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Dynamic Load Balancing Using Periodically Load Collection with Past Experience Policy on Linux Cluster System
American Journal of Mathematical and Computer Modelling
Volume 2, Issue 2, May 2017, Pages: 60-75
Received: Oct. 21, 2016; Accepted: Jan. 9, 2017; Published: Mar. 9, 2017
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Sharada Santosh Patil, MCA, Dept. Sinhgad Institute of Business Administration and Research, Kondhwa, Pune, Maharashtra, India
Arpita Nirbhay Gopal, MCA, Dept. Sinhgad Institute of Business Administration and Research, Kondhwa, Pune, Maharashtra, India
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Fast execution of the applications achieved through parallel execution of the processes. This is very easily achieved by high performance cluster (HPC) through concurrent processing with the help of its compute nodes. The HPC cluster provides super computing power using execution of dynamic load balancing algorithm on compute nodes of the clusters. The main objective of dynamic load balancing algorithm is to distribute even workload among the compute nodes for increasing overall efficiency of the clustered system. The logic of dynamic load balancing algorithm needs parallel programming. The parallel programming on the HPC cluster can achieve through massage passing interface in C programming. The MPI library plays very important role to build new load balancing algorithm. The workload on a HPC cluster system can be highly variable, increasing the difficulty of balancing the load across its compute nodes. This paper proposes new idea of existing dynamic load balancing algorithm, by mixing centralized and decentralized approach which is implemented on Rock cluster and maximum time it gives the better performance. This paper also gives comparison between previous dynamic load balancing algorithm and new dynamic load balancing algorithm.
MPI, Parallel Programming, HPC Clusters, DLBA ARPLCLB, ARPLCPELB
To cite this article
Sharada Santosh Patil, Arpita Nirbhay Gopal, Dynamic Load Balancing Using Periodically Load Collection with Past Experience Policy on Linux Cluster System, American Journal of Mathematical and Computer Modelling. Vol. 2, No. 2, 2017, pp. 60-75. doi: 10.11648/j.ajmcm.20170202.13
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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