Session details: Special issue on the workshop on MAthematical performance Modeling and Analysis (MAMA 2014)

The complexity of computer systems, networks and applications, as well as the advancements in computer technology, continue to grow at a rapid pace. Mathematical analysis, modeling and optimization have been playing, and continue to play, an important role in research studies to investigate fundamental issues and trade-offs at the core of performance problems in the design and implementation of complex computer systems, networks and applications.On June 20, 2014, the 16th Workshop on MAthematical performance Modeling and Analysis (MAMA 2014) was held in Austin TX, USA, sponsored by ACM SIGMETRICS, and held in conjunction with SIGMETRICS 2014. This workshop seeks to bring together researchers working on the mathematical, methodological and theoretical aspects of performance analysis, modeling and optimization. It is intended to provide a forum at SIGMETRICS conferences for talks on early research in the more mathematical areas of computer performance analysis. These talks tend to be based on very recent research results (including work in progress) or on new research results that will be otherwise submitted only to a journal (or recently have been submitted to a journal). Thus, part of the goal is to complement and supplement the SIGMETRICS Conference program with such talks without removing any theoretical contributions from the main technical program. Furthermore, we continue to experience the desired result of having abstracts from previous MAMA workshops appear as full papers in the main program of subsequent SIGMETRICS and related conferences.All submissions were reviewed by at least 4 members of the program committee, from which a total of 13 were selected for presentation at the MAMA 2014 workshop. This special issue of Performance Evaluation Review includes extended abstracts relating to these presentations (arranged in the order of their presentation), which cover a wide range of topics in the area of mathematical performance analysis, modeling and optimization.The study of Gelenbe examines the backlog of energy and of data packets in a sensor node that harvests energy, computing the properties of energy and data backlogs and discussing system stability. Meyfroyt derives asymptotic results for the coverage ratio under a specific class of spatial stochastic models (Cooperative Sequential Adsorption) and investigates the scalability of the Trickle communication protocol algorithm. The study of Tune and Roughan applies the principle of maximum entropy to develop fast traffic matrix synthesis models, with the future goal of developing realistic spatio-temporal traffic matrices. Bradonjic et al. compare and contrast the capacity, congestion and reliability requirements for alternative connectivity models of large-scale data centers relative to fat trees. The study of Rochman et al. considers the problem of resource placement in network applications, based on a largescale service faced with regionally distributed demands for various resources in cloud computing. Xie and Lui investigate the design and analysis of a rating system and a mechanism to encourage users to participate in crowdsourcing and to incentivize workers to develop high-quality solutions. The study of Asadi et al. formulates a general problem for the joint per-user mode selection, connection activation and resource scheduling of connections using both LTE and WiFi resources within the context of device-todevice communications. Zheng and Tan consider a nonconvex joint rate and power control optimization to achieve egalitarian fairness (max-min weighted fairness) in wireless networks, exploiting the nonlinear Perron-Frobenius theory and nonnegative matrix theory. The study of Goldberg et al. derives an asymptotically optimal control policy for a stochastic capacity problem of dynamically matching supply resources and uncertain demand, based on connections with lost-sales inventory models. Ghaderi et al. investigate a dynamic stochastic bin packing problem, analyzing the fluid limits of the system under an asymptotic best-fit algorithm and showing it asymptotically minimizes the number of servers used in steady state. The study of Tizghadam and Leon-Garcia examines the impact of overlaying or removing a subgraph on the Moore-Penrose inverse of the Laplacian matrix of an existing network topology and proposes an iterative method to find key performance measures. Miyazawa considers a two-node generalized Jackson network in a phase-type setting as a special case of a Markov-modulated twodimensional reflecting random walk and analyzes the tail asymptotics for this reflecting process. The study of Squillante et al. investigates improvement in scalability of search in networks through the use of multiple random walks, deriving bounds on the hitting time to a set of nodes and on various performance metrics.