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

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negotiation process between service users and service providers based on either a time oriented or probability oriented manner. When the coarse-grained temporal constraints are set, the fine-grained temporal constraints for each individual workflow activities can be derived and propagated in an automatic fashion. Therefore, the time overheads and computation cost for temporal constraints setting are much smaller compared with conventional manual settings. The effectiveness of the strategy is demonstrated through a case study. COMPONENT II consists of Chapter 6. In Chapter 6, the existing state-of-the-art checkpoint selection and temporal verification strategy is modified to adapt to our probability based temporal consistency model so that only one type of checkpoint needs to be selected instead of previous multiple types, and only one type of temporal consistency state needs to be verified instead of previous multiple types. Accordingly, the minimum probability time redundancy based checkpoint selection strategy and probability temporal consistency based temporal verification strategy are provided. The mathematical 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. COMPONENT III consists of Chapter 7 and Chapter 8. In Chapter 7, an adaptive temporal violation handling point selection strategy is proposed. Temporal violation handling point selection is a novel idea of our latest study on workflow temporal verification. Based on the necessary and sufficient checkpoints, temporal violation handling point selection is to further select a subset of these checkpoints where the probability of temporal violations is higher than the specific threshold, i.e. temporal violation handling is indeed necessary. Motivated by adaptive testing techniques and requirements of cost-effectiveness, our temporal violation handling point selection strategy can select much fewer violation handling points than conventional strategies while maintaining satisfactory temporal QoS. In such a case, the overall cost for handling temporal violations can be significantly reduced. In Chapter 8, an overview of temporal violation handling strategies is presented. Given the basic requirements of automation and cost-effectiveness, we design a general light-weight two-stage local workflow rescheduling strategy which features 12
a two-stage searching process with metaheuristic algorithms. Such a general rescheduling strategy can ensure the balance between the execution of workflow instances with temporal violations and those without temporal violations. With such a general rescheduling strategy, two metaheuristic algorithms, viz. GA and ACO, are adapted and implemented, and then their performances are compared in four different measurements including the optimisation rate of makespan, the optimisation rate of cost, the time deficit compensation rate and the CPU time. Afterwards, the three-level temporal violation handling strategy designed in our temporal framework is presented and followed by comprehensive evaluation on its performance and cost. As detailed in Section 8.4, the handling strategy consists of three levels of temporal violations, viz. level I, level II and level III temporal violations, and their corresponding handling strategies, viz. PTDA (probability time deficit allocation), ACOWR (ACO based two-stage workflow local rescheduling), and PTDA+ACOWR (the hybrid of PTDA and ACOWR). Finally, in Chapter 9, we first make an overall cost analysis for each component in our temporal framework. Then, we summarise the new ideas discussed in this thesis, the major contributions of this research, and consequent further research works. 13
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: In Chapter 2, we introduce the rela
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
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
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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
Page 99 and 100:
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
Page 111 and 112:
Unfortunately, conventional discret
Page 113 and 114:
The probability consistency range w
Page 115 and 116:
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
Page 121 and 122:
Figure 6.3 Checkpoint Selection wit
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work and problem analysis. Section
Page 125 and 126:
expected time redundancy of subsequ
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the probability for self-recovery i
Page 129 and 130:
skipped if self-recovery applies. A
Page 131 and 132:
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
Page 159 and 160:
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
Page 177 and 178:
equivalent times of ACOWR are calcu
Page 179 and 180:
Chapter 9 Conclusions and Future Wo
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the real world, simulation experime
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ased temporal consistency model. Ac
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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