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A novel scheme for processing packets in a router is presented, which provides for load sharing among multiple network processors distributed within the router. It is complemented by a feedback control mechanism designed to prevent processor overload. Incoming traffic is scheduled to multiple processors based on a deterministic mapping. The mapping formula is derived from the robust hash routing (also known as the highest random weight-HRW) scheme, introduced in K.W. Ross, IEEE Network, 11(6), 1997, and D.G. Thaler et al., IEEE Trans. Networking, 6(1), 1998. No state information on individual flow mapping needs to be stored, but for each packet, a mapping function is computed over an identifier vector, a predefined set of fields in the packet. An adaptive extension to the HRW scheme is provided in order to cope with biased traffic patterns. We prove that our adaptation possesses the minimal disruption property with respect to the mapping and exploit that property in order to minimize the probability of flow reordering. Simulation results indicate that the scheme achieves significant improvements in processor utilization. A higher number of router interfaces can thus be supported with the same amount of processing power.

Abstract-We present a concrete methodology for saving energy in future and contemporary cellular networks. It is based on re-arranging the user-cell association so as to allow shutting down under-utilized parts of the network. We consider a hypothetical static case where we have complete knowledge of stationary user locations and thus the results represent an upper bound of potential energy savings. We formulate the problem as a binary integer programming problem, thus it is NP-hard, and we present a heuristic approximation method. We simulate the methodology on an example real cellular network topology with trafficand user distribution generated according to recently measured patterns. Further, we evaluate the energy savings, using realistic energy profiles, and the impact on the user-perceived network performance, represented by delay and throughput, at various times of day. The general findings conclude that up to 50% energy may be saved in less busy periods, while the performance effects remain limited. We conclude that practical, real-time usercell re-allocation methodology, taking into account user mobility predictions, may thus be feasible and bring significant energy savings at acceptable performance impact.

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