Relevant window-based bitmap compression in P2P systems: Framework and solution

Abstract

P2P systems require neighbor peers to frequently exchange buffer-map (BM) messages for efficient content sharing and distribution, which, however, can result in considerable communication overhead. A big problem in the BMs exchanged between neighbor peers is that a lot of information in them is redundant. To reduce the redundancy, some P2P systems have adopted certain block-level compression schemes (e.g., Huffman encoding) to compress each BM in isolation. However, these schemes simply treat each BM separately and as a single block of data, which largely affects their compression efficiency. In this paper, we propose a novel relevant-window-based (RW) compression framework, which takes advantage of the correlation between sequentially exchanged BMs between neighbor peers and thus can greatly remove the redundancy in them. We accordingly design a RW-based distributed compression scheme, which can work alone or co-work well with an existing block-level compression scheme for higher compression efficiency. We prove the correctness of our scheme and derive tight upper bound on average length of compressed bitmaps by our scheme via mathematical modeling. Numerical results demonstrate that our scheme alone can achieve compression efficiency of 96.6%, which can be further increased to up to 97.1% when jointly working with a block-level compression scheme.