A Customer-Oriented Task Scheduling for Heterogeneous Multi-Cloud Environment

A Customer-Oriented Task Scheduling for Heterogeneous Multi-Cloud Environment

Sohan Kumar Pande (Department of CSE, SUIIT, Burla, India), Sanjaya Kumar Panda (Department of CSE, Indian Institute of Technology (ISM), Dhanbad, India & Department of CSE and IT, VSS University of Technology, Burla, India) and Satyabrata Das (Department of CSE and IT, V S S University of Technology, Burla, India)
Copyright: © 2016 |Pages: 17
DOI: 10.4018/IJCAC.2016100101


Task scheduling is widely studied in various environments such as cluster, grid and cloud computing systems. Moreover, it is NP-Complete as the optimization criteria is to minimize the overall processing time of all the tasks (i.e., makespan). However, minimization of makespan does not equate to customer satisfaction. In this paper, the authors propose a customer-oriented task scheduling algorithm for heterogeneous multi-cloud environment. The basic idea of this algorithm is to assign a suitable task for each cloud which takes minimum execution time. Then it balances the makespan by inserting as much as tasks into the idle slots of each cloud. As a result, the customers will get better services in minimum time. They simulate the proposed algorithm in a virtualized environment and compare the simulation results with a well-known algorithm, called cloud min-min scheduling. The results show the superiority of the proposed algorithm in terms of customer satisfaction and surplus customer expectation. The authors validate the results using two statistical techniques, namely T-test and ANOVA.
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1. Introduction

Cloud computing provides various services such as infrastructure, platform and software as a service over the Internet (Buyya, Yeo, Venugopal et al., 2009; Durao, Carvalho, Fonseka, & Garcia, 2014). These services are requested by the customers as and when required. In general, the customer requests are represented in the form of applications/jobs/tasks (Tsai, Fang, & Chou, 2013; Li et al., 2012; Panda, & Jana, 2015; Panda, & Jana, 2016). On the contrary, the services are provisioned in the form of various resources such as network, storage, hardware, software and many more (Tsai, Fang, & Chou, 2013). In order to provide the services, the customer requests are mapped with the pool of resources (Li et al., 2012). Therefore, efficient mapping of customer requests to the resources (referred as task scheduling) is a challenging problem which was shown to be NP-Complete (Braun et al., 2001; Maheswaran, Ali, Siegelet al., 1999; Mokotoff, 1999).

Task scheduling is the ordering of n customers’ tasks to the m resources or clouds such that the overall processing time (i.e., makespan) is minimized (Mokotoff, 1999). Note that n >> m. Here, the requirements of the customers are varying with respect to the number of resource, cost, deadline etc. On the contrary, the resources are varying with respect to processing speed, capacity, bandwidth, service level etc. Therefore, the performance of the customer task is different from one resource to another resource. It introduces the problem of resource selection for each task in heterogeneous environment like cloud computing systems in which the primary objective is to minimize the makespan. However, minimization of makespan does not necessarily mean customers satisfaction as some tasks dominate the execution of other tasks. Therefore, task scheduling must emphasis on the customer satisfaction. More specifically, it must focuses on the minimization of individual makespan of the tasks rather than the overall makespan of all the tasks.

In this paper, we present the following task scheduling problem. Given a set of n independent customer tasks and a set of m clouds, the primary objective is to minimize the individual makespan of all the tasks so that the customer satisfaction is considerably increased. We propose an algorithm called customer-oriented task scheduling (COTS) for the above scheduling problem.

The paper is organized as follows. Section 2 discusses the related work in task scheduling algorithms. Section 3 presents the model and problem description. Section 4 proposes a customer-oriented task scheduling algorithm and analyzes the complexity of the algorithm. Section 5 introduces two performance metrics followed by simulation results in Section 6. We conclude with some future insights in Section 7.

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