An intelligent water drops-based approach for workflow scheduling with balanced resource utilisation in cloud computing

Abstract

The problem of finding optimal solutions for scheduling scientific workflows in cloud environment has been thoroughly investigated using various nature-inspired algorithms. These solutions minimise the execution time of workflows, however may result in severe load imbalance among Virtual Machines (VMs) in cloud data centres. Cloud vendors desire the proper utilisation of all the VMs in the data centres to have efficient performance of overall system. Thus, load balancing of VMs becomes an important aspect while scheduling tasks in cloud environment. In this paper, we propose an approach based on Intelligent Water Drops (IWD) algorithm to minimise the execution time of workflows while balancing the resource utilisation of VMs in cloud computing environment. The proposed approach is compared with a variety of well-known heuristic and meta-heuristic techniques using three real-time scientific workflows, and experimental results show that the proposed algorithm performs better than these existing techniques in terms of makespan and load balancing.