Load Balancing Analyzer: A Recommendation System using Machine Learning

The Parallel and distributed systems focus on the concept of load balancing. A tangle is split into a hard and fast number of processors that are to be executed in parallel. However, there may be a state that some processors will complete their tasks before other processes and reach idle state as the work is unevenly divided or perhaps some processors complete before the others. Ideally, we like all the processors to have the minimum wait time. Achieving the above goal by spreading the tasks is eventually named as load balancing. In the paper, we stress on clustering techniques. The basis of the join-idle-queue algorithm is seen by using clustering. The technique of load balancing uses Support Vector Machine (SVM) and clustering techniques (K-means, Hierarchical). A comparative study of the above techniques is done utilizing load balancing.