Statistical Machine Learning in Networking
Led by: Di Niu
Self-Diagnostic Peer-Assisted Video Streaming through a Learning Framework
Quality control and resource optimization are challenging problems in peer-assisted video streaming systems, due to their large scales and unreliable peer behavior. Such systems are also prone to per- formance degradation in the event of drastic demand changes, such as flash crowds and large-scale simultaneous peer departures. In this paper, we demonstrate the deficiency of state-of-the-art video streaming systems by analyzing real-world traces from UUSee, a popular commercial P2P media streaming system based in China, during the 2008 Beijing Olympics. We show how simple machine learning techniques combined with periodic collection of statistics can be used for automated monitoring and diagnosis of peer-assisted video streaming systems. With such a framework, it is possible to es- timate performance given certain resource usage patterns, making resource utilization more efficient. It also enables the prediction of large-scale performance degradation due to irregular demand pat- terns. The effectiveness of our proposed framework is validated with extensive trace-driven evaluations.
This work has been published in ACM Multimedia 2010 (PDF).
Demand Forecast and Performance Prediction in Peer-Assisted On-Demand Streaming Systems
Peer-assisted on-demand video streaming services are extremely large-scale distributed systems on the Internet. Automated demand forecast and performance prediction, if implemented, can help with capacity planning and quality control so that sufficient server bandwidth can always be supplied to each video channel without incurring wastage. In this paper, we use time-series analysis techniques to automatically predict the online population, the peer upload and the server bandwidth demand in each video channel, based on the learning of both human factors and system dynamics from online measurements. The proposed mechanisms are evaluated on a large dataset collected from a commercial Internet video-on-demand system.
This work has been published in IEEE INFOCOM Mini-Conference 2011 (PDF).
Understanding Demand Volatility in Large VoD Systems
Bandwidth usage in large-scale Video on Demand (VoD) systems varies rapidly over time, due to unpredictable dynamics in user de- mand and network conditions. Such bandwidth volatility makes it hard to provision the exact amount of server resources that matches the demand in each video channel, posing significant challenges to achieving quality assurance and efficient resource allocation at the same time. In this paper, we seek to statistically model time-varying traffic volatility in VoD servers, leveraging heteroscedastic models first used to interpret economic time series, with the goal of forecast- ing not only traffic patterns but also traffic volatility. We present the application of volatility forecast to efficient resource allocation that provides probabilistic service level guarantees to user groups. We also discuss volatility reduction from diversification, and its impli- cations to new strategies for cost-effective server management. Our study is based on monitoring the workload of a large-scale commer- cial VoD system widely deployed on the Internet.
This work has been published in NOSSDAV 2011 (PDF).
