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Resource-aware load balancing system with artificial neural networks

Yıldız, Ali
As the distributed systems becomes popular, efficient load balancing systems taking better decisions must be designed. The most important reasons that necessitate load balancing in a distributed system are the heterogeneous hosts having different com- puting powers, external loads and the tasks running on different hosts but communi- cating with each other. In this thesis, a load balancing approach, called RALBANN, developed using graph partitioning and artificial neural networks (ANNs) is de- scribed. The aim of RALBANN is to integrate the successful load balancing deci- sions of graph partitioning algorithms with the efficient decision making mechanism of ANNs. The results showed that using ANNs to make efficient load balancing can be very beneficial. If trained enough, ANNs may load the balance as good as graph partitioning algorithms more efficiently.