Co-evolutionary and Elite learning-based bi-objective Poor and Rich Optimization algorithm for scheduling multiple workflows in the cloud

Abstract

Cloud computing is a cost-effective environment for deploying large-scale scientific applications. However, multi-workflow scheduling has great challenge since users may request a series of applications with different Quality of Service (QoS) at the same time. In this paper, a Co-evolutionary and Elite learning-based bi-objective Poor and Rich Optimization algorithm (CE-PRO) is proposed for scheduling applications to minimize the makespan and cost of each workflow. First, an MPMO framework is combined with PRO to optimize two objectives by two populations, respectively for better balancing the search diversity and convergence speed, where each population is updated by an improved PRO, which adopts the middle-class sub-population and re-defines the update mechanism for rich individuals to enhance search diversity and reduce the possibility of falling into local optima. Second, to restrain each population focusing overly on its respective objective, a global information exchange pool is innovatively designed to save the non-dominated solutions ever found, which will be used back as the shared guiding solutions to foster inter-population communication and co-evolution during an evolutionary process. Third, a hybrid mutation-based Elite Enhancement Strategy (EES) is developed by introducing multiple scales of mutation operations into elite solutions alternatively and iteratively to exploit excellent individuals and explore more trade-off solutions. Extensive experiments are conducted on real world scientific workflows with different types and scales, and the experimental results demonstrate that in most cases, our proposed CE-PRO outperforms its peers in the number of obtained non-dominated solutions, and the solution diversity and quality as well. In particular, the dominance of CE-PRO is superior to its peers by at least 25.62%.