P2P traffic identification and optimization using fuzzy c-means clustering

Abstract

Accurate identification of P2P traffic is critical for efficient network management and reasonable utilization of network resources, as P2P applications have been growing dramatically. Fuzzy clustering is more flexible than hard clustering and is practical for P2P traffic identification because of the natural treatment of data using fuzzy clustering. Fuzzy c-means clustering (FCM) is an iteratively optimal algorithm normally based on the least square method to partition data sets, which has high computational overhead. This paper proposes modifications to the objective function and the distance function that greatly reduces the computational complexity of FCM while keeping the clustering accurate. The proposed FCM clustering technology can be incorporated into a Fuzzy Inference System (FIS) to implement real-time network traffic classification by updating the training data set continuously and efficiently.