ISSN: 2393-8528 Contents lists available at www.ijicse.in International Journal of Innovative Computer Science & Engineering Volume 2 Issue 3; July-August-2015; Page No. 44-51 Page44 Implementation of Randomized Hydrodynamic Load Balancing Algorithm using Map Reduce framework on Open Source Platform of Hadoop Rashi Saxena 1 , Sarvesh Singh 2 1 (Research Scholar, Jayoti Vidyapeeth Women’s University, India) 2 (HOD, Computer Science & Engineering Department, Jayoti Vidyapeeth Women’s University, India) ARTICLE INFO ABSTRACT Received: 23 June 2015 Accepted 18 July 2015 Corresponding Author: Rashi Saxena Department of Computer Science and Engineering, Jayoti Vidyapeeth Women’s University, Jaipur Load balancing is performed to achieve the optimal use of the existing computational resources as much as possible whereby none of the resources remains idle while some other resources are being utilized. Balanced load distribution can be achieved by the immigration of the load from the source nodes which have surplus workload to the comparatively lightly loaded destination nodes. Applying load balancing during run time is called dynamic load balancing (DLB). This paper presents the randomized hydrodynamic load balancing (RHLB) method which is a hybrid method that takes advantage of both direct and iterative methods. Using random load migration as a direct method, RHLB approach intends to solve the problems derived from the exceptional instantaneous load rises, and diffuse the surplus workload to relatively free resources. Besides, using hydrodynamic approach as an iterative method, RHLB aims to consume minimum possible system resources to balance the common workload distributions. The results of the experiments designate that, RHLB outruns other iterative based methods in terms of both balance quality and the total time of the load balancing process. © IJICSE, All Right Reserved. Key words: Dynamic Load Balancing, Randomized Hydrodynamic Load Balancing. INTRODUCTION High performance computing (HPC) systems are very effective at solving problems that can be partitioned into small tasks. Distributing tasks among computing nodes (CNs) is the key issue to increase the throughput and the utilization of an HPC system. Any strategy, used for even load distribution among CNs, is called load balancing (LB). The main purpose of load balancing is to keep all computational resources in use as much as possible, and not to leave any resource in idle state while some other resources are being utilized. Conceptually, a load balancing algorithm implements a mapping function between the tasks and CNs. Load balancing is especially more crucial in case of the no uniform computational patterns and/or in heterogeneous environments. In dynamic load balancing (DLB), the load is balanced during the computation process. The primary mechanism of the DLB is the notion of migration of tasks among CNs. Given the instantaneous load distribution of the whole system, queued tasks, waiting to be executed, are migrated from a heavy loaded CN to a relatively free CN. Thus, even in case of non-uniform computational patterns or instantaneous computational power needs, balanced workload distribution among the whole system can be achieved and the utilization of the CNs can be increased. However, any DLB method has to deal with some issues like forecasting the right time to invoke the balancing process, selection of the node that makes the load balancing decisions and how to migrate the load among the CNs. Dynamic load balancing techniques can be classified according to migration operation as direct and iterative methods. Direct methods aim to find out the last execution node of the surplus load in one load balancing step. Hence, direct methods need actual information about the load distribution of the whole system. These kinds of methods are most suitable for systems equipped with a broadcast mechanism [2]. Load balancing process is performed by migration of the partition objects that contain the surplus workload. Load transfer is controlled by two dynamically selected threshold values. The process is invoked by the sender (highly loaded) node and the invocation mechanism is