International Journal of Trend in Scientific Research and Development, Volume 1(4), ISSN: 2456-6470 www.ijtsrd.com 594 IJTSRD | May-Jun 2017 Available Online @www.ijtsrd.com Various Approaches for Dynamic Load Balancing for Multiprocessor Interconnection Network Mukul Varshney Assistant professor, Computer Science and Engineering, Sharda University Dr. Anand Sharma HOD, Aligarh College of Engineering and Technology, Aligarh Abhakiran Rajpoot Assistant professor , Computer Science and Engineering, Sharda University ABSTRACT Multiprocessor interconnection network have become powerful parallel computing system for real-time applications. Now a days the many researchers doing research on the dynamic load scheduling in multiprocessor system. Load balancing is the method of dividing the total load among the processors of the distributed system to progress task's response time as well as resource utilization whereas ignoring a condition where few processors are overloaded or under loaded or moderately loaded. However, in dynamic load balancing algorithm presumes no priori information about behavior of tasks or the global state of the system. There are numerous issues while designing an efficient dynamic load balancing algorithm that involves utilization of system, amount of information transferred among processors, selection of tasks for migration, load evaluation, comparison of load levels and many more. This paper enlightens the performance analysis on dynamic load balancing strategy (DLBS) algorithm, used for hypercube network in multiprocessor system. Dynamic load scheduling (DLB) algorithm are required to efficiently solve this problems on multiprocessor systems. In this paper our focus on study and evaluation of various dynamic load balancing strategies such as SID, RID,DEM ,GM HBM etc. KEYWORD: Interconnection network, Parallel processing, Multiprocessor System, Load Balancing, Scheduling Algorithm, knowledge over head, threshold I. Introduction In computing, load balancing improves the distribution of workloads across multiple computing resources, such as computers, a computer cluster, network links, central processing units, or disk drives[1]. Load balancing aims to optimize resource use, maximize throughput, minimize response time, and avoid overload of any single resource. Using multiple components with load balancing instead of a single component may increase reliability and availability through redundancy. Load balancing usually involves dedicated software or hardware, such as a multilayer switch or a Domain Name System server process. When adaptive algorithms are used, after an interval of computation, the mesh may be refined (or coarsened) at some locations, usually based on an estimate of the discretization error. The refinement (or coarsening) process can generate widely varying numbers of mesh nodes on the processors. Subsequently, there is a need for dynamic load balancing. Load imbalance may also be caused by the use of local time stepping, local spatial approximation schemes of varying orders [2], or non-linear material properties. Load balancing differs from channel bonding in that load balancing divides traffic between network interfaces on a network socket (OSI model layer 4) basis, while channel bonding implies a division of traffic between physical interfaces at a lower level, either per packet (OSI model Layer 3) or on a data link (OSI model Layer 2) basis with a protocol like shortest path bridging. The tradeoff between knowledge and overhead is illustrated, by example, with five different DLS schemes. The schemes presented vary in the amount of processing and communication overhead and in the degree of knowledge used in making balancing decisions. The load balancing overhead includes the