One who walks in another's tracks leaves no footprints.
I am a Ph.D. student in the Department of Computer Science at Columbia University where I work in the Internet Real-Time Laboratory. My advisor is Prof. Henning Schulzrinne. Prior to Columbia, I was a researcher at Fraunhofer FOKUS in Berlin and later a co-founder and chief software architect at iptel.org, one of its spin-off companies (acquired by Tekelec, now part of Oracle). I received my M.S. in Computer Science from the Czech Technical University in Prague in 2003.
Outside of work, I enjoy outdoor adventures, scuba diving, running, and cross-country skiing. I am also an avid reader with a soft spot for classic science fiction and Karel Capek. Some of my favorite films have been made by Alexander Payne, Milos Forman, and Wes Anderson.
My primary research area is distributed network applications, with a focus on the Internet of Things (IoT), cyber-physical systems, and multi-media network services. My goal is to design and develop better programming abstractions for distributed and reliable IoT applications that are deployable across a heterogeneous network of devices. I am specificially interested in exploring the "computation follows data" approach to IoT application design, where the application gets deployed close to the origin of the data, as opposed to the more traditional "data follows computation" model leveraged by most existing cloud-based IoT services.
I also mentor and supervise student projects (undegraduate and graduate level). Our paper on the design of a wireless networking lab won the Best Educational Paper Award at the Second GENI Educational and Research Workshop in 2013. I helped design the homework assigments for the Advanced Programming class at Columbia and supervised numerous student team projects.
With over 20 million units sold since 2015, Amazon Echo, the Alexa-enabled smart speaker developed by Amazon, is probably one of the most widely deployed Internet of Things consumer devices. Despite the very large installed base, surprisingly little is known about the device's network behavior. We modify a first generation Echo device, decrypt its communication with Amazon cloud, and analyze the device pairing, Alexa Voice Service, and drop-in calling protocols. We also describe our methodology and the experimental setup. We find a minor shortcoming in the device pairing protocol and learn that drop-in calls are end-to-end encrypted and based on modern open standards. Overall, we find the Echo to be a well-designed device from the network communication perspective.
When the electrical grid in a region suffers a major outage, e.g., after a catastrophic cyber attack, a "black start" may be required, where the grid is slowly restarted, carefully and incrementally adding generating capacity and demand. To ensure safe and effective black start, the grid control center has to be able to communicate with field personnel and with supervisory control and data acquisition (SCADA) systems. Voice and text communication are particularly critical. As part of the Defense Advanced Research Projects Agency (DARPA) Rapid Attack Detection, Isolation, and Characterization Systems (RADICS) program, we designed, tested and evaluated a self-configuring mesh network prototype called the Phoenix Secure Emergency Network (PhoenixSEN). PhoenixSEN provides a secure drop-in replacement for grid's primary communication networks during black start recovery. The network combines existing and new technologies, can work with a variety of link-layer protocols, emphasizes manageability and auto-configuration, and provides services and applications for coordination of people and devices including voice, text, and SCADA communication. We discuss the architecture of PhoenixSEN and evaluate a prototype on realistic grid infrastructure through a series of DARPA-led exercises.
The COVID-19 pandemic and related restrictions forced many to work, learn, and socialize from home over the internet. There appears to be consensus that internet infrastructure in the developed world handled the resulting traffic surge well. In this paper, we study network measurement data collected by the Federal Communications Commission's Measuring Broadband America program before and during the pandemic in the United States (US). We analyze the data to understand the impact of lockdown orders on the performance of fixed broadband internet infrastructure across the US, and also attempt to correlate internet usage patterns with the changing behavior of users during lockdown. We found the key metrics such as change in data usage to be generally consistent with the literature. Through additional analysis, we found differences between metro and rural areas, changes in weekday, weekend, and hourly internet usage patterns, and indications of network congestion for some users.
The serverless and functions as a service (FaaS) paradigms are currently trending among cloud providers and are now increasingly being applied to the network edge, and to the Internet of Things (IoT) devices. The benefits include reduced latency for communication, less network traffic and increased privacy for data processing. However, there are challenges as IoT devices have limited resources for running multiple simultaneous containerized functions, and also FaaS does not typically support long-running functions. Our implementation utilizes Docker and CRIU for checkpointing and suspending long-running blocking functions. The results show that checkpointing is slightly slower than regular Docker pause, but it saves memory and allows for more long-running functions to be run on an IoT device. Furthermore, the resulting checkpoint files are small, hence they are suitable for live migration and backing up stateful functions, therefore improving availability and reliability of the system.