Craig Gutterman

Scalable WiFi multicast services for very large groups

IEEE 802.11-based wireless local area networks, referred to as WiFi, have been globally deployed and the vast majority of mobile devices are currently WiFi-enabled. While WiFi has been proposed for multimedia content distribution, its lack of adequate support for multicast services hinders its ability to provide multimedia content distribution to a large number of devices. We propose AMuSe, a scalable and adaptive interference mitigation solution for WiFi multicast services which is based on accurate receiver feedback and that incurs a small control overhead. Specifically, we develop a scheme for dynamic selection of a subset of the multicast receivers as feedback nodes, which periodically send information, such as channel quality or received packet statistics, to the multicast sender. This feedback information is used by the multicast sender to optimize the multicast service quality, e.g., by dynamically adjusting the transmission bit-rate. Our proposed solution does not require any changes to the standards or any modifications to the WiFi devices. We have implemented the proposed solution in the ORBIT testbed and evaluated its performance in large groups with approximately 250 receivers, both with and without interference sources. Our online experiments demonstrate that our system provides practical multicast services that can accommodate hundreds of receivers.

Experimental evaluation of large scale WiFi multicast rate control

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the Multicast Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the rate adaptation response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality.

Dynamic mitigation of EDFA power excursions with machine learning

Dynamic optical networking has promising potential to support the rapidly changing traffic demands in metro and long-haul networks. However, the improvement in dynamicity is hindered by wavelength-dependent power excursions in gain-controlled erbium doped fiber amplifiers (EDFA) when channels change rapidly. We introduce a general approach that leverages machine learning (ML) to characterize and mitigate the power excursions of EDFA systems with different equipment and scales. An ML engine is developed and experimentally validated to show accurate predictions of the power dynamics in cascaded EDFAs. Recommended channel provisioning based on the ML predictions achieves within 1% error of the lowest possible power excursion over 94% of the time. We also showcase significant mitigation of EDFA power excursions in super-channel provisioning when compared to the first-fit wavelength assignment algorithm.

Experimental Evaluation of a Scalable WiFi Multicast Scheme in the ORBIT Testbed

IEEE 802.11-based wireless local area networks, referred to as WiFi, have been globally deployed and the vast majority of the mobile devices are currently WiFi-enabled. While WiFi has been proposed for multimedia content distribution, its lack of adequate support for multicast services hinders its ability to provide multimedia content distribution to a large number of devices. In earlier work, we proposed a dynamic scheme called AMuSe that selects a subset of the multicast receivers as feedback nodes. The feedback nodes periodically send information about channel quality to the multicast sender and the sender in turn can optimize multicast service quality, e.g., by dynamically adjusting transmission bit-rate. In this paper, we discuss several experimental results for the performance evaluation of AMuSe. Our experiments use more than 250 nodes placed in a grid topology in the ORBIT testbed. We consider different experimental scenarios: with and without the presence of external noise. Our focus is on studying the performance of WiFi nodes in WiFi multicast and establishing the conditions that make AMuSe an attractive scheme for feedback in WiFi multicast.

Cognitive radio kit framework: experimental platform for dynamic spectrum research

This paper presents an overview of a Cognitive Radio Kit, an open software defined radio framework developed specifically to enable experimental research in cognitive radio and dynamic spectrum techniques. Currently available open software defined platforms are limited by performance and bandwidth constraints, and inadequate frequency tuning range at the RF front-end. The proposed platform addressed those limitations by providing the ability to dynamically add hardware based acceleration for baseband processing, coupled with up to four wide-tuning range RF front-ends. The challenge resides in defining the architecture and programming model for the platform. All those considerations along with an application example are discussed and presented in this paper.

Neural Network Based Wavelength Assignment in Optical Switching

Greater network flexibility through software defined networking and the growth of high bandwidth services are motivating faster service provisioning and capacity management in the optical layer. These functionalities require increased capacity along with rapid reconfiguration of network resources. Recent advances in optical hardware can enable a dramatic reduction in wavelength provisioning times in optical circuit switched networks. To support such operations, it is imperative to reconfigure the network without causing a drop in service quality to existing users. Therefore, we present a system that uses neural networks to predict the dynamic response of an optically circuit-switched 90-channel multi-hop Reconfigurable Optical Add-Drop Multiplexer (ROADM) network. The neural network is able to recommend wavelength assignments that contain the power excursion to less than 0.5 dB with a precision of over 99%.

Light-Weight Feedback Mechanism for WiFi Multicast to Very Large Groups—Experimental Evaluation

WiFi networks have been globally deployed and most mobile devices are currently WiFi-enabled. While WiFi has been proposed for multimedia content distribution, its lack of adequate support for multicast services hinders its ability to provide multimedia content distribution to a large number of devices. In this paper, we present the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast services. AMuSe is based on accurate receiver feedback and incurs a small control overhead. In particular, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes, which periodically send information about the channel quality to the multicast sender. This feedback information can be used by the multicast sender to optimize multicast service quality, e.g., by dynamically adjusting transmission bitrate. AMuSe does not require any changes to the standards or any modifications to the WiFi devices. We implemented AMuSe on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi devices, both with and without interference sources. Our extensive experiments demonstrate that AMuSe can provide accurate feedback in a dense multicast environment. It outperforms several alternatives even in the case of external interference and changing network conditions.

AMuSe: Adaptive Multicast Services to Very Large Groups - Project Overview

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed approaches do not provide performance guarantees. In this paper, we describe the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast services. AMuSe includes a lightweight feedback mechanism that allows monitoring channel quality of a large number of users. This feedback allows the system to dynamically optimize the multicast transmission rate at the AP. We implemented AMuSe on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi devices in different scenarios. We show that AMuSe supports high throughput multicast flows to hundreds of receivers while meeting quality requirements and that it outperforms other systems.

AMuSe: Large-scale WiFi video distribution - experimentation on the ORBIT testbed.

Currently, wireless video distribution cannot be provided in crowded venues due to resource limitations. In our recent papers we proposed AMuSe, a scalable system for WiFi multicast video delivery. The system includes a scheme for dynamic selection of a subset of the receivers as feedback nodes and a rate adaptation algorithm MuDRA that maximizes the channel utilization while meeting QoS requirements. We implemented AMuSe in the ORBIT testbed and evaluated its performance with 150-200 nodes. We present a dynamic web-based application that demonstrates the operation of AMuSe based on traces collected on the testbed in several experiments. The application allows to compare the performance of AMuSe with other multicast schemes and evaluate the performance of video delivery.

Experimental Evaluation of Large Scale WiFi Multicast Rate Control

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, the most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the multicast dynamic rate adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the RA response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high-quality video streams, where 90% of the nodes report excellent or very good video quality.