Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud Workflows

Abstract

Cloud workflow scheduling often encounters two conflicting optimization objectives of makespan and monetary cost, and is a representative multi-objective optimization problem (MOP). Its challenges mainly come from three aspects: 1) a large number of tasks in a workflow cause large-scale decision variables; 2) the two optimization objectives are of quite different scales; 3) and cloud resources are heterogeneous and elastic. So far, many studies focus on adopting multi-objective evolutionary algorithms (MOEAs) to solve the cloud workflow scheduling problem without mining the domain knowledge. To make a good tradeoff between the makespan and monetary cost, this paper puts forward a knowledge-driven multi-objective evolutionary workflow scheduling algorithm, abbreviated as KMEWSA, including two novel features. On the one hand, the structural knowledge of workflow is mined to simplify the large-scale decision variables into a series of small-scale components, such accelerating the convergence speed of MOEAs. On the other hand, the knowledge on the Pareto front range is mined to estimate the ideal and nadir points for the objective space normalization during the search process, which helps maintain population diversity for MOEAs. At last, based on twenty real-world workflows and parameters of Amazon EC2, extensive experiments are performed to compare the KMEWSA with three baseline algorithms. The results demonstrate the effectiveness of the KMEWSA in balancing makespan and monetary cost for deploying workflows in cloud computing.