Efficient Task Scheduling for Applications on Clouds

Task scheduling is a central issue in the realm of parallel processing, and recently in cloud computing. The directed acyclic graph (DAG) is a well-known technique used to represent scheduling of computational tasks. Many researchers have studied task scheduling under various constraints and proposed alternative solutions. The purpose of this research is to edify task scheduling on the cloud using a recent bio-inspired optimization technique, the Grasshopper Optimization Algorithm (GOA). The proposed method is compared with the state-of-the-art techniques in this area, where a reduction of 10% in the makespan is obtained.