We study auction-theoretic scheduling in cellular networks using the idea of a mean field equilibrium (MFE). Here, agents model their opponents through an assumed distribution over their action spaces, and play the best response action against this distribution. We say that the system is at MFE if this best response action turns out to be a sample drawn from the assumed distribution. In our setting, the agents are smart phone apps that generate service requests, have costs associated with waiting, and bid against each other for service from base stations. The users of the apps spend an geometrically distributed amount of time on each app, and then move on to another. We show that in a system in which we conduct a second-price auction at each base station and schedule the winner at each time, there exists an MFE that will schedule the user with highest value at each time. We further show that the scheme can be interpreted as a weighted longest queue first type policy. The result suggests that auctions can implicitly attain the same stabilizing effects as queue-length based scheduling. We will also present some results on the convergence between a system with a finite number of agents to a mean field case as the number of agents become large. Finally, we will spend some time discussing our other recent work in the space of game theory and content distribution networks.
Srinivas Shakkottai received a PhD (2007) in Electrical Engineering, from the University of Illinois at Urbana-Champaign. He was a post-doctoral associate in Management Science and Engineering at Stanford University in 2007, and is currently an assistant professor at the Dept. of ECE at Texas A&M University. His research interests include content distribution systems, wireless ad-hoc networks, Internet economics and game theory, congestion control, and the measurement and analysis of Internet data. Srinivas is the recipient of the Defense Threat Reduction Agency Young Investigator Award (US Dept. of Defense, 2009) and the NSF CAREER Award (2012), as well as research awards from Cisco (2008) and Google (2010).