Abstract
Active networks provide a programmable userβnetwork interface that supports dynamic modification of the network's behavior. Network nodes, in addition to forwarding packets, perform customized computation on the messages flowing through them. Resources in an active network mainly consist of CPU and bandwidth. The inherent unpredictability of processing times of active packet poses a significant challenge in CPU scheduling. It has been identified that prior estimation of the resource requirements of a packet is very difficult since it is platform dependent and also depends on processing load at the time of execution, operating system scheduling, etc. An efficient allocation is required for the optimal utilization of resources. In this paper, resources are estimated using prediction techniques such as single exponential smoothing (SES), adaptiveβresponseβrate single exponential smoothing (ARRSES) and Holt's twoβparameter estimation models. The estimated results agreed most with the actual requirements. The estimation models were compared with model criteria. An algorithm was also designed for the allocation of resources. Effectiveness of the algorithm was measured through simulation and achieved almost perfect fairness for all flows and also provided much superior delay guarantees under a highly dynamic environment. Copyright Β© 2008 John Wiley & Sons, Ltd.