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

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Temporal constraints are not well emphasised in traditional workflow systems. However, some business workflow systems accommodate temporal information for the purpose of performance analysis. For example, Staffware provides the audit trail tool to monitor the execution of individual instances [1] and SAP business workflow system employs the workload analysis [51]. As for the support of temporal constraints in scientific workflow systems, an overview on the support of temporal constraints in representative scientific workflow systems is conducted by us and based on some of the work reported in [19, 98]. Since workflow modelling is highly related to the specification of temporal constraints, the overview also concerns with two aspects of the modelling language and the modelling tool (language-based, graph-based or both) in addition to the three aspects of whether they support the specification of temporal constraints (the specification of temporal constraints in workflow models), the management of temporal constraints (i.e. the setting and updating of temporal constraints) and the temporal verification (the verification of temporal constraints). As shown in Table 5.1, among the 10 representative scientific workflow projects (ASKALON [73], CROWN workflow component [74], DAGMan [29], GridBus [75], JOpera [76], Kepler [77], SwinDeW-G [95], Taverna [79], Triana [78] and UNICORE [80]), most projects use XML-like modelling language and support language-based or graph-based modelling tool. Therefore, in the modelling stage, a temporal constraint can either be inexplicitly specified as an element in the XML document or explicitly as a graphic component in the workflow template. As for the representation of temporal constraints, the management of temporal constraints and the support of temporal verification which we are most concerned with, only some of the projects such as ASKALON, DAGMan, GridBus, JOpera, Kepler, Taverna and SwinDeW-G clearly stated in their published literatures that temporal constraints are supported in their system QoS control or performance analysis. Yet, to our best knowledge, only SwinDeW-G has set up a series of strategies such as the probabilistic strategy for temporal constraint management [54] and the efficient checkpoint selection strategy to support dynamic temporal verification [20]. In summary, even though temporal QoS has been recognised as an important aspect in scientific workflow systems, the work in this area, e.g. the specification of temporal constraints and the support of temporal verification, is still in its infancy [19]. 70
Table 5.1 Overview on the Support of Temporal QoS Constraints 5.1.2 Problem Analysis Setting temporal constraints is not a trivial task. Many factors such as workflow structures, system performance and user requirements should be taken into consideration. Here, we present the basic requirements for temporal constraint setting by analysing two criteria for high quality temporal constraints. 1) Temporal constraints should be well balanced between user requirements and system performance. It is common that users often suggest coarse-grained temporal constraints based on their own interest while with limited knowledge about the actual performance of workflow systems. For example, it is not rational to set a 60 minutes temporal constraint to the segment which normally needs two hours to finish. Therefore, user specified constraints are normally prone to cause frequent temporal violations. To address this problem, a negotiation process between the user and the service provider who is well aware of the system performance is desirable to derive balanced coarse-grained temporal constraints that both sides are satisfied with. 2) Temporal constraints should facilitate both overall coarse-grained control and 71
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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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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
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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
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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
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Page 79 and 80: cycle. Here, we also trace back the
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Page 91 and 92: 2 2 can be denoted as N ( µ , σ )
Page 93 and 94: mean iteration times or with some p
Page 95 and 96: Figure 5.4 Choice Building Block No
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Page 99 and 100: 5.3.1 Calculating Weighted Joint Di
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Page 103 and 104: Therefore, it can be expressed as n
Page 105 and 106: Table 5.3 Specification of the Work
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Page 111 and 112: Unfortunately, conventional discret
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Page 115 and 116: 6.2.3 Temporal Checkpoint Selection
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Page 121 and 122: Figure 6.3 Checkpoint Selection wit
Page 123 and 124: work and problem analysis. Section
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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.
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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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