Clustering Analysis for the Management of Self-Monitoring Device Networks

The increasing computing and communication capabilities of multi-function devices (MFDs) have enabled networks of such devices to provide value-added services. This has placed stringent QoS requirements on the operations of these device networks. This paper investigates how the computational capabilities of the devices in the network can be harnessed to achieve self-monitoring and QoS management. Specifically, the paper investigates the application of clustering analysis for detecting anomalies and trends in events generated during device operation, and presents a novel decentralized cluster and anomaly detection algorithm. The paper also describes how the algorithm can be implemented within a device overlay network, and demonstrates its performance and utility using simulated as well as real workloads.