A Task Scheduling Algorithm With Improved Makespan Based on Prediction of Tasks Computation Time algorithm for Cloud Computing

Abstract

Cloud computing is extensively used in a variety of applications and domains, however task and resource scheduling remains an area that requires improvement. Put simply, in a heterogeneous computing system, task scheduling algorithms, which allow the transfer of incoming tasks to machines, are needed to satisfy high performance data mapping requirements. The appropriate mapping between resources and tasks reduces makespan and maximises resource utilisation. In this contribution, we present a novel scheduling algorithm using Directed Acyclic Graph (DAG) based on the Prediction of Tasks Computation Time algorithm (PTCT) to estimate the preeminent scheduling algorithm for prominent cloud data. In addition, the proposed algorithm provides a significant improvement with respect to the makespan and reduces the computation and complexity via employing Principle Components Analysis (PCA) and reducing the Expected Time to Compute (ETC) matrix. Simulation results confirm the superior performance of the algorithm for heterogeneous systems in terms of efficiency, speedup and schedule length ratio, when compared to the state-of-the-art Min-Min, Max-Min, QoS-Guide and MiM-MaM scheduling algorithms. INDEX TERMS Scheduling algorithm, task scheduling, resource utilization, cloud computing. I. INTRODUCTION A. MOTIVATION AND AIM Cloud computing has grown to be a major technological enabler in companies and organizations [1]-[3]. It has been shown to increase reliability, deliver cost-cutting solutions, and provide 24/7/365 access to hard/soft resources from anywhere based on pay/use pricing policy [4], [5]. The cloud offers services in the structure of Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS) [3]. Task scheduling is a major challenge in widely distributed heterogeneous systems (e.g., cloud computing), which chooses the preeminent resources for a provided task. Also, in heterogeneous systems, task scheduling is more convoluted in comparison to homogeneous The associate editor coordinating the review of this manuscript and approving it for publication was Yaser Jararweh.

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