The IEEE 802.11n standard allows wireless devices to operate on 40MHz-width channels by doubling their channel width from standard 20MHz channels, a concept called channel bonding. Increasing channel width should increase bandwidth, but it comes at the cost of decreased transmission range and greater susceptibility to interference. However, with the incorporation of MIMO (Multiple-Input MultipleOutput) technology in 802.11n, devices can now exploit the increased transmission rates from wider channels at a reduced sacrifice to signal quality and range. The goal of our work is to understand the characteristics of channel bonding in 802.11n networks and the factors that influence that behavior to ultimately be able to predict behavior so that network performance is maximized. We discuss the impact of channel bonding choices as well as the effects of both cochannel and adjacent channel interference on network performance. We discover that intelligent channel bonding decisions rely not only on a link's signal quality, but also on the strength of neighboring links and their physical rates.

Online Social Networks (OSN) are fun, popular, and socially significant. An integral part of their success is the immense size of their global user base. To provide a consistent service to all users, Facebook, the world's largest OSN, is heavily dependent on centralized U.S. data centers, which renders service outside of the U.S. sluggish and wasteful of Internet bandwidth. In this paper, we investigate the detailed causes of these two problems and identify mitigation opportunities. Because details of Facebook's service remain proprietary, we treat the OSN as a black box and reverse engineer its operation from publicly available traces. We find that contrary to current wisdom, OSN state is amenable to partitioning and that its fine grained distribution and processing can significantly improve performance without loss in service consistency. Through simulations of reconstructed Facebook traffic over measured Internet paths, we show that user requests can be processed 79% faster and use 91% less bandwidth. We conclude that the partitioning of OSN state is an attractive scaling strategy for Facebook and other OSN services.

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Abstract-Online spectrum auctions offer ample flexibility for bidders to request and obtain spectrum on-the-fly. Such flexibility, however, opens up new vulnerabilities to bidder manipulation. Aside from rigging their bids, selfish bidders can falsely report their arrival time to game the system and obtain unfair advantage over others. Such time-based cheating is easy to perform yet produces severe damage to auction performance.We propose Topaz, a truthful online spectrum auction design that distributes spectrum efficiently while discouraging bidders from misreporting their bids or time report. Topaz makes three key contributions. First, Topaz applies a 3D bin packing mechanism to distribute spectrum across time, space and frequency, exploiting spatial and time reuse to improve allocation efficiency. Second, Topaz enforces truthfulness using a novel temporalsmoothed critical value based pricing. Capturing the temporal and spatial dependency among bidders who arrive subsequently, this pricing effectively diminishes gain from bid and/or timecheating. Finally, Topaz offers a "scalable" winner preemption to address the uncertainty of future arrivals at each decision time, which significantly boosts auction revenue. We analytically prove Topaz's truthfulness, which does not require any knowledge of bidder behavior, or an optimal spectrum allocation to enforce truthfulness. Using empirical arrival and bidding models, we perform simulations to demonstrate the efficiency of Topaz. We show that proper winner preemption improves auction revenue by 45-65% at a minimum cost of spectrum utilization.

Abstract-The emergence of MIMO antennas and channel bonding in 802.11n wireless networks has resulted in a huge leap in capacity compared with legacy 802.11 systems. This leap, however, adds complexity to selecting the right transmission rate. Not only does the appropriate data rate need to be selected, but also the MIMO transmission technique (e.g., Spatial Diversity or Spatial Multiplexing), the number of streams, and the channel width. Incorporating these features into a rate adaptation (RA) solution requires a new set of rules to accurately evaluate channel conditions and select the appropriate transmission setting with minimal overhead. To address these challenges, we propose ARAMIS (Agile Rate Adaptation for MIMO Systems), a standard-compliant, closed-loop RA solution that jointly adapts rate and bandwidth. ARAMIS adapts transmission rates on a per-packet basis; we believe it is the first 802.11n RA algorithm that simultaneously adapts rate and channel width. We have implemented ARAMIS on Atheros-based devices and deployed it on our 15-node testbed. Our experiments show that ARAMIS accurately adapts to a wide variety of channel conditions with negligible overhead. Furthermore, ARAMIS outperforms existing RA algorithms in 802.11n environments with up to a 10 fold increase in throughput.

