PCP-ACO: a deadline-constrained workflow scheduling algorithm for cloud environment

Abstract A cloud computing environment is the most popular choice for workflow execution, as it gives customers on-demand access to computing resources. However, in cloud workflow scheduling, cloud-native requirements regarding QoS requirements such as monetary cost and execution time should be taken into account. This paper proposes PCP-ACO, a list scheduling algorithm for minimizing the execution cost of a workflow, while meeting its user-defined deadline in cloud environments. In PCP-ACO, first a topological sort of the workflow tasks is computed to assign a priority to each task. Then, Ant Colony Optimization (ACO) meta-heuristic is used to assign a proper resource to each task of the workflow, in order of their priorities. The Partial Critical Path (PCP) concept is also used as a heuristic to guide ACO algorithm. Several experiments are conducted using real scientific workflows, and the cost saving is compared with PSO and IC-PCP algorithms. The experimental results show that the proposed algorithm outperforms other compared algorithms in terms of cost saving.