Multi-dimensional data density estimation in P2P networks

Estimating the global data distribution in Peer-to-Peer (P2P) networks is an important issue and has not yet been well addressed. It can benefit many P2P applications, such as load balancing analysis, query processing, data mining, and so on. In this paper, we propose a novel algorithm which is based on compact multi-dimensional histogram information to achieve high estimation accuracy with low estimation cost. Maintaining data distribution in a multi-dimensional histogram which is spread among peers without overlapping and each part of which is further condensed by a set of discrete cosine transform coefficients, each peer is capable to hierarchically accumulate the compact information to the entire histogram by information exchange and consequently estimates the global data density with accuracy and efficiency. Algorithms on discrete cosine transform coefficients hierarchically accumulating as well as density estimation error are introduced with detailed theoretical analysis and proof. Our extensive performance study confirms the effectiveness and efficiency of our methods on density estimation in dynamic P2P networks.