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

More documents

Recommendations

Info

Ant Colony Optimisation Input: Integrated Task-to-VM list L{ ( Ti , VM j ) | i = 1,2,.., n; j = 1,2,.., m}; Tasks { T i , Cost i , Time i , DAG{ T i > T i+ 1}}; Virtual Machines { VM j, Price j , Speed j}. Output: Optimised Scheduling Plan //Optimising the overall makespan and cost //Initialisation of pheromone and other parameters for ACO algorithm // Package based random generation 1) INITIALISATION(ACO); 2) While (stopping condition is not met) { // initialise each ant for each ant { // select heuristics from duration-greedy, cost-greedy and overall-greedy 3) SELECTION (Heuristics); // build tackling sequence TS based on the input DAG task graphs 4) BUILDING( TS, DAGs); } // construct solutions 5) While (not end of TS) { // choose the resource for the next activity based on its earliest start time , earliest end time, and the bias of B ij (mapping resource R j to activity a i ) 6) CHOOSE ( ai, ai. est, ai. eet , R{ R j}, Bij ); // update the est and eet for all the subsequent activities; 7) UPDATE( est, eet); // update local pheromone for both duration and cost 8) LOCALUPDATE( dτ ij , dτ ij ); } // update the global pheromone based on the makespan and cost of the best-sofar solution bs bs 9) GLOBALUPDATE ( dτ ij , dτ ij , makespan , cos t ); // return the best-so-far solution and record into the SolutionSet 10) RETURN( Solution, SolutionSet); } // Selecting the BestSolution according to user preferences 11) BestSoluti on = COMPARE( SolutionSet , UserPref ); // deploy the scheduling plan 12) Deploy(L). Figure 8.4 ACO based Rescheduling Algorithm 8.3.4 Other Representative Metaheuristic Algorithms Besides GA and ACO introduced above, other representative metaheuristic algorithms often used in workflow scheduling/rescheduling include PSO (Particle Swarm Optimisation) [102], GRASP (Greedy Randomised Adaptive Search Procedure) [100] and SA (Simulated Annealing) [5]. In this thesis, we do not intend to investigate all of the metaheuristics algorithms. Readers interested in an overview of representative metaheuristics based algorithms may refer to [6]. Meanwhile, we 136
have done some simulation experiments to evaluate and compare the performance of GA, ACO and the other three metaheuristics algorithms mentioned above. For detailed results, please refer to our document online 8 . 8.4 Three-Level Temporal Violation Handling Strategy The above sections have presented an overview of temporal violation handling strategies. In this section, we present the temporal violation handling strategies designed in our temporal framework in detail. As introduced in Section 8.2, there are many temporal violation handling strategies available. To save the cost for handling temporal violations, different scientific cloud workflow systems may choose a set of different temporal violation handling strategies to deal with different levels of temporal violations with small to large time deficits. In our temporal framework, as shown in Figure 8.5, three levels of fine-grained recoverable temporal violations including level I, level II and level III are defined and their corresponding lightweight temporal violation handling strategies are PTDA (Probability Time Deficit Allocation), ACOWR (Ant Colony Optimisation based two-stage Workflow Rescheduling) and PTDA+ACOWR (the hybrid of PTDA and ACOWR) respectively. Figure 8.5 Fine-Grained Temporal Violations based on Three Temporal Violation Handling Strategies As discussed in Section 6.2.1, apart from AC and AI, all the probability temporal 8 http://www.ict.swin.edu.au/personal/xliu/doc/HandlingFramework.rar 137
Page 1 and 2:
A Novel Probabilistic Temporal Fram
Page 3 and 4:
Declaration This thesis contains no
Page 5 and 6:
Abstract Cloud computing is a lates
Page 7 and 8:
Component 2, the state of scientifi
Page 9 and 10:
http://dx.doi.org/10.1016/j.jpdc.20
Page 11 and 12:
17. X. Liu, J. Chen, and Y. Yang, A
Page 13 and 14:
4.2 RELATED WORK AND PROBLEM ANALYS
Page 15 and 16:
STRATEGY ..........................
Page 17 and 18:
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 and 70:
specified with different means and
Page 71 and 72:
Table 4.1 Notations Used in K-MaxSD
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
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 and 150: For example, in Figure 8.2, local w
Page 151 and 152: value to sample all of the solution
Page 153: mapping task a i to resource R j fr
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
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
show all

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

Create successful ePaper yourself

Delete template?

Save as template?