A Task Allocation Schema Based on Response Time Optimization in Cloud Computing

TL;DR

This paper introduces a new task scheduling model for cloud computing that focuses on minimizing response time through parallel task execution and an improved entropy-based solution, outperforming existing algorithms.

Contribution

It proposes a novel response time-oriented task allocation model with an improved entropy-based solution and a new scheduling algorithm for cloud computing.

Findings

Proposed algorithm significantly reduces response time.

Outperforms game-theoretic and balanced scheduling algorithms.

Parallel execution improves overall system performance.

Abstract

Cloud computing is a newly emerging distributed computing which is evolved from Grid computing. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum or the overall system performance is optimal. Unlike the previous task slices' sequential execution of an independent task in the model of which the target is processing time, we build a model that targets at the response time, in which the task slices are executed in parallel. Then we give its solution with a method based on an improved adjusting entropy function. At last, we design a new task scheduling algorithm. Experimental results show that the response time of our proposed algorithm is much lower than the game-theoretic algorithm and balanced scheduling algorithm and compared with the balanced scheduling algorithm, game-theoretic algorithm is not necessarily superior in parallel although its objective function value is better.