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Abstract-Error-correcting convolutional codes provide a proven mechanism to limit the effects of noise in digital data transmission. Although hardware implementations of decoding algorithms, such as the Viterbi algorithm, have shown good noise tolerance for error-correcting codes, these implementations require an exponential increase in VLSI area and power consumption to achieve increased decoding accuracy. To achieve reduced decoder power consumption, we have examined and implemented decoders based on the reduced-complexity adaptive Viterbi algorithm (AVA). Run-time dynamic reconfiguration is performed in response to varying communication channel noise conditions to match minimized power consumption to required error-correction capabilities. Experimental calculations indicate that the use of dynamic reconfiguration leads to a 69% reduction in decoder power consumption over a non-reconfigurable fieldprogrammable gate array (FPGA) implementation with no loss of decode accuracy.

We present an architecture for a Distributed Online Measurement Environment (DOME) which is a passive measurement system that correlates network information between several measurement nodes placed at different locations in the network to offer a large scale view of network operation. The system is capable of capturing packet traces and pre-processing them on the measurement node itself. Real-time queries are implemented by breaking them down into standard statistics that are updated during run-time. We present details of a prototype implementation of our architecture on an Intel IXP2400 network processor. The prototype is deployed on the main Internet access link of the University of Massachusetts and measurement results are validated against those obtained from an Endace DAG card. Performance of the prototype is compared to that of a conventional post processing system for an application to detect network anomalies.

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