Browsing by Author "Zhao, Ben Y."

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AirLab: Distributed Infrastructure for Wireless Measurements
(USENIX, 2010) Kone, Vinod; Zheleva, Mariya; Wittie, Mike P.; Zhang, Zengbin; Zhao, Xiaohan; Zhao, Ben Y.; Belding, Elizabeth M.; Zheng, Haitao; Almeroth, Kevin C.
The importance of experimental research in the field of wireless networks is well understood. So far researchers have either built their own testbeds or accessed third-party controlled testbeds (http://orbit-lab.org) or used publicly available traces (http://crawdad.cs.dartmouth.edu) for evaluation. While immensely useful, all these approaches have their drawbacks. While building own test beds requires cost and effort, third-party controlled test beds do not replicate real network deployments. On the other hand, the publicly available traces are often collected using different software and hardware platforms, making it very difficult to compare results across traces. As a result, observations are often inconsistent across different networks, leading researchers to draw potentially conflicting conclusions across their own studies. To facilitate meaningful analysis of wireless networks and protocols, we need a way to collect measurement traces across a wide variety of network deployments, all using a consistent set of measurement metrics. Widespread multi-faceted data collection will provide multiple viewpoints of the same network, enabling deeper understanding of both self and exterior interference properties, spectrum usage, network usage, and a wide variety of other factors. Furthermore, data collected in this manner across a variety of heterogeneous network types, such as university, corporate, and home environments, will facilitate cross-comparison of observed network phenomena within each of these settings. To address the critical need for comparable and consistent wireless traces, we propose AirLab, a publicly accessible distributed infrastructure for wireless measurements
Exploiting Locality of Interest in Online Social Networks
(ACM CoNEXT, 2010) Wittie, Mike P.; Pejovic, Veljko; Deek, Lara B.; Almeroth, Kevin C.; Zhao, Ben Y.
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.