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

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solution, the integrated Task-Resource list is rescheduled and deployed on related resources (Line 9). 8.3.2 Metaheuristic Algorithm 1: Genetic Algorithm GA is a search technique often employed to find the exact or approximate solutions to optimisation and search problems [43, 99]. GA is a specific class of evolutionary algorithms inspired by evolutionary biology. In GA, every solution is represented with a string, also known as a chromosome, which follows the semantics defined by the encoding method. After encoding, the candidate solutions, i.e. the initial population, need to be generated as the basic search space. Within each generation, three basic GA operations, i.e. selection, crossover and mutation, are conducted to imitate the process of evolution in nature. Finally, after the stopping condition is met, the chromosome with the best fitness value is returned, representing the best solution found in the search space. That ends the whole GA process. In recent years, GA has been adopted to address large complex scheduling problems and proved to be effective in many distributed and dynamic resource environments, such as parallel processor systems and grid workflow systems [66, 90]. The detailed introduction for each operation of GA can be found in our work in [56] and [94], and hence omitted here. As depicted in Figure 8.3, the first searching phase of GA based task-level scheduling is to optimise the overall makespan and cost for the integrated Task-Resource list through GA (Line 1 to Line 9). The GA algorithm starts from encoding (Line 1). Here two-dimension encoding method is adopted. The first dimension represents the scheduled acts and the second represents the resource allocated to the corresponding act in the first dimension. GA starts with the generation of a fixed size initial population (Line 2). Because the quality of initial population is critical for the final outcome, package based random algorithm is applied to generate the initial population. After that, the algorithm starts searching for the best solution iteratively until the stopping condition, e.g. the maximum generation, is met (Line 3 to Line 9). Three fundamental operations of GA including selection, crossover and mutation take actions in sequence. Stochastic universal sampling (SUS) [43] for selecting potentially useful solutions as parents for recombination is used in the selection operation (Line 4). SUS uses a single random 132
value to sample all of the solutions by choosing them at evenly spaced intervals. The solution candidates generated during initialisation phase are all legal (i.e. they all satisfy the precedence relationship as defined in DAGs); however, conventional crossover will probably make some individuals illegal. To keep the diversity of the population, single point crossover strategy is employed (Line 5). When two parents are both legal, single point crossover ensures their children are legal. So before the mutation, the whole population is valid [70]. The third genetic operation is mutation (Line 6) where the allocated resource is mutated, i.e. substituted for another resource, at a randomly selected cell of a chromosome. The mutation rate is normally set to a small probability value such as 10% since mutation can easily destroy the correct precedence relationship and result in invalid solutions. The major effect of mutation is that it can introduce diversity into the population to help it jump out of local optimal traps. However, it can make some individuals invalid. These invalid individuals should be eliminated through validation and replacement (Line 7, Line 8). Since cloud workflow system is a market-oriented system, the candidate scheduling solution is expected to satisfy the QoS constraints according to the service contract. The last operation of the first phase is check which verifies whether the candidate individual should be retained or discarded. During the second searching phase, the SolutionSet is compared with the user preference, i.e. UserPref, and the best scheduling plan L is deployed (Line 10 to Line 12). Both makespan and costs are taken into account in UserPref which defines the specific preference of users towards the two factors. For example, the UserPref can be the minimum makespan, the minimum cost or a balance ratio between makespan and cost. The best scheduling plan (BestSolution) is selected from the satisfied population (Line 10). BestSolution is defined as the best solution according to UserPref among all the valid solutions. Since BestSolution is represented in the two dimensional vector, it should be decoded back to L as an integrated Task-to-VM list (Line 11). The last step of the whole algorithm is to deploy the L (Line12). 133
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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
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
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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
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 and 130: skipped if self-recovery applies. A
Page 131 and 132: To evaluate the average performance
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: For example, in Figure 8.2, local w
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Page 155 and 156: have done some simulation experimen
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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
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
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PTR ( U ( SW ), ( a p + , a p + 1 m
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