Directional antennas improve the link-connectivity of interference limited ad hoc networks

We study wireless ad hoc networks in the absence of any channel contention or transmit power control and ask how antenna directivity affects network connectivity in the interference limited regime. We answer this question by deriving closed-form expressions for the outage probability, capacity and mean node degree of the network using tools from stochastic geometry. These novel results provide valuable insights for the design of future ad hoc networks. Significantly, our results suggest that the more directional the interfering transmitters are, the less detrimental are the effects of interference to individual links. We validate our analytical results through computer simulations.Comment: 6 pages, 7 figures, conference proceedings of PIMRC'201

CapEst: A Measurement-based Approach to Estimating Link Capacity in Wireless Networks

Estimating link capacity in a wireless network is a complex task because the available capacity at a link is a function of not only the current arrival rate at that link, but also of the arrival rate at links which interfere with that link as well as of the nature of interference between these links. Models which accurately characterize this dependence are either too computationally complex to be useful or lack accuracy. Further, they have a high implementation overhead and make restrictive assumptions, which makes them inapplicable to real networks. In this paper, we propose CapEst, a general, simple yet accurate, measurement-based approach to estimating link capacity in a wireless network. To be computationally light, CapEst allows inaccuracy in estimation; however, using measurements, it can correct this inaccuracy in an iterative fashion and converge to the correct estimate. Our evaluation shows that CapEst always converged to within 5% of the correct value in less than 18 iterations. CapEst is model-independent, hence, is applicable to any MAC/PHY layer and works with auto-rate adaptation. Moreover, it has a low implementation overhead, can be used with any application which requires an estimate of residual capacity on a wireless link and can be implemented completely at the network layer without any support from the underlying chipset

Random Access Scheduling without Message Passing: A Collision-based AIMD Approach

Department of Computer EngineeringWireless scheduling has been extensively studied in the literature. Since Maximum Weighted Scheduling has been developed and shown to achieve the optimal performance, there have been many efforts to overcome its complexity issue. Random access has attracted much attention due to its potential for low complexity and distributed control, which are desirable for scheduling in multi-hop wireless networks. Although several interesting random access scheduling schemes have been shown to be provably efficient, they suffer in practice from high packet delays or severe performance degradation due to the control overhead to exchange information between neighboring links. In this paper, we develop a novel random access scheduling scheme that does not need message passing. We pay attention to the interplay between the links and control their access probabilities targeting at a certain collision rate. We employ the Additive Increase Multiplicative Decrease (AIMD) algorithm for convergence, and show that our proposed scheme can achieve the same performance bound as the previous random access schemes with high control overhead. We verify our results through simulations and show that our proposed scheme achieves the performance close to that of the centralized greedy algorithm.ope

Capacity of Asynchronous Random-Access Scheduling in Wireless Networks

Abstract—We study the throughput capacity of wireless networks which employ (asynchronous) random-access scheduling as opposed to deterministic scheduling. The central question we answer is: how should we set the channel-access probability for each link in the network so that the network operates close to its optimal throughput capacity? We design simple and distributed channel-access strategies for random-access networks which are provably competitive with respect to the optimal scheduling strategy, which is deterministic, centralized, and computationally infeasible. We show that the competitiveness of our strategies are nearly the best achievable via random-access scheduling, thus establishing fundamental limits on the performance of randomaccess. A notable outcome of our work is that random access compares well with deterministic scheduling when link transmission durations differ by small factors, and much worse otherwise. The distinguishing aspects of our work include modeling and rigorous analysis of asynchronous communication, asymmetry in link transmission durations, and hidden terminals under arbitrary link-conflict based wireless interference models. I

Longest-queue-first scheduling under SINR interference model

We investigate the performance of longest-queue-first (LQF) scheduling (i.e., greedy maximal scheduling) for wireless networks under the SINR interference model. This interference model takes network geometry and the cumulative interference effect into account, which, therefore, capture the wireless interference more precisely than binary interference models. By employing the ρ-local pooling technique, we show that LQF scheduling achieves zero throughput in the worst case. We then propose a novel technique to localize interference which enables us to decentralize the LQF scheduling while preventing it from having vanishing throughput in all network topologies. We characterize the maximum throughput region under interference localization and present a distributed LQF scheduling algorithm. Finally, we present numerical results to illustrate the usefulness and to validate the theory developed in the paper.United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-08-1-0238)National Science Foundation (U.S.) (Grant CNS-0915988)United States. Defense Threat Reduction Agency (Grant HDTRA1-07-1-0004

