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

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either the maximum repetition time R is reached or all individual segments cannot be merged with their neighbours any more. Since the condition of “all individual segments cannot be merged with their neighbours any more” requires additional programming (or memory space) to store the segmentation results of the previous repetition, in practice, setting a maximum repetition time is a more efficient way to stop the repetition process. Meanwhile, the maximum repetition time can prevent the occurrence of endless loop (e.g. the deadlock of Split and Merge). According to our experiments, for most duration series, the repetition process (i.e. the do-until part as shown in Algorithm 1 in Table 4.2) will be repeated for no more than 3 times in most cases. Therefore, the maximum repetition time R is set as 5 in this chapter (including the experiments demonstrated in Section 4.4) but may be changed according to different system environments. Evidently, the aim here is to discover the minimum number of segments. The purpose of designing K-MaxSDev rather than employing existing algorithms is to take full advantage of these three generic algorithms to achieve a better performance. Specifically, Split is to divide the current segment into two equal parts. In the case where the current segment has odd number of sample points, the point at the middle position will be included into the left 2 3 4 5 6 7 segment. For example, if we Split a segment of { p 1 2 , p , p , p , p , p , p } , it then 2 3 4 2 3 becomes { p 1 2 , p , p , p , p , p , p } . Its subsequent sample needs to update their index 2 2 2 1 3 3 3 accordingly. Merge is to merge two neighbouring segments into one. For example, if 1 2 3 2 1 2 3 4 5 we Merge two segments of { p 3 , p , p } and { p , } , it then becomes { p , p , p , p , }. 3 3 1 4 p 4 2 2 2 2 2 2 3 3 3 3 p3 Its subsequent sample needs to update their index accordingly. Delete is to “delete” the previous segment after Merge, i.e. update the index of the sample points. Like in the above Merge operation, the fourth segment is deleted, i.e. the previous fifth segment now becomes the new fourth segment, so does the subsequent segments in the time series. The notations and the pseudo-code for the K-MaxSDev algorithm are presented in Table 4.1 and Table 4.2 respectively. 52
Table 4.1 Notations Used in K-MaxSDev Table 4.2 Algorithm 1: K-MaxSDev Time-Series Segmentation The empirical function for choosing K is based on the equal segmentation tests. The candidates of K are defined to be in the form of 2 i and should be a value of no bigger than n / 4 where i is a natural number and n is the length of the duration series. Otherwise, K-MaxSDev is turning into Bottom-up with low efficiency. The process is that we first choose some of the candidates and perform equal segmentation. We calculate the mean for each segment and rank each candidate according to the average of the absolute differences between the means of neighbouring segments. The intuition is that initial segmentation should ensure large differences between the means of segments, so as to guarantee the efficiency of the segmentation algorithm. The formula for the empirical function is denoted as Formula 4.1: 53
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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
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
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: specified with different means and
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
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
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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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