Publish/Subscribe Service in CAN-based P2P Networks: Dimension Mismatch and The Random Projection Approach

Abstract

CAN is a well-known DHT technique for content- based P2P networks, where each node is assigned a zone in a virtual coordinate space to store the index of the data hashed into this zone. The dimension of this space is usually lower than the data dimension, thus we have the problem of dimension mismatch. This problem is widely addressed in the context of data retrieval that follows the traditional request/response model. However, little has been done for the publish/subscribe model, which is the focus of our paper. We show that dimension mismatch in CAN-based publish/subscribe applications poses new challenges. We furthermore investigate how a random projection approach can help reduce the negative effects of dimension mismatch. Our theoretical findings are complemented by a simulation-based evaluation.