Wednesday, August 19, 2015

Session 6: Practical, Real-time Centralized Control for CDN-based Live Video Delivery

Authors: Matthew K. Mukerjee (Carnegie Mellon University), David Naylor (Carnegie Mellon University), Junchen Jiang (Carnegie Mellon University), Dongsu Han (KAIST), Srinivasan Seshan (Carnegie Mellon University), Hui Zhang (Carnegie Mellon University, Conviva Inc.)

Presenter: Matthew Mukerjee

Link to the paper
Link to the public review

This presentation outlines the design of a hybrid VDN (Video Distribution Network) which combines the global state view of a centralized controller with the scalability of a distributed control system. 

Content delivery exists as an optimization to maximize service quality which minimizing cost. Due the large demand of Internet video traffic, and its significant traffic volumes, the problem of delivering video content should be optimized on a per request basis. Much of the challenge of centralized optimization in this context is the ability to scale up to thousands of servers with the minimal response needed for live video distribution. The authors present three potential solutions to this problem: a completely centralized approach, a distributed approach and the eventual hybrid approach. 

In the centralized approach, at each level of the distribution tree, the central controller is queried to inform where to next route each request. The controller would run an integer linear program for each request to determine the optimal next hop. The large amount of overhead, and considerable per request computation, all directed toward a single controller resulted in adverse performance in video start time for users. The authors next evaluated a decentralized control mechanism, distributing control using “distance-to-video” tables (similar to distance-vector) distributed at each server. While this approach provided much more scalability, it produced suboptimal decisions due to a lack of a global state. Also provided a much larger variance in performance due to a lack of coordination between requests.

The hybrid approach described in the paper combines the two approaches by using the same distance-to-video tables in the completely decentralized approach but which are updated and maintained by a centralized controller. 


Presenter Questions:

Q1. Why did you not decompose the optimization problem (e.g. NUM)
A. Due to the per request information used for optimization, the authors needed a global view of state which can only be accomplished through centralized control.

Q2. Do you take advantage of caching?
A.     Since the system focuses on live video, caching is less of an issue. For example user interactive videos like Twitch have a delay tolerance which is too low for a cache delay, therefore the system is designed to not utilize caching.

Q3. Did you look at reducing the TTLs? Did you look at lowering the bitrate for improving client performance?
A.     Reducing the TTL to 1 second from 30 seconds produced a nominal improvement, and was not worth the cost of the increase in DNS server traffic.

Q4. Did you consider adapting client bitrates in your study?
A. Client bitrate adaptation is a well studied problem, and was out of the scope of the paper.