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

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temporal consistency model, and hence to differentiate our strategy to the conventional TDA strategy, it is named PTDA. The pseudo-code for PTDA is presented in Figure 8.6. The actual process of PTDA is to borrow the expected time redundancy of the next workflow segment to compensate the current time deficit and then allocate the time deficit to the subsequent activities. The first task of PTDA is to calculate the expected time redundancy of the next workflow segment (Line 1). Normally, the next workflow segment is chosen as the workflow segment between the next activity of the checkpoint a p , i.e. a p+ 1 , and the end activity of the next local temporal constraint, say a p + m here. The expected time redundancy is defined as the difference between the temporal constraint and the execution time for the minimum acceptable temporal consistency state (Line 1). The expected time redundancy can be borrowed to compensate the time deficits for level I temporal violations. After that, the expected time redundancy is allocated to the subsequent activities within the workflow segment according to the proportion of their mean activity time redundancy as defined in [17] (Line 2 to Line 4). After that, re-assigned temporal constraints for each activity are returned (Line 5). Therefore, the actual compensation process for PTDA is to tighten the temporal constraints of the subsequent activities so as to ensure that the current scientific workflow execution is close to absolute consistency. However, since PTDA does not decrease the actual activity durations of the subsequent activities, it can handle level I violations of some local workflow segments but has no effectiveness on global constraints, e.g. the final deadline. To actually decrease the execution time of workflow segments (required by level II and level III temporal violations), more sophisticated handling strategies such as workflow rescheduling are required. ACOWR for Level Violations Since the pseudo-code for ACOWR is the integrated pseudo-code of the twostage local workflow rescheduling and ant colony optimisation strategy which are presented in Section 8.3.1 (Figure 8.1) and Section 8.3.3 (Figure 8.4) respectively, it is omitted here. 140
PTDA+ACOWR for Level III Violations The hybrid strategy of PTDA and ACOWR (denoted as PTDA+ACOWR) is responsible for handling level III temporal violations. ACOWR is capable of removing most time deficits and handle level II temporal violations. However, due to the larger amount of time deficits occurred in level III temporal violations, we propose the hybrid strategy of PTDA and ACOWR to achieve stronger temporal violation handling capability. The pseudo-code for PTDA+ACOWR is presented in Figure 8.7. R TD( a p ) WS ( a p+ 1, a p+ 1,... a p+ m ) L{ ( ai , R j ) | i = p + 1,... p + n, j = 1,2,.. K}; DAG{ Gi | a j ≤ am} U ( a p + 1, a p + n ) 2 M{ µ , σ } i i i i i ,... { R , ES( R ), Cost( R ) | i = 1,2 K} p+ n TR( a , ) ( , ) ∑ p+ 1 a p+ n = U a p+ 1 a p+ n − ( µ i + λθ * σ i ); i= p+ 1 TD ( a p ) = TD ( a p ) − TR( a p+ 1, a p+ m); TD( ) > 0 a p TD( a p ) = TD( a p ) − BestSolution. ct; TD( ) > 0 a p WS = WS ( ap+ m+ 1, ap+ m+ 2,... a p+ m+ m' ); UPDATE( L{}, DAG{}, M{}, R{}); Figure 8.7 PTDA+ACOWR Algorithm The hybrid strategy starts with the first stage of PTDA (Line 1 to Line 2). However, here we only utilise the expected time redundancy to decrease the current time deficits without allocating them since the subsequent activities will be further rescheduled by ACOWR. The second stage is an iterative process of ACOWR (Line 3 to Line 8). Here, ACOWR is called for the first time to compensate the time deficit with the best solution for the subsequent workflow segment (Line 4 to Line 5). However, if the time deficit is not removed, the second subsequent workflow 141
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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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cycle. Here, we also trace back the
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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
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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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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
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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 127 and 128: 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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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
Page 151 and 152: value to sample all of the solution
Page 153 and 154: mapping task a i to resource R j fr
Page 155 and 156: have done some simulation experimen
Page 157: violations can still be recovered b
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
Page 181 and 182: the real world, simulation experime
Page 183 and 184: 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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