Shruti Jadon

Work place: Motilal Nehru National Institute of Technology, Allahabad, India

E-mail: rcs1301@mnnit.ac.in

Website:

Research Interests: Computer systems and computational processes, Real-Time Computing, Distributed Computing, Parallel Computing, Data Structures and Algorithms

Biography

Shruti Jadon received her B. Tech degree in Computer Science and Engineering from Uttar Pradesh Technical University, Lucknow (U.P.), India in 2011 and M.Tech in Computer Science from Banasthali University, Banasthali (Rajasthan) India in 2013. Presently she is pursuing PhD from Motilal Nehru National Institute of Technology Allahabad (U.P.), India since July, 2013. She has also worked with Dr. Amey Karkare, Computer Science and Engineering Department, IIT Kanpur (U.P.), India from July 2012 to June 2013. Her area of interest includes real time embedded systems and parallel and distributed systems.

Author Articles
Load Balancing in Multicore Systems using Heuristics Based Approach

By Shruti Jadon Rama Shankar Yadav

DOI: https://doi.org/10.5815/ijisa.2018.12.06, Pub. Date: 8 Dec. 2018

Multicore processing is advantageous over single core processors in the present highly advanced time critical applications. The tasks in real time applications need to be completed within the prescribed deadlines. Based on this philosophy, the proposed paper discusses the concept of load balancing algorithms in such a way that the work load is equally distributed amongst all cores in the processor. The equal distribution of work load amongst all the cores will result in enhanced utilization and increase in computing speed of application with all the deadlines met. In the heuristic based load balanced algorithm (HBLB), the best task from the set of tasks is selected using the feasibility check window and is assigned to the core. The application of HBLB reduces imbalance among the cores and results in lesser migration leading to low migration overhead. By utilizing all the cores of the multicore system, the computing speed of the application increases tremendously which results in the increase in efficiency of the system. The present paper also discusses the improved version of HBLB, known as Improved_Heuristic Based Load Balancing (Improved_HBLB), which focuses on further reducing the imbalance and the number of backtracks as compared to HBLB algorithm. It was observed that Improved_HBLB gives approximately 10% better results over the HBLB algorithm.

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