My research interest lies in wireless networks, network protocols, mobile computing. I am currently working on
- TCP download performance in dense WiFi settings: Analysis and Solution: How does a dense WiFi network perform, specifically for the common case of TCP download? While the empirical answer to this question is ‘poor’, analysis and experimentation
in prior work has indicated that TCP clocks itself quite well,
avoiding contention-driven WiFi overload in dense settings. This
work focuses on measurements from a real-life use of WiFi in
a dense scenario: a classroom where several students use the
network to download quizzes and instruction material. We find
that the TCP download performance is poor, contrary to that
suggested by prior work. Through careful analysis, we explain
the complex interaction of various phenomena which leads to this
poor performance. Specifically, we observe that a small amount
of upload traffic generated when downloading data upsets the
TCP clocking, and increases contention on the channel. Further,
contention losses lead to a vicious cycle of poor interaction
with autorate adaptation and TCP’s timeout mechanism. To
reduce channel contention and improve performance, we propose
a modification to the AP scheduling policy to improve the
performance of large TCP downloads. Our solution, WiFiRR,
picks only a subset of clients to be served by the AP during
any instant, and varies this set of “active” clients periodically
in a round-robin fashion over all clients to ensure that no
client starves. We have done extensive evaluation of WiFiRR
in simulation and in real settings. By reducing the number of
contending nodes at any point of time, WiFiRR improves the
download time of large TCP flows upto 3.5× of our classroom
scenario. We also compare WiFiRR with state-of-the-art prior
work WiFox, WiFiRR improves download time by 2.25× over
WiFox.
- Witals: AP-centric health diagnosis: In recent years, WiFi has grown in capacity as well as deployment demand. WiFi system administrators (sysads) want a simple answer to the question “Is my WiFi network healthy?”, and a possible follow-up “What is wrong with it?”, if it is reported as “unhealthy”. But we are far from having such an interface today.
It is this gap that this work attempts to fill. We present Witals, a system for WiFi
performance diagnosis. Our first cpme up with a causal diagnosis graph, designed to be comprehensive in identifying the underlying cause(s) of any WiFi performance problem. Next, we identify a set of
metrics corresponding to nodes in this causal graph. These metrics are measured in real-time by an
operational AP, and help in quantifying the effect of each cause. We design a diagnosis algorithm based on the witals metrics and the causal graph, which ultimately presents a sanitized view of WiFi network health to the sysad. We have implemented a prototype of Witals on an enterprise grade 802.11n AP
platform. Using a variety of controlled as well as real-life measurements, we show that our diagnosis
framework follows ground truth accurately.