A Grey Prediction Based Load Balancing Mechanism for Distributed Computing Systems

• Main Authors: Hsing-Lu Chen, 陳幸祿
• Other Authors: Liang-Teh Lee
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/11173887419537214778

碩士 === 大同大學 === 資訊工程學系(所) === 94 === For 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 disperse nodes to a cluster under one console and achieve load balancing, the setup and control of the agent is of great importance. Of course, the agent has to be provided with a Load Balancing Mechanism (LBM) and a GM (GM: Grey Dynamic Model). 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 task according to the load in the predicted group, to avoid the overloading or vacancy of some nodes, eliminate system bottleneck and increase system performance. The grey dynamic model-based Load Balancing Mechanism (GMLBM) proposed in this thesis, first predicts the utilization of each node then distributes the task to the node with the lowest load. 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 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.