Stability and diversity of end-to-end routes are key properties of Internet that have a large effect on the design of networks and networked systems. As the Internet evolves and deploys new technologies, it is important to re-assess these properties in the face of the new realities. This paper evaluates the stability and diversity of Internet routes with emphasis on the impact of the widely deployed Multi-Protocol Label Switching (MPLS). In particular, we analyze two datasets: (1) high frequency (once per minute) traceroutes originating from a number of PlanetLab hosts to a set of destinations distributed around the Internet (random hosts extracted from the Alexa list of top-1M websites), and (2) traceroutes originating from a large number of Ripe Atlas probes to a number of hosts (mostly root DNS servers). We find that Internet routes are significantly less stable than previously reported, at least according to one common metric (the route persistence), and much more diverse. At the same time, these more frequent route changes do not translate to high variability of route-trip time (RTT) delays between hosts, as RTTs tend to stay similar across route changes. Notably, with regard to diversity, we show that higher diversity is likely due not to a real change in the Internet but rather to underestimation by previous studies. Finally, our assessment of MPLS role in increased path instability produced inconclusive results as MPLS appears to be a significant contributor in the Ripe Atlas data set but not in the Plant Lab dataset.

The paper revisits the performance evaluation of caching in a Named Data Networking (NDN) router where the content store (CS) is supplemented by a pending interest table (PIT). The PIT aggregates requests for a given content that arrive within the download delay and thus brings an additional reduction in upstream bandwidth usage beyond that due to CS hits. We extend prior work on caching with non-zero download delay (non-ZDD) by proposing a novel mathematical framework that is more easily applicable to general traffic models and by considering alternative cache insertion policies. Specifically we evaluate the use of an LRU filter to improve CS hit rate performance in this non-ZDD context. We also consider the impact of time locality in demand due to finite content lifetimes. The models are used to quantify the impact of the PIT on upstream bandwidth reduction, demonstrating notably that this is significant only for relatively small content catalogues or high average request rate per content. We further explore how the effectiveness of the filter with finite content lifetimes depends on catalogue size and traffic intensity.

In this paper, we consider an MX/M/1/GSET-VARI queue, which is a single server queue with a variable service speed, batch arrivals and general setup times. The service speed of the server is proportional to the number of jobs in the system. Our model is motivated by power-aware servers in data centers where dynamic scaling techniques are used. In this paper, we obtain the necessary and sufficient condition for the stability of the system and derive an expression for the probability generating function of the number of jobs in the system. In addition, we present some numerical results in order to show the energy-performance trade-off of our queueing system.

This paper investigates the performance of the numerical inverse Laplace transformation (ILT) method based on concentrated matrix exponential (CME) distributions, referred to as the CME method. The CME method does not generate overshoot and undershoot (i.e., avoids Gibbs oscillation), preserves monotonicity of functions, its accuracy is gradually improving with the order, and it is numerically stable even for order 1000 when using machine precision arithmetic, while other methods get unstable already for order 100 using the same arithmetic. For ILT based tail approximation, the paper recommends an abscissa shifting approach which improves the accuracy of most ILT methods and proposes a heuristic procedure to approximate the numerical accuracy of some ILT methods.

We consider a resource allocation problem in a geographically distributed cloud network, where the goal is to obtain the capacities of the servers across the network in order to minimize the overall cost. In this study, the system resources (servers) are subject to failures, due to occasional breakdowns or cyber attacks. As a result, the servers supply is of a stochastic nature. From an optimization point of view, we are facing a non-convex multi-dimensional problem. For the solution, we propose an efficient algorithm to obtain the optimal capacities of the servers in all the regions in the network, where computational experience is presented and discussed. We then numerically analyze the effect of the supply stochastic properties, namely expected volume, variability and correlation across regions, on system performance. The methodology and the results can be used to evaluate the effect of cyber attacks on resource allocation in geographically distributed systems and on the planning of these systems.