Big Data and parallel computing are used extensively for processing large quantities of data, structured, semi structured or totally unstructured. MapReduce and Hadoop are used for the parallel data processing of these kinds of data. Various scheduling policies are used for MapReduce scheduling which is discussed in detail and a new scheduling technique Two Phase Scheduling Policy (TPSP) based resource allocation for MapReduce is implemented and the efficiency is verified. © 2015 The Authors.

Dhinesh Babu L-D

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Raj E.D

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Procedia Computer Science

In cloud computing, clients comply a policy of pay-as-you go, i.e., they only pay for the resources they use. So, the processing power of the clouds has to be optimised to reduce the cost at client's side. Using the resources optimally ensures enterprise sustainability of cloud service providers. Workflow is a set of tasks that are interdependent on each other. Scheduling these workflows is one of the most important challenges to optimally utilise the cloud resources and ensure better quality of service (QoS) to clients. Existing works on scheduling in cloud computing mainly focus on scheduling independent tasks rather than (inter)dependent tasks. In this paper, we propose a strategy to schedule dependent tasks called pre-emptive fair scheduling algorithm (PFSA). This is fair scheduling strategy also aims to ensure higher utilisation of virtual machines (VMs) by reducing the idle time and to minimize the number of times a pre-empted task is submitted to the virtual machine. In both cases, this algorithm tries to effectively reduce the overall processing time of dependent tasks at virtual machine, thus minimising the cost involved in processing of tasks. This economically viable decision-based strategy will be helpful for cloud service providers in ensuring sustainability. Copyright © 2014 Inderscience Enterprises Ltd.

International Journal of Business Information Systems

A decision-based pre-emptive fair scheduling strategy to process cloud computing work-flows for sustainable enterprise management

In cloud computing environments, resources and infrastructure are provided as a service over internet on demand. The users are interested in reducing the service cost provided by the cloud service providers. Scheduling tasks of workflows play a vital role in determining performance of cloud computing systems. Workflows have many tasks in it and are interdependent on each other. Time critical workflows comprise of a collection of tasks which should be completed as early as possible so that other workflows get its turn. The budget involved in executing the time critical tasks is very high. The execution cost increases whenever we try to reduce the execution time. In this paper, we propose a method called versatile time-cost algorithm (VTCA) to schedule time critical workflows with minimum cost. VTCA will schedule the tasks to complete in earliest possible time as well as optimise the cost involved in resource provisioning. The results of experiments conducted using CloudSim simulator show that our scheduling policy minimises the completion time of workflows than other existing algorithms like min-min and fair max-min by 5% to 30% and it also reduces the costs by 5% to 35%. © 2013 Inderscience Enterprises Ltd.

International Journal of Communication Networks and Distributed Systems

Versatile time-cost algorithm (VTCA) for scheduling non-preemptive tasks of time critical workflows in cloud computing systems

Scheduling of tasks in cloud computing is an NP-hard optimization problem. Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing (HBB-LB), which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue. © 2013 Elsevier B.V.

Journal	Data powered by TypesetProcedia Computer Science
Publisher	Data powered by TypesetElsevier BV
ISSN	1877-0509
Open Access	No