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Jiming Liu, Xiaolong Jin, Yuanshi Wang, "AgentBased Load Balancing on Homogeneous Minigrids: Macroscopic Modeling and Characterization," IEEE Transactions on Parallel and Distributed Systems, vol. 16, no. 7, pp. 586598, July, 2005.  
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@article{ 10.1109/TPDS.2005.76, author = {Jiming Liu and Xiaolong Jin and Yuanshi Wang}, title = {AgentBased Load Balancing on Homogeneous Minigrids: Macroscopic Modeling and Characterization}, journal ={IEEE Transactions on Parallel and Distributed Systems}, volume = {16}, number = {7}, issn = {10459219}, year = {2005}, pages = {586598}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2005.76}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Parallel and Distributed Systems TI  AgentBased Load Balancing on Homogeneous Minigrids: Macroscopic Modeling and Characterization IS  7 SN  10459219 SP586 EP598 EPD  586598 A1  Jiming Liu, A1  Xiaolong Jin, A1  Yuanshi Wang, PY  2005 KW  Homogeneous minigrids KW  load balancing KW  task distribution KW  agents KW  macroscopic modeling KW  steady states KW  convergence KW  grid simulation. VL  16 JA  IEEE Transactions on Parallel and Distributed Systems ER   
Abstract—In this paper, we present a macroscopic characterization of agentbased load balancing in homogeneous minigrid environments. The agentbased load balancing is regarded as agent distribution from a macroscopic point of view. We study two quantities on minigrids: the number and size of teams where agents (tasks) queue. In macroscopic modeling, the load balancing mechanism is characterized using differential equations. We show that the load balancing we concern always converges to a steady state. Furthermore, we show that load balancing with different initial distributions converges to the same steady state gradually. Also, we prove that the steady state becomes an even distribution if and only if agents have complete knowledge about agent teams on minigrids. Utility gains and efficiency are introduced to measure the quality of load balancing. Through numerical simulations, we discuss the utility gains and efficiency of load balancing in different cases and give a series of analysis. In order to maximize the utility gain and the efficiency, we theoretically study the optimization of agents' strategies. Finally, in order to validate our proposed agentbased load balancing mechanism, we develop a computing platform, called
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