientific workflows, and cloud services. All the basic requirements for scientific cloud workflows come from real world applications. Real world applications need to be abstracted by service users with the support of workflow modelling tools and then create the workflow specifications. With the workflow specifications (usually in the form of visualised workflow templates) submitted by the service users, scientific workflows are executed by the cloud workflow systems with the underlying cloud computing infrastructures. Cloud workflow systems, as a type of platform services themselves, can utilise many other cloud services which provide software services or computing services. Evidently, the execution of scientific cloud workflows is 23
Page 1 and 2: A Novel Probabilistic Temporal Fram
Page 3 and 4: Declaration This thesis contains no
Page 5 and 6: Abstract Cloud computing is a lates
Page 7 and 8: Component 2, the state of scientifi
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: compensation, is believed as a suit
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
Page 57 and 58: framework for cost-effective delive
Page 59 and 60: pattern based forecasting strategy.
Page 61 and 62: 4.2 Related Work and Problem Analys
Page 63 and 64: 4.2.2 Problem Analysis In cloud wor
Page 65 and 66: significantly deteriorate the overa
Page 67 and 68: activity durations: characteristics
Page 69 and 70: specified with different means and
Page 71 and 72: Table 4.1 Notations Used in K-MaxSD
Page 73 and 74: 1) Duration series building As ment
Page 75 and 76: Table 4.5 Algorithm 4: Pattern Matc
Page 77 and 78: to search for those activities whic
Page 79 and 80: cycle. Here, we also trace back the
Page 81 and 82: ecognition. Sliding Window ranks in
Page 83 and 84: predicted value will be the one pre
Page 85 and 86: segmentation algorithm is capable o
Page 87 and 88: distributed soft real-time system,
Page 89 and 90: 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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