A PSO Algorithm Based Task Scheduling in Cloud Computing

A PSO Algorithm Based Task Scheduling in Cloud Computing

Mohit Agarwal, Gur Mauj Saran Srivastava
Copyright: © 2019 |Pages: 17
DOI: 10.4018/IJAMC.2019100101
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Cloud computing is an emerging technology which involves the allocation and de-allocation of the computing resources using the internet. Task scheduling (TS) is one of the fundamental issues in cloud computing and effort has been made to solve this problem. An efficient task scheduling mechanism is always needed for the allocation to the available processing machines in such a manner that no machine is over or under-utilized. Scheduling tasks belongs to the category of NP-hard problem. Through this article, the authors are proposing a particle swarm optimization (PSO) based task scheduling mechanism for the efficient scheduling of tasks among the virtual machines (VMs). The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism. The simulation results clearly show that the PSO-based task scheduling mechanism clearly outperforms the others as it results in almost 30% reduction in makespan and increases the resource utilization by 20%.
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1. Introduction

With the advancement in the field of information technology, the need of computing model which helps the users to carry out their day to day computation of task also arises with the passage of time. Cloud computing model presents itself as the solution to such demand and since its inception; this computing paradigm gains a lot of popularity both in academia as well as in industry (Sadiku, Musa & Momoh, 2014). The term Cloud may be defined as the pool of the computing resources like memory units, processing elements, networking components, etc., which are allocated and released in order to execute the tasks submitted by the users (Alhamazani, Ranjan, Mitra, Rabhi, Jayaraman, Khan & Bhatnagar, 2014). The prominent characteristics like ubiquitous, economical, scalable, on-demand access and elastic in nature are responsible for the migration of the business from the traditional model to the cloud-based model (Duan, Yan & Vasilakos, 2012; Agarwal & Srivastava, 2017). Cloud computing is said to be an extension of the existing technologies like distributed computing, grid computing and utility computing (Sadiku, Musa & Momoh, 2014) with the distinguishing features like virtualization, virtual machine migration and much more. Cloud computing is successful in providing the new business opportunity not only to the service providers but also to the users of such services by the means of the platform for delivering Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). Apart from offering the computing services using concept of allocating the computing resources, cloud computing model also offers application services like e-commerce and social platforms which works for the entire network. It allows the user to focus on their core business activity instead of investing their time and wealth in setting the IT infrastructure required to perform their business activity. Cloud computing model enables the service users to use the underlying IT infrastructure purely on the basis of plug and pay, i.e. users only need to pay for the duration of time for which they consume the services not for the entire period of time. It is right to say that cloud computing based model is quite inseparable from network support, the reason in limitlessness of the network (Xue, Shi & Xu, 2016). The users also don’t to need to bother about the location and number of the computing resources required for the processing of their tasks or jobs what they are submitting. Virtualization is the key concept behind the success of cloud computing model as it is used to create virtual machines on the limited hardware. Scheduling used to play a very significant role in the process of optimization as they involve the distribution of the load or tasks on the VMs in order to maximize their utilization and minimizing the overall execution time (Zomaya & Yee-Hwei, 2001).

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