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

mean duration plus 1.28* variance and cost constraint is defined as the triple of
the corresponding time constraint. The makespan of a workflow is defined as the
latest finished time on all the virtual machines and the total cost of a workflow is
defined as the sum of task durations multiply the prices of their allocated virtual
machines. As for the three basic performance measurements, the optimisation rate
on makespan equals to the mean makespan subtract the minimum makespan, then is
divided by the mean makespan; the optimisation rate on cost equals to the mean cost
subtract the minimum cost, then is divided by the mean cost; the CPU time used is
defined as the average execution time of each algorithm running on a standard
SwinDeW-C node.
B: Parameter Settings for GA
In GA, 50 new individuals are created during each iteration. The crossover rate
is set to 0.7 and the mutation rate is 0.1. To make a trade-off between effectiveness
and efficiency, we design a compound stopping condition with four parameters: the
minimum iteration times, the maximum iteration times, the minimum increase of
optimisation rate on time (the increase of optimisation rate on time: the minimum
makespan of last iteration subtracts the minimum makespan of the current iteration
and divided by the one of the last iteration), the minimum increase of optimisation
rate on cost (similar to that of makespan). Specifically, the evolutionary process
iterates at least a minimum of 100 times. After 100 times iterations, the iteration will
stop on condition that the maximum iteration times are met; or the increase of
optimisation rate on time is less than 0.02; or the increase of optimisation rate on
cost is less than 0.02.
C: Parameter Settings for ACO
In ACO, 50 new ants are created in each iteration. Since we focus on both the
reduction of makespan and the reduction of cost, half of them are created as
duration-greedy and another half as cost-greedy. The maximum iteration times are
set as 1,000 and the minimum iteration times are 100. The weights of pheromone
and heuristic information are set to be 1 and 2. The probability of selecting the
implementation with the largest value of B ij is 0.8. Local pheromone updating rate is
0.1 and the global pheromone updating rate is also 0.1. For fairness, the stopping
condition is the same as defined in GA.
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