Leveraging Physical Layer Capabilites: Distributed Scheduling in Interference Networks with Local Views

In most wireless networks, nodes have only limited local information about the state of the network, which includes connectivity and channel state information. With limited local information about the network, each node's knowledge is mismatched; therefore, they must make distributed decisions. In this paper, we pose the following question - if every node has network state information only about a small neighborhood, how and when should nodes choose to transmit? While link scheduling answers the above question for point-to-point physical layers which are designed for an interference-avoidance paradigm, we look for answers in cases when interference can be embraced by advanced PHY layer design, as suggested by results in network information theory. To make progress on this challenging problem, we propose a constructive distributed algorithm that achieves rates higher than link scheduling based on interference avoidance, especially if each node knows more than one hop of network state information. We compare our new aggressive algorithm to a conservative algorithm we have presented in [1]. Both algorithms schedule sub-networks such that each sub-network can employ advanced interference-embracing coding schemes to achieve higher rates. Our innovation is in the identification, selection and scheduling of sub-networks, especially when sub-networks are larger than a single link.Comment: 14 pages, Submitted to IEEE/ACM Transactions on Networking, October 201

Dynamic scheduling based on particle swarm optimization for cloud-based scientific experiments

Los Experimentos de Barrido de Parámetros (PSEs) permiten a los científicos llevar a cabo simulaciones mediante la ejecución de un mismo código con diferentes entradas de datos, lo cual genera una gran cantidad de trabajos intensivos en CPU que para ser ejecutados es necesario utilizar entornos de cómputo paralelos. Un ejemplo de este tipo de entornos son las Infraestructura como un Servicio (IaaS) de Cloud, las cuales ofrecen máquinas virtuales (VM) personalizables que son asignadas a máquinas físicas disponibles para luego ejecutar los trabajos. Además, es importante planificar correctamente la asignación de las máquinas físicas del Cloud, y por lo tanto es necesario implementar estrategias eficientes de planificación para asignar adecuadamente las VMs en las máquinas físicas. Una planificación eficiente constituye un desafío, debido a que es un problema NP-Completo. En este trabajo describimos y evaluamos un planificador Cloud basado en Optimización por Enjambre de Partículas (PSO). Las métricas principales de rendimiento a estudiar son el número de usuarios que el planificador es capáz de servir exitosamente y el número total de VMs creadas en un escenario online (no por lotes). Además, en este trabajo se evalúa el número de mensajes enviados a través de la red. Los experimentos son realizados mediante el uso del simulador CloudSim y datos de trabajos de problemas científicos reales. Los resultados muestran que nuestro planificador logra el mejor rendimiento respecto de las métricas estudiadas con respecto a una asignación random y algoritmos genéticos. En este trabajo también evaluamos el rendimiento, a través de las métricas propuestas, cuando se provee al planificador información cualitativa relacionada a la longitud de los trabajos o no se provee la misma.Parameter Sweep Experiments (PSEs) allow scientists to perform simulations by running the same code with different input data, which results in many CPU-intensive jobs, and hence parallel computing environments must be used. Within these, Infrastructure as a Service (IaaS) Clouds offer custom Virtual Machines (VM) that are launched in appropriate hosts available in a Cloud to handle such jobs. Then, correctly scheduling Cloud hosts is very important and thus efficient scheduling strategies to appropriately allocate VMs to physical resources must be developed. Scheduling is however challenging due to its inherent NP-completeness. We describe and evaluate a Cloud scheduler based on Particle Swarm Optimization (PSO). The main performance metrics to study are the number of Cloud users that the scheduler is able to successfully serve, and the total number of created VMs, in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performedusing CloudSim and job data from real scientific problems show that our scheduler achieves better performance than schedulers based on Random assignment and Genetic Algorithms. We also study the performance when supplying or not job information to the schedulers, namely a qualitative indication of job length.Fil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto de Tecnologías de la Información y las Comunicaciones; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto de Tecnologías de la Información y las Comunicaciones; Argentin