Sonia Fahmy

Bio: Sonia Fahmy is an academic researcher from Purdue University. The author has contributed to research in topics: Asynchronous Transfer Mode & Wireless sensor network. The author has an hindex of 39, co-authored 217 publications receiving 11177 citations. Previous affiliations of Sonia Fahmy include Ohio State University & Hewlett-Packard.

Network Traffic Management

Abstract: Traffic management aims at delivering a negotiated quality of service (QoS) to applications and at controlling congestion in a communication network. In order to accomplish these two goals, several building blocks can be used at traffic senders, receivers, core network routers, and edge routers at the entry and exit of a network domain. These components include routing based on given constraints, admission and policy control for new connections, resource reservation, packet scheduling, policing and shaping, buffer management and closed-loop congestion control. This chapter defines these components, and discusses a number of case studies from Internet architectures. Keywords: traffic management; admission control; policy control; constrained-based routing; policy routing; QoS routing; resource reservation; packet scheduling; buffer management; shaping; policing; congestion control; integrated services; differentiated services; multi-protocol label switching; Transmission Control Protocol (TCP); asynchronous transfer mode (ATM)

Infinity Learning: Learning Markov Chains from Aggregate Steady-State Observations

Abstract: We consider the task of learning a parametric Continuous Time Markov Chain (CTMC) sequence model without examples of sequences, where the training data consists entirely of aggregate steady-state statistics. Making the problem harder, we assume that the states we wish to predict are unobserved in the training data. Specifically, given a parametric model over the transition rates of a CTMC and some known transition rates, we wish to extrapolate its steady state distribution to states that are unobserved. A technical roadblock to learn a CTMC from its steady state has been that the chain rule to compute gradients will not work over the arbitrarily long sequences necessary to reach steady state —from where the aggregate statistics are sampled. To overcome this optimization challenge, we propose ∞-SGD, a principled stochastic gradient descent method that uses randomly-stopped estimators to avoid infinite sums required by the steady state computation, while learning even when only a subset of the CTMC states can be observed. We apply ∞-SGD to a real-world testbed and synthetic experiments showcasing its accuracy, ability to extrapolate the steady state distribution to unobserved states under unobserved conditions (heavy loads, when training under light loads), and succeeding in difficult scenarios where even a tailor-made extension of existing methods fails.

Towards High Fidelity Network Emulation

Abstract: Instantiating a distributed application that involves extensive inter-node communication onto a network is a challenging task. In this work, we focus on the special case of mapping a network emulation experiment onto a cluster comprising several (possibly heterogeneous) physical machines. We automatically profile the available physical machine resources, and use this information, together with the characteristics of the experimental topology, to determine an efficient mapping that preserves performance fidelity. We design an algorithm, which we call the "Waterfall" algorithm}, and integrate it into a complete framework for profiling and mapping. We demonstrate the effectiveness of our framework via simulations and two sets of Crossfire Distributed Denial of Service attack testbed experiments.

Providing Rate Guarantees to TCP over the ATM GFR Service

Abstract: The ATM Guaranteed Frame Rate (GFR) service is intended for best effort traffic that can benefit from minimum throughput guarantees. Edge devices connecting LANs to an ATM network can use GFR to transport multiple TCP/IP connections over a single GFR VC.These devices would typically multiplex VCs into a single FIFO queue. It has been shown that in general, FIFO queuing is not sufficient to provide rate guarantees, and per-VC queuing with scheduling is needed. We show that under conditions of low buffer allocation, it is possible to control TCP rates with FIFO queuing and buffer management. We present analysis and simulation results on controlling TCP rates by buffer management. We present a buffer management policy that provides loose rate guarantees to SACK TCP sources when the total buffer allocation is low. We study the performance of this buffer management scheme by simulation.

Location matters: Eliciting responses to direct probes

Abstract: In this work, we propose techniques to attain visibility into an arbitrary Internet subnetwork that is responsive to indirect probes but not to direct probes. By probing the network from a small number of selected vantage points, we are able to collect information about network-layer topology which would otherwise be hidden from measurement due to rate limiting practices, security mechanisms, and routing dynamics. We investigate the reasons for differing visibility, and the required number and placement strategies of vantage points needed to collect topology information at a low cost. We demonstrate substantial improvement in global visibility as probed by the TraceNET path measurement tool when leveraging only five vantage points selected according to route similarity.

HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

Abstract: Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. We propose a novel distributed clustering approach for long-lived ad hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in O(1) iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation.

Topology Control in Wireless Ad Hoc and Sensor Networks

Abstract: Topology Control (TC) is one of the most important techniques used in wireless ad hoc and sensor networks to reduce energy consumption (which is essential to extend the network operational time) and radio interference (with a positive effect on the network traffic carrying capacity). The goal of this technique is to control the topology of the graph representing the communication links between network nodes with the purpose of maintaining some global graph property (e.g., connectivity), while reducing energy consumption and/or interference that are strictly related to the nodes' transmitting range. In this article, we state several problems related to topology control in wireless ad hoc and sensor networks, and we survey state-of-the-art solutions which have been proposed to tackle them. We also outline several directions for further research which we hope will motivate researchers to undertake additional studies in this field.