Demand response is crucial for the incorporation of renewable energy into the grid. In this paper, we focus on a particularly promising industry for demand response: data centers. We use simulations to show that, not only are data centers large loads, but they can provide as much (or possibly more) flexibility as large-scale storage if given the proper incentives. However, due to the market power most data centers maintain, it is difficult to design programs that are efficient for data center demand response. To that end, we propose that prediction-based pricing is an appealing market design, and show that it outperforms more traditional supply function bidding mechanisms in situations where market power is an issue. However, prediction-based pricing may be inefficient when predictions are inaccurate, and so we provide analytic, worst-case bounds on the impact of prediction error on the efficiency of prediction-based pricing. These bounds hold even when network constraints are considered, and highlight that prediction-based pricing is surprisingly robust to prediction error.

Various link bandwidth adjustment mechanisms are being developed to save network energy. However, their interaction with congestion control can significantly reduce network throughput, and is not well understood. We firstly put forward a framework to study this interaction, and then propose an easily implementable dynamic bandwidth adjustment (DBA) mechanism for the links. In DBA, each link updates its bandwidth according to an integral control law to match its average buffer size with a target buffer size. We prove that DBA reduces link bandwidth without sacrificing throughput---DBA only turns off excess bandwidth---in the presence of congestion control. Preliminary ns2 simulations confirm this result.

Organizers: Mung Chiang (Princeton) and Steven Low (Caltech) Recently, there has been a surge in research activities that utilize the power of recent developments in nonlinear optimization to tackle a wide scope of work in the analysis and design of communication systems, touching every layer of the layered network architecture, and resulting in both intellectual and practical impacts significantly beyond the earlier frameworks. These research activities are driven by both new demands in the areas of communications and networking, and new tools emerging from optimization theory. Such tools include new developments of powerful theories and highly efficient computational algorithms for nonlinear convex optimization, as well as global solution methods and relaxation techniques for nonconvex optimization.Optimization theory can be used to analyze, interpret, or design a communication system, for both forwardengineering and reverse-engineering. Over the last few years, it has been successfully applied to a wide range of communication systems, from the high speed Internet core to wireless networks, from coding and equalization to broadband access, and from information theory to network topology models. Some of the theoretical advances have also been put into practice and started making visible impacts, including new versions of TCP congestion control, power control and scheduling algorithms in wireless networks, and spectrum management in DSL broadband access networks.Under the theme of optimization and control of communication networks, this Hot Topic Session consists of five invited talks covering a wide range of issues, including protocols, pricing, resource allocation, cross layer design, traffic engineering in the Internet, optical transport networks, and wireless networks. Abstracts (in presentation order): Optimization Model of Internet ProtocolsSteven Low (CS and EE, Caltech) Joint work with J. Doyle, L. Li, A. Tang, and J. Wang (Caltech) Layered architecture is one of the most fundamental and influential structures of network design. Can we integrate the various protocol layers into a single coherent theory by regarding them as carrying out an asynchronous distributed primal-dual computation over the network to implicitly solve a global optimization problem? Different layers iterate on different subsets of the decision variables using local information to achieve individual optimalities, but taken together, these local algorithms attempt to achieve a global objective. Such a theory will expose the interconnection between protocol layers and can be used to study rigorously the performance tradeoff in protocol layering as different ways to distribute a centralized computation. In this talk, we describe some preliminary work towards this goal and discuss some of the difficulties of this approach. Network Utility Maximization with Nonconcave, Coupled, and Reliability-based UtilitiesMung Chiang (EE, Princeton University) Joint work with J. W. Lee, R. Calderbank, D. Palomar (Princeton) and M. Fazel (Caltech) Net...

Organizers: Mung Chiang (Princeton) and Steven Low (Caltech)Recently, there has been a surge in research activities that utilize the power of recent developments in nonlinear optimization to tackle a wide scope of work in the analysis and design of communication systems, touching every layer of the layered network architecture, and resulting in both intellectual and practical impacts significantly beyond the earlier frameworks. These research activities are driven by both new demands in the areas of communications and networking, and new tools emerging from optimization theory. Such tools include new developments of powerful theories and highly efficient computational algorithms for nonlinear convex optimization, as well as global solution methods and relaxation techniques for nonconvex optimization.