On minimizing memory and computation overheads for binary-tree based data replication

Abstract

Data replication is used to track the most popular files (i.e., the ones with most requests) and replicate them in selected nodes. In this way, more requests for such popular files can be completed over a period of time and bandwidth consumption is reduced, since these files do not need to be transferred from remote nodes. In this article, we extend our previous work [1] to make it more efficient in terms of memory and total computation cost, so that it becomes more efficient and suitable for larger grids. To reduce the memory costs, we present a centralized strategy which estimates the potential for selected batches of files. The computations required for these estimations are executed in a pipelined way, so their cost is also reduced.