Resource Scheduling for Big Data on Cloud: Scheduling Resources

Resource Scheduling for Big Data on Cloud: Scheduling Resources

K. Indira Suthakar, M. K. Kavitha Devi
Copyright: © 2016 |Pages: 21
DOI: 10.4018/978-1-4666-9767-6.ch013
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Abstract

Cloud computing is based on the concepts of distributed computing, grid computing, utility computing and virtualization. It is a virtual pool of resources which are provided to users via Internet. It gives users virtually unlimited pay-per-use computing resources without the burden of managing the underlying infrastructure. Cloud computing service providers' one of the goals is to use the resources efficiently and gain maximum profit. This leads to task scheduling as a core and challenging issue in cloud computing. This paper gives different scheduling strategies and algorithms in cloud computing.
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Introduction

Cloud computing dates back to the 1960’s when John McCarthy opined that “computation may someday be organized as a public utility”. Amazon played a key role in cloud computing development by launching Amazon web service on utility basis in 2006. Before scheduling tasks on cloud computing, the characteristics of the cloud should be taken into account. Some of the characteristics of cloud include:

  • 1.

    On-demand self service

  • 2.

    Ubiquitous network access

  • 3.

    Location independent resource pooling

  • 4.

    Rapid elasticity

  • 5.

    Pay per use

Millions of user share cloud resources by submitting their computing task to the cloud system. Scheduling these millions of task is a challenge to cloud computing environment. Different scheduling strategies are proposed in the cloud resource scheduling environment. These strategies considers different factors like cost matrix generated by using credit of tasks to be assigned to a particular resource, quality of Service (QoS) based meta-scheduler and Backfill strategy based light weight virtual machine scheduler for dispatching jobs, QoS requirement heterogeneity of the cloud environment and workloads. Optimal resource allocation or task scheduling in the cloud should decide optimal number of systems required in the cloud so that the total cost is minimized and the SLA is upheld. Cloud computing is highly dynamic, and hence, resource allocation problems have to be continuously addressed, as servers become available/non-available while at the same time the customer demand fluctuates. Thus this study focuses on scheduling algorithms in cloud environment considering above mentioned characteristics, challenges and strategies.

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Task Scheduling Types

Cloud Service Scheduling

Cloud service scheduling is categorized at user level and system level. At user level scheduling deals with problems raised by service provision between providers and customers. The system level scheduling handles resource management within datacenter. Datacenter consists of many physical machines. Millions of tasks from users are received; assignment of these tasks to physical machine is done at datacenter. This assignment or scheduling significantly impacts the performance of datacenter. In addition to system utilization, other requirements like QoS, SLA, resource sharing, fault tolerance, reliability, real time satisfaction, etc. should be taken into consideration.

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