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Two important Quality-of-Service (QoS) measures for current cellular networks are the fractions of new and handoff "calls" that are blocked due to unavailabili t y of "channels" (radio and/or computing resources).Based on these QoS measures, we derive optimal admission control policies for three problems: minimizing a linear objective function of the new and handoff call blocking probabilities (MINOBJ), minimizing the new call blocking probability with a hard constraint on the handoff call blocking probability (MINBLOCK) and minimizing the number of channels with hard constraints on both of the blocking probabilities (MINC). We show that the well-known Guard Channel policy is optimal for the MINOBJ problem, while a new Fractional Guard Channel policy is optimal for the MINBLOCK and MINC problems. The Guard Channel policy reserves a set of channels for handoff calls while the Fractional Guard Channel policy eflectively reserves a non-integral number of guard channels for handoff calls by rejecting new calls with some probability that depends on the current channel occupancy.It is also shown that the Fractional policy results in significant savings (20-50%) in the new call blocking probability for the MINBLOCK problem and provides some, though small, gains over the Integral Guard Channel policy for the MINC problem. Further, we also develop computationally inezpensive algorithms for the determination of the parameters for the optimal policies.

The Code Red worm incident of July 2001 has stimulated activities to model and analyze Internet worm propagation. In this paper we provide a careful analysis of Code Red propagation by accounting for two factors: one is the dynamic countermeasures taken by ISPs and users; the other is the slowed down worm infection rate because Code Red rampant propagation caused congestion and troubles to some routers. Based on the classical epidemic Kermack-Mckendrick model, we derive a general Internet worm model called the twofactor worm model. Simulations and numerical solutions of the two-factor worm model match the observed data of Code Red worm better than previous models do. This model leads to a better understanding and prediction of the scale and speed of Internet worm spreading.

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Neighbor discovery is one of the first steps in the initialization of a wireless ad hoc network. In this paper, we design and analyze practical algorithms for neighbor discovery in wireless networks. We first consider an ALOHA-like neighbor discovery algorithm in a synchronous system, proposed in an earlier work. When nodes do not have a collision detection mechanism, we show that this algorithm reduces to the classical Coupon Collector's Problem. Consequently, we show that each node discovers all its n neighbors in an expected time equal to ne(ln n+c), for some constant c. When nodes have a collision detection mechanism, we propose an algorithm based on receiver status feedback which yields a ln n improvement over the ALOHA-like algorithm.Our algorithms do not require nodes to have any estimate of the number of neighbors. In particular, we show that not knowing n results in no more than a factor of two slowdown in the algorithm performance. In the absence of node synchronization, we develop asynchronous neighbor discovery algorithms that are only a factor of two slower than their synchronous counterparts. We show that our algorithms can achieve neighbor discovery despite allowing nodes to begin execution at different time instants. Furthermore, our algorithms allow each node to detect when to terminate the neighbor discovery phase.