Chaotic-Nondominated-Sorting Owl Search Algorithm for Energy-Aware Multi-Workflow Scheduling in Hybrid Clouds

Abstract

Since a single private cloud cannot satisfy the increasing computational requirements for executing multiple workflows simultaneously, hybrid clouds are often adopted to perform such execution. Multi-workflow scheduling is challenging as users may request various applications with different QoS requirements. This work proposes a Chaotic-nondominated-sorting Owl Search Algorithm (COSA) by combining an Owl Search Algorithm (OSA) with a Nondominated Sorting Genetic Algorithm II (NSGA-II) to schedule resource-constrained multiple workflows in hybrid clouds with makespan, cost and energy consumption minimized under the given deadline and budget constraints. First, a hierarchical evolving mechanism is designed to update the better half and worse half of population by NSGA-II and OSA, respectively to guarantee a good trade-off between exploration and exploitation. Second, a chaotic sequence is introduced to adaptively adjust OSA's step size during population evolution for better exploration. Third, we adopt a chaotic operator for searching around the resulting Non-Dominated Solutions (NDS) to improve COSA's local search ability. Experiments are conducted to compare COSA with four peers and the results show its superiority in the number of obtained NDS, diversity preservation and convergence towards the near optimal Pareto set. In particular, it can find at least 19% more NDS than its peers.