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

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R ( 0 < R < 1) is first generated. Afterwards, R is compared with the predefined fixed confidence threshold FT ( 0 < FT < 1) , if R is larger than FT , then a p is selected as a handling point. Otherwise, a p is not selected as a handling point. In RA , the fixed confidence threshold FT actually decides how much the portion of checkpoints will be selected as handling points. For example, if FT is set as 0.9, then a total of 1-0.9=0.1, i.e. 10% of the checkpoints will be selected as violation handling points while the others will be skipped without temporal violation handling. As shown in Table 7.2, the experiment settings are as follows. The process definitions are generated based on the pulsar searching example presented in Section 1.2 and the attribute settings are based historic data for the running of scientific workflow instances [56]. Meanwhile, the experiment settings are extended deliberately for the purpose of exploring a much larger parameter space to evaluate our strategy in general software applications of similar nature in distributed high performance computing environments. Table 7.2 Experimental Settings 112
To evaluate the average performance, 10 independent experiments with different scientific workflow sizes (ranging from 2,000 to 50,000 workflow activities with their mean activity durations between 30 to 3,000 time units) are executed 100 times each. All the activity durations are generated by the normal distribution model. The standard deviation is set as 10% of the mean activity duration to represent dynamic system performance. We have also implemented other representative distribution models such as exponential, uniform and a mixture of them. Since the experimental results are similar, this chapter only demonstrates the results with normal distribution. The constraint setting utilises the strategy introduced in [55] and the initial probability is set reasonably as 90% to serve as a type of QoS contract between users and service providers which is agreed at scientific workflow build time. Here the initial probability means that a scientific workflow has a 90% probability to finish on time, or in other words, 90% workflow instances can finish on time. Therefore, in our experiment, we specify the “satisfactory temporal correctness” as a temporal violation rate below 10% so as to meet the QoS contract. The average length of the workflow segments is set as 20 which is a moderate size for a workflow sub-process similar to those high-level activities depicted in Figure 1.1. Meanwhile, although under normal circumstances, the experiments can be conducted without noises (i.e. 0% noise), to simulate some worse case scenarios, random noises (i.e. a fixed rate of delays at random activities) are also injected to simulate extra delays accordingly along workflow execution due to potential unpredictable causes such as system overload and resource unavailability. For the comparison purpose, four rounds of simulation experiments with different random noise levels of 0%, 5%, 15% and 25% are conducted. Since the standard deviation of activity durations are set as 10% of the mean values, according to the “ 3 σ ” rule, there is a scarce chance that the noises, i.e. the delays, would exceed 30% of the mean durations [87]. Therefore, we set the upper bound of noises as 25% to investigate the effectiveness of our strategy under extreme situations. There are many temporal violation handling strategies available as will be introduced in Chapter 8. In our experiments, similar to the settings in Section 6.3.1, we use pseudo workflow local rescheduling with a reasonable average time compensation rate of 50% [56, 100]. The size of rescheduled workflow activities is randomly selected up 113
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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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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
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
Page 97 and 98: activities may be omitted for the e
Page 99 and 100: 5.3.1 Calculating Weighted Joint Di
Page 101 and 102: constraints until the constraint is
Page 103 and 104: Therefore, it can be expressed as n
Page 105 and 106: Table 5.3 Specification of the Work
Page 107 and 108: and the constraint for activity X 1
Page 109 and 110: 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
Page 117 and 118: 100 times each. All the activity du
Page 119 and 120: normal distribution and correlated
Page 121 and 122: Figure 6.3 Checkpoint Selection wit
Page 123 and 124: work and problem analysis. Section
Page 125 and 126: expected time redundancy of subsequ
Page 127 and 128: the probability for self-recovery i
Page 129: skipped if self-recovery applies. A
Page 133 and 134: activities and around 300 violation
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
Page 143 and 144: alone does not involve any time def
Page 145 and 146: swap slower machines in the active
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 153 and 154: mapping task a i to resource R j fr
Page 155 and 156: have done some simulation experimen
Page 157 and 158: violations can still be recovered b
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
Page 165 and 166: strategy; and End(Rescheduling) is
Page 167 and 168: (a) Optimisation Ratio on Total Cos
Page 169 and 170: makespan. In our two stage workflow
Page 171 and 172: espectively. It is clearly that ACO
Page 173 and 174: comparison results on the violation
Page 175 and 176: 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
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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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