Abstract :
Notice of Violation of IEEE Publication Principles
"Adaptive Load Balancing Algorithm over Heterogenous Workstations"
by Liang Guangmin
in the Proceedings of the 2008 Seventh International Conference on Grid and Cooperative Computing, October 2008, pp. 169-174
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Adaptive IOCM Load Balancing Algorithm in Network of Heterogenous Workstations"
by Masani Paresh P., S. Mary Saira Bhanu, N.P. Gopalan
in the Proceedings of the 3rd International Conference on Sematics, Knowledge, and Grid October 2007, pp. 654-675
Distributed computing environment consists of a collection of autonomous workstations connected by the LAN. Due to random arrival of tasks and their random CPU service time requirements, there is good possibility that several workstations are heavily loaded, while others are idle or lightly loaded. Many load sharing/balancing algorithms mainly target sharing CPU resources, and have been intensively evaluated in a homogeneous environment. The main purpose of our algorithm is to improve the overall response time of the distributed system with minimum network traffic over heterogeneous workstations. This algorithm tries to keep the load difference between any two nodes within specified thresholds, and shares CPU, Memory and IO (thus named IOCM_LB) resources with the best effort. Theoretical analysis and exper- imental evaluation demonstrate that compared with existing approaches, proposed algorithm can reduce the mean slow down and network traffic.
