The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks. This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data. This architecture leverages and combines existing frequent itemset discovery over data streams, association rule deduction, frequent sequential pattern mining, and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.

Today's routers need to perform packet classification at wire speed in order to provide critical services such as traffic billing, priority routing and blocking unwanted Internet traffic. With everincreasing ruleset size and line speed, the task of implementing wire speed packet classification with reduced power consumption remains difficult. Software approaches are unable to classify packets at wire speed as line rates reach OC-768, while state of the art hardware approaches such as TCAM still consume large amounts of power. This paper presents a low power architecture for a high speed packet classifier which can meet OC-768 line rate. The architecture consists of an adaptive clocking unit which dynamically changes the clock speed of an energy efficient packet classifier to match fluctuations in traffic on a router line card. It achieves this with the help of a scheme developed to keep clock frequencies at the lowest speed capable of servicing the line card while reducing frequency switches. The low power architecture has been tested on OC-48, OC-192 and OC-768 packet traces created from real life network traces obtained from NLANR while classifying packets using synthetic rulesets containing up to 25,000 rules. Simulation results of our classifier implemented on a Cyclone 3 and Stratix 3 FPGA, and as an ASIC show that power savings of between 17--88% can be achieved, using our adaptive clocking unit rather than a fixed clock speed.