Srikant seeks to exploit variability in social, cloud networks

In a computer network, file sizes vary from Internet pages that are a few kilobytes to movie files that are many gigabytes. Likewise in a social network, some users have several dozen friends or followers, while others have several thousand.

While the traditional view is that wide variability in networks poses a problem – video files hog bandwidth and delay even small operations -- R. Srikant believes that it is possible to exploit those variations for positive outcomes. Srikant is the Fredric G. and Elizabeth H. Nearing Endowed Professor of Electrical and Computer Engineering and a researcher at the Coordinated Science Laboratory. He is among researchers at six universities that will explore how to design systems to optimize heavy-tailed distribution in networks. The project, “Multivariate Heavy -Tail Phenomena: Modeling and Diagnostics,” will be funded by the National Science Foundation for five years at $6.25 million.

“We hope to create a model that would enable you to understand or make inferences about behavior based on an observation,” Srikant explained.

In a computer network, the idea of exploiting variability is already reasonably well understood. However, it’s less clear how the concept would work in social networks and cloud computing systems – the focus of this project.

In a social network such as Facebook, some people have a large number of friends, while others have just a few. Marketers who are looking to maximize their advertising dollars may want to place an ad on the Facebook page of a user with a large network, since they will likely influence more people than others with fewer friends. Srikant is working to create a model that would enable organizations to make inferences about a power user’s ability to influence her connections without actually knowing who the user’s friends are.

In cloud computing, variability is key in allocating resources. As in the case of the Internet, cloud providers want to ensure that small jobs are not delayed by getting behind very large jobs in the queue. Srikant’s group will develop a methodology for understanding how to schedule and route jobs, taking into consideration the wide variety of the number and size of jobs at any given time.

In addition to this grant, Srikant will be studying related problems in cloud computing through another NSF grant, “Resource Allocation in Clouds: A Stochastic Modeling and Control Perspective.” The grant, which will be a total of $450,000 for three years, will be split with Prof. Lei Ying, Srikant’s former PhD student who was a research assistant at CSL, at Arizona State University.

The research could have applications outside of social networks and cloud computing. The military could potentially use it to understand or infer relationships based on behaviors. It may also help companies build better business models. A company like Family Video, for example, could make better predictions about what type of movies to have in stock, likely the most popular blockbusters, and what type of movies to offer online, such as indie movies that have a smaller but devoted audience base.

“This work could really be applied to any number of networks,” Srikant said.  “It will allow a more targeted approach to business, defense and other areas.”
__________________________

Contact: R. Srikant, Department of Electrical and Computer Engineering, 217/333-2457.

Writer: Kim Gudeman, Coordinated Science Laboratory, 217/493-1618.

If you have any questions about the College of Engineering, or other story ideas, contact Rick Kubetz, editor, Engineering Communications Office, University of Illinois at Urbana-Champaign, 217/244-7716.