A Distributed Economics-based Infrastructure for Utility Computing

Existing attempts at utility computing revolve around two approaches. The first consists of proprietary solutions involving renting time on dedicated utility computing machines. The second requires the use of heavy, monolithic applications that are difficult to deploy, maintain, and use. We propose a distributed, community-oriented approach to utility computing. Our approach provides an infrastructure built on Web Services in which modular components are combined to create a seemingly simple, yet powerful system. The community-oriented nature generates an economic environment which results in fair transactions between consumers and providers of computing cycles while simultaneously encouraging improvements in the infrastructure of the computational grid itself.Comment: 8 pages, 1 figur

Cross-layer optimization in TCP/IP networks

TCP-AQM can be interpreted as distributed primal-dual algorithms to maximize aggregate utility over source rates. We show that an equilibrium of TCP/IP, if exists, maximizes aggregate utility over both source rates and routes, provided congestion prices are used as link costs. An equilibrium exists if and only if this utility maximization problem and its Lagrangian dual have no duality gap. In this case, TCP/IP incurs no penalty in not splitting traffic across multiple paths. Such an equilibrium, however, can be unstable. It can be stabilized by adding a static component to link cost, but at the expense of a reduced utility in equilibrium. If link capacities are optimally provisioned, however, pure static routing, which is necessarily stable, is sufficient to maximize utility. Moreover single-path routing again achieves the same utility as multipath routing at optimality

Noncooperative equilibrium solutions for spectrum access in distributed cognitive radio networks

This paper considers the problem of channel selection and dynamic spectrum access in distributed cognitive radio networks. The ability of a cognitive radio to adaptively switch between channels offers tremendous scope to optimize performance. In this paper, the dynamic spectrum access in a distributed network is modeled as a noncooperative game and the equilibrium solutions are obtained through a bimatrix game. The cost term of the utility function and the several possible definitions of “price” and how they characterize the equilibrium solutions provides a new perspective on the analysis. In distributed cognitive radