The dimensioning of caching systems represents a difficult task in the design of infrastructures for content distribution in the current Internet. This paper addresses the problem of defining a realistic arrival process for the content requests generated by users, due its critical importance for both analytical and simulative evaluations of the performance of caching systems. First, with the aid of YouTube traces collected inside operational residential networks, we identify the characteristics of real traffic that need to be considered or can be safely neglected in order to accurately predict the performance of a cache. Second, we propose a new parsimonious traffic model, named the Shot Noise Model (SNM), that enables users to natively capture the dynamics of content popularity, whilst still being sufficiently simple to be employed effectively for both analytical and scalable simulative studies of caching systems. Finally, our results show that the SNM presents a much better solution to account for the temporal locality observed in real traffic compared to existing approaches.

The aggregate bandwidth of a switch is its port count multiplied by its operating line rate. We consider switches with high-aggregate bandwidths; for example, a 30-port switch operating at 40 Gb/s or a 1000-port switch operating at 1 Gb/s. Designing high-performance schedulers for such switches with input queues is a challenging problem for the following reasons: 1) high performance requires finding good matchings; 2) good matchings take time to find; and 3) in high-aggregate bandwidth switches there is either too little time (due to high line rates) or there is too much work to do (due to a high port count). We exploit the following features of the switching problem to devise simple-to-implement, high-performance schedulers for highaggregate bandwidth switches: 1) the state of the switch (carried in the lengths of its queues) changes slowly with time, implying that heavy matchings will likely stay heavy over a period of time and 2) observing arriving packets will convey useful information about the state of the switch. The above features are exploited using hardware parallelism and randomization to yield three scheduling algorithms-APSARA, LAURA, and SERENA. These algorithms are shown to achieve 100% throughput and simulations show that their delay performance is quite close to that of the maximum weight matching, even when the traffic is correlated. We also consider the stability property of these algorithms under generic admissible traffic using the fluid-model technique. The main contribution of this paper is a suite of simple to implement, high-performance scheduling algorithms for input-queued switches. We exploit a novel operation, called MERGE, which combines the edges of two matchings to produce a heavier match, and study of the properties of this operation via simulations and theory. The stability proof of the randomized algorithms we present involves a derandomization procedure and uses methods which may have wider applicability.

Abstract-This paper studies input-queued packet switches loaded with both unicast and multicast traffic. The packet switch architecture is assumed to comprise a switching fabric with multicast (and broadcast) capabilities, operating in a synchronous slotted fashion. Fixed-size data units, called cells, are transferred from each switch input to any set of outputs in one time slot, according to the decisions of the switch scheduler, that identifies at each time slot a set of nonconflicting cells, i.e., cells neither coming from the same input, nor directed to the same output.First, multicast traffic admissibility conditions are discussed, and a simple counterexample showing intrinsic performance losses of input-queued with respect to output-queued switch architectures is presented. Second, the optimal scheduling discipline to transfer multicast packets from inputs to outputs is defined. This discipline is rather complex, requires a queuing architecture that probably is not implementable, and does not guarantee in-sequence delivery of data. However, from the definition of the optimal multicast scheduling discipline, the formal characterization of the sustainable multicast traffic region naturally follows. Then, several theorems showing intrinsic performance losses of input-queued with respect to output-queued switch architectures are proved. In particular, we prove that, when using per multicast flow FIFO queueing architectures, the internal speedup that guarantees 100% throughput under admissible traffic grows with the number of switch ports.

We provide a general framework for the analysis of the capacity scaling properties in mobile ad-hoc networks with heterogeneous nodes and spatial inhomogeneities. Existing analytical studies strongly rely on the assumption that nodes are identical and uniformly visit the entire network space. Experimental data, however, have shown that the mobility pattern of individual nodes is typically restricted over the area, while the overall node density is often largely inhomogeneous, due to prevailing clustering behavior resulting from hot-spots. Such ubiquitous features of realistic mobility processes demand to reconsider the scaling laws for the peruser throughput achievable by the store-carry-forward communication paradigm which provides the foundation of many promising applications of delay tolerant networking. We show how the analysis of the asymptotic capacity of dense mobile ad-hoc networks can be transformed, under mild assumptions, into a Maximum Concurrent Flow (MCF) problem over an associated Generalized Random Geometric Graph (GRGG). Our methodology allows to identify the scaling laws for a general class of mobile wireless networks, and to precisely determine under which conditions the mobility of nodes can indeed be exploited to increase the per-node throughput. At last we propose a simple, asymptotically optimal, scheduling and routing scheme that achieves the maximum transport capacity of the network.

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