We use simulation to compare different power flow models in the process of charging electric vehicles (EVs) by considering their random arrivals, their stochastic demand for energy at charging stations, and the characteristics of the electricity distribution network. We assume the distribution network is a line with charging stations located on it. We consider the Distflow and the Linearized Distflow power flow models and we assume that EVs arrive at the network with an exponential rate, have an exponential charging requirement, and that voltage drops on the distribution network stay under control. We provide extensive numerical results investigating the effect of using different power flow models on the performance of the network.

This special issue of Performance Evaluation contains the proceedings of the Performance 2015 conference, held in Sydney, Australia on October 19–21, 2015. Performance is the flagship conference of the IFIP Working Group 7.3 on Computer Performance Modeling and Analysis. [..]

Heavy-tails are a continual source of excitement and confusion across disciplines as they are repeatedly "discovered" in new contexts. This is especially true within computer systems, where heavy-tails seemingly pop up everywhere -- from degree distributions in the internet and social networks to file sizes and interarrival times of workloads. However, despite nearly a decade of work on heavy-tails they are still treated as mysterious, surprising, and even controversial.

Batching plays an important role in semiconductor fabs, and it can lead to inefficiency if not treated with care. The performance of parallel batch processes is often approximated by the G/G/1-based approximate models. By carefully examining the existing models, the dependence between queueing time and wait-to-batch time has been identified. A new improved model for parallel batch systems is proposed to exploit this dependence. The computation of the new model is still simple and fast, but it gives better approximation by reducing the systematic error in earlier models which ignored the dependence between queueing time and wait-to-batch time.

We analyze dependencies in power law graph data (Web sample, Wikipedia sample and a preferential attachment graph) using statistical inference for multivariate regular variation. The well developed theory of regular variation is widely applied in extreme value theory, telecommunications and mathematical finance, and it provides a natural mathematical formalism for analyzing dependencies between variables with power laws. However, most of the proposed methods have never been used in the Web graph data mining. The present work fills this gap. The new insights this yields are striking: the three above-mentioned data sets are shown to have a totally different dependence structure between different graph parameters, such as in-degree and PageRank.

In this paper we analyze the M/G/1 processor sharing queue with heavy tailed services and with impatient customers. It is assumed that impatience depends on the value of the service required. We prove that a reduced service rate (RSR) approximation holds for estimating the sojourn time of a customer in the system, when the queue capacity is finite or infinite. This allows us to evaluate the reneging probability of customers with very large service times. We then use these results to investigate the impact of admission control on a link of a packet network. Admission control simply consists of limiting the number of simultaneous connections. It turns out that there is a real benefit for the efficiency of the system to perform admission control: It globally increases the fraction of customers, who complete their service (i.e. without being impatient). Finally, we investigate the fairness of the system and propose a criterion to assess the capacity of the system so as to allow the completion of very large service times.

We consider a fluid queue fed by multiple on-off flows with heavy-tailed (regularly varying) on-periods. Under fairly mild assumptions, we prove that the workload distribution is asymptotically equivalent to that in a reduced system. The reduced system consists of a dominant subset of the flows, with the original service rate subtracted by the mean rate of the other flows. We describe how a dominant set may be determined from a simple knapsack formulation. We exploit a powerful intuitive argument to obtain the exact asymptotics for the reduced system. Combined with the reduced-load equivalence, the results for the reduced system provide an asymptotic characterization of the buffer behavior.