Chaotic-nondominated-sorting Owl Search Algorithm for Energy-aware Multi-Workflow Scheduling in Hybrid Clouds

Since a single private cloud cannot satisfy the increasing computing requirements for executing multiple workflows simultaneously, hybrid clouds are preferred to do so. However, multi-workflow scheduling is challenging as users may request various workflows with different quality of service (QoS) requirements. This work proposes a Chaotic-non-dominated-sorting Owl Search Algorithm (COSA) by combining an Owl Search Algorithm (OSA) with a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to do so with makespan, cost and energy consumption minimized for each workflow 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 OSAs step size during population evolution for better exploration. Third, we adopt a chaotic operator for searching around the resulting non-dominated solutions to improve COSAs local search ability. Experiments are conducted to compare COSA with four state-of-the-art ones. The results demonstrate that it outperforms them in terms of diversity preservation, convergence towards a true Pareto front and the number of non-dominated solutions. In particular, it can find at least 19% more NDS than its peers.