Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing
Li Liu, Miao Zhang, Rajkumar Buyya, Qi Fan
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE | WILEY | Published : 2017
The cloud infrastructures provide a suitable environment for the execution of large-scale scientific workflow application. However, it raises new challenges to efficiently allocate resources for the workflow application and also to meet the user's quality of service requirements. In this paper, we propose an adaptive penalty function for the strict constraints compared with other genetic algorithms. Moreover, the coevolution approach is used to adjust the crossover and mutation probability, which is able to accelerate the convergence and prevent the prematurity. We also compare our algorithm with baselines such as Random, particle swarm optimization, Heterogeneous Earliest Finish Time, and g..View full abstract
Awarded by National Natural Science Foundation of China
Awarded by Beijing Natural Science Foundation
This work was supported by the National Natural Science Foundation of China (grant nos. 61370132, 61472033, and 61272432) and Beijing Natural Science Foundation (no. 4152034).