A New Double Rank-based Multi-workflow Scheduling with Multi-objective Optimization in Cloud Environments

Abstract

Workflow scheduling in clouds has been extensively researched. Many workflows from different users could be submitted to clouds at the same time and cloud providers should handle them simultaneously. So, it is necessary to consider the problem of scheduling multi-workflow. In addition, cloud computing systems can offer some special features, like Pay-Per-Use and Quality of Service (QoS) over the Internet. The scheduler has to consider the tradeoffs between different QoS parameters in order to satisfy the QoS requirements. Hence, how to schedule multiple heterogeneous workflows in the meanwhile to balance multiple objectives is a big challenge. The majority of the existing multi-workflow scheduling algorithms are based on QoS constrained approaches and attempt to optimize one objective while taking other QoS factors as constraints. Meanwhile, most of the multi-objective optimization scheduling works aim to deal with single-workflow. Conversely, this paper focuses on QoS optimization approaches by finding trade-off schedules to execute multi-workflow on cloud computing resources so as to balance multi-objective. To this end, a new double rank-based task sequencing method is proposed and integrated with a multi-objective heuristic algorithm for multi-workflow scheduling. Different algorithms are evaluated using various well-known real-world workflows and simulated workflows. The performance evaluation results demonstrate that the proposed approach is capable of generating efficient schedules with high quality in terms of meeting multi-objective for multiple workflows.