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

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equivalent number of times for the application of ACOWR to represent the cost (and/or overhead) given that the average CPU time for running ACOWR once in a SwinDeW-G node is 4.73 seconds as indicated in Figure 8.7. Here, with various scientific workflows same as in Figure 8.12, we compare the cost of a standalone ACOWR and the cost of standalone PTDA+ACOWR with that of our temporal framework. Here, for the comparison purpose, the standalone ACOWR and standalone PTDA+ACOWR are implemented separately as the sole strategies for handling all the three levels of temporal violations. The experiment settings are the same as in COM(1.28). Given similar global violation rates, the results for our temporal framework and the two standalone strategies are shown in Table 8.3. Table 8.3 Experimental Results on the Times of Temporal Violation Handling As discussed above, the total cost for each strategy is calculated as the equivalent times for the application of ACOWR. As shown in Table 8.3, for our three-level handling strategy with 2,000 activities, on average, the total times of PTDA are 4.3 (for level I temporal violation), the total times of ACOWR (for level II temporal violation) are 33.7, and the total times of PTDA+ACOWR are 5.8 (for level III temporal violation, and non-recoverable temporal violation if any) where 3.5 times with ACOWR twice and 2.3 with ACOWR once. Therefore, in such a case, the 158
equivalent times of ACOWR are calculated as 33.7+3.5*2+2.3*1=43.0. The other scenarios can be calculated in the similar way. Meanwhile, we can see that with 10,000 activities, for the total of 195.8 times of violations, the ratio of level I, level II, and level III temporal violations (and non-recoverable temporal violation if any) are 12.0%, 80.3%, and 7.7% respectively. The experimental results show that our three-level handling strategy has the smallest number of total temporal violation handling among all the strategies where the equivalent times of ACOWR for the workflow sizes of 2,000, 5,000 and 10,000 are 43.0, 97.2 and 182.1 respectively. This is mainly because in our temporal framework, different levels of temporal violations will be handled by their corresponding temporal violation handling strategies with the least cost. As for the standalone ACOWR and PTDA+ACOWR, their equivalent times of ACOWR are actually very close to each other. The main reason is that in the standalone PTDA+ACOWR, ACOWR will be executed for either once or twice according to the level of the occurred temporal violations, e.g. once for level I or level II, and maybe twice for level III. Therefore, the total cost of PTDA+ACOWR, i.e. the total times for the application of ACOWR in PTDA+ACOWR, are actually very close to that of the standalone ACOWR. Take the standalone PTDA+ACOWR for example, the cost reductions by our temporal framework are 14.7%, 25.6% and 30.1% respectively for the workflow size of 2,000, 5,000 and 10,000. Therefore, we can claim that our temporal framework is costeffective. Comprehensive results including the results for other workflows can be found in our online document (see footnote 8). Note that we have also investigated several expensive heavy-weight temporal violation handling strategies. Here, we take “Adding a New Resource” (“Add” in short) 10 as an example, the time overhead for Add mainly consists of two parts, viz. the data transfer time for workflow activities and the set-up time for a new resource. Given similar data transfer time in ACOWR and Add due to the re-mapping of tasks and resources, the set-up time for a new resource in our simulation environment is normally around several minutes, similar to that of a reserved resource in the Amazon Elastic Compute Cloud (EC2, http://aws.amazon.com/ec2/). Therefore, the time overhead for Add is very large and thus not suitable for handling temporal 10 Here, a new resource is a hot online machine in the system. The set-up time for a cold offline machine will normally be much higher. 159
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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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and the constraint for activity X 1
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ease of discussion and to avoid the
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Unfortunately, conventional discret
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The probability consistency range w
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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
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Figure 6.3 Checkpoint Selection wit
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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
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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: percentiles. The average global vio
Page 179 and 180: Chapter 9 Conclusions and Future Wo
Page 181 and 182: the real world, simulation experime
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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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