Abstract
Bin packing problem is one amongst the major problems which need attention in this era of distributed computing. In this optimization is attained by packing a set of items in as fewer bins as possible. Its application can vary from placing data on multiple disks to jobs scheduling, packing advertisements in fixed length radio/TV station breaks etc. The efforts have been put to parallelize the bin packing solution with the well-known programming model, MapReduce which is highly supportive for distributed computing over large cluster of computers. Here we have proposed two different algorithms using two different approaches, for parallelizing generalized bin packing problem. The results obtained were tested on the hadoop cluster organization and complexities were estimated thereafter. It is found that working on the problem set in parallel results in significant time efficient solutions for Bin Packing Problem.
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