A Grey Prediction Based Load Balancing Mechanism for Distributed Computing Systems

Abstract

Due to the rapid growth of the hardware technology, personal computers and workstations are more powerful than before. Instead of using the expensive supercomputer, many personal computers can be connected by a high speed network to form a distributed computing system, so as to decrease the cost of building a high performance computing system.To link all of the dispersed nodes to a cluster, it is very important to setup an agent for achieving the load balancing of the cluster. A Grey Dynamic model-based Load Balancing Mechanism (GMLBM) has been proposed in this paper. It will produce grey prediction for the load data according to the grey theory By applying a few data to get the load model for assigning new tasks according to the load in the predicted group for avoiding the overloading or vacancy of some nodes, so as to eliminate the system bottleneck and improve the system performance. The GMLBM is installed at the agent. The agent detects records and predicts the load of each node in a local group, and selects the node with the lowest load predicted as the node for executing the next task. A simulation has been made to evaluate the performance of the proposed system. By comparing with other load balancing methods, the experimental results show that the method of GMLBM can achieve a better performance than that of round robin and linear extrapolation.

An Intelligent Transaction Assistant System for Electronic Commerce to Recommend Personalized Transaction Behavior

“…By using the known white information, we can construct a model to transform grey condition into white [8,12]. The grey system generates inner rules through the processing of disordered data to find the implicit characteristics.…”

“…The grey system generates inner rules through the processing of disordered data to find the implicit characteristics. The grey system construction contains four main steps, that are, grey generating, grey relational analysis, grey modeling, and grey prediction [12,18]. Grey generating usually uses accumulating, inverse or partial generating operations to reduce data randomization and enhance data regularity.…”

“…A recommendation system uses information filtering that applies data analysis techniques to help customers to find the product they would like to purchase [16]. They employ past transaction history and preferences to predict potential products by using data mining and collaborative filtering [1,4,9,10,11,12,13,14,15,19]. For example, D. R. Liu uses collaborative filtering, preference, and statistical method to conduct product recommendation [13,14] C. Miao uses a fuzzy agent to provide a personalized recommendation [15].…”