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Mathematical Problems in Engineering
Volume 2016, Article ID 5641831, 9 pages
Research Article

A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems

1School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China
2School of Materials Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China
3College of Business Administration, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China

Received 22 November 2015; Accepted 6 April 2016

Academic Editor: Veljko Milutinovic

Copyright © 2016 Jixiang Yang et al. This 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.


Load balancing technology can effectively exploit potential enormous compute power available on distributed systems and achieve scalability. Communication delay overhead on distributed system, which is time-varying and is usually ignored or assumed to be deterministic for traditional load balancing strategies, can greatly degrade the load balancing performance. Considering communication delay overhead and its time-varying feature, a hierarchical load balancing strategy based on generalized neural network (HLBSGNN) is presented for large distributed systems. The novelty of the HLBSGNN is threefold: () the hierarchy with optimized communication is employed to reduce load balancing overhead for large distributed computing systems, () node computation rate and communication delay randomness imposed by the communication medium are considered, and () communication and migration overheads are optimized via forecasting delay. Comparisons with traditional strategies, such as centralized, distributed, and random delay strategies, indicate that the HLBSGNN is more effective and efficient.