The emergence of MIMO antennas and channel bonding in 802.11n wireless networks has resulted in a huge leap in capacity compared with legacy 802.11 systems.This leap, however, adds complexity to optimizing transmission. Not only does the appropriate data rate need to be selected, but also the MIMO transmission technique (e.g., Spatial Diversity or Spatial Multiplexing), the number of streams, and the channel width. Incorporating these features into a rate adaptation (RA) solution requires a new set of rules to accurately evaluate channel conditions and select the appropriate transmission setting with minimal overhead. To address these challenges, our contributions in this work are two-fold. First, we propose a practical link metric that accurately captures channel conditions in MIMO 802.11n environments, and we call this metric diffSNR. Using diffSNR captured from real testbed environments, we build performance models that accuractely predict link quality in 95.5% of test cases. Practicality and deployability are guaranteed with diffSNR as it can be measured on all off-the-shelf MIMO WiFi chipsets. Second, we propose ARAMIS (Agile Rate Adaptation for MIMO Systems), a standard-compliant, closed-loop RA solution that jointly adapts rate and bandwidth, and we utilize the diffSNR-based 802.11n performance models within ARAMIS's framework. ARAMIS adapts transmission rates on a perpacket basis; we believe it is the first closed-loop, 802.11 RA algorithm that simultaneously adapts rate and channel width. We have implemented ARAMIS with diffSNR on Atheros-based devices and deployed it on our 15-node testbed. Our experiments show Preprint submitted to Elsevier February 9, 2015 that ARAMIS accurately adapts to a wide variety of channel conditions with negligible overhead. Furthermore, ARAMIS outperforms existing RA algorithms in 802.11nenvironments with up to a 10 fold increase in throughput.

-The rapid increase and proliferation of mobile wireless technologies has led to the rise of new problems that fall under the umbrella of challenged networks, namely, intermittent network connectivity. This diversity in wireless devices, along with their convergence, has granted users access to multiple heterogeneous networks available in parallel. owadays, a user generally expects to be connected in all places at all times. To better meet this expectation, we propose a system that takes advantage of intermittent connection opportunities while exploiting other networks available in parallel. We build our system over the parallel networks architecture, Para ets, which suggests using parallel heterogeneous networks simultaneously as data and control channels. Our system adopts the Data Bundling System for Intermittent Connections (DBS-IC), previously proposed as a stand-alone architecture for intermittent connectivity, and integrates it with the Para ets architecture. We evaluate our system by fully implementing a Para ets-enabled version of DBS-IC and thoroughly testing it over emulated network conditions representing multiple networks available in parallel. Characteristics of these networks are adopted based on real-life data on 802.11, 3G cell, and satellite networks. Our results show how minimal exploitation of parallel networks largely optimizes both cost and delivery rate.

Many researchers have been focusing on the outcomes and consequences of the rapid increase and proliferation of mobile wireless technologies. If it is not already the case, it will soon be rare for a user to be in a situation where absolutely no network connection exists. In fact, through numerous devices, users will soon expect to be connected in all places at all times. Through the great variety and increase in the capabilities of these devices, it is not a surprise to find a single user with many connection opportunities. As a result, we believe that the next major evolution of wireless mobile networks will be in the exploitation of multiple network connections in parallel. Due to network heterogeneity, the major challenge in such situations is to determine the way that these networks can be utilized to better serve different network applications. In this work, we propose a dynamic channel scheduling mechanism that adapts to the state of the available channels to provide more efficient usage of network connectivity. We do so by observing channel throughput, creating a set of channel usage combinations, and then choosing the most efficient combination. We evaluate an implementation of the proposed mechanism using emulation. Our results show that under realistic conditions our dynamic approach greatly improves cost delay metrics, and the overall user-perceived performance compared to a more static approach.