Manisha Verma

Work place: Department of Computer Science and Technology, UPTU University, Lucknow, U.P., India



Research Interests: Autonomic Computing, Information Security, Network Security, Distributed Computing


Manisha Verma has done B. Tech in Computer Science & Engineering from F.E.T. Engineering College, Agra. She is now pursuing M.Tech in Computer Science & Engineering from Hindustan Institute Technology & Management, Agra.

Her research interest includes Cryptography and Network Security, Distributed System, Object Oriented System, Cloud Computing and Fog Computing.

Author Articles
Real Time Efficient Scheduling Algorithm for Load Balancing in Fog Computing Environment

By Manisha Verma Neelam Bhardwaj Arun Kumar Yadav

DOI:, Pub. Date: 8 Apr. 2016

Cloud computing is the new era technology, which is entirely dependent on the internet to maintain large applications, where data is shared over one platform to provide better services to clients belonging to a different organization. It ensures maximum utilization of computational resources by making availability of data, software and infrastructure with lower cost in a secure, reliable and flexible manner. Though cloud computing offers many advantages, but it suffers from certain limitation too, that during load balancing of data in cloud data centers the internet faces problems of network congestion, less bandwidth utilization, fault tolerance and security etc. To get rid out of this issue new computing model called Fog Computing is introduced which easily transfer sensitive data without delaying to distributed devices. Fog is similar to the cloud only difference lies in the fact that it is located more close to end users to process and give response to the client in less time. Secondly, it is beneficial to the real time streaming applications, sensor networks, Internet of things which need high speed and reliable internet connectivity. Our proposed architecture introduced a new scheduling policy for load balancing in Fog Computing environment, which complete real tasks within deadline, increase throughput and network utilization, maintaining data consistency with less complexity to meet the present day demand of end users.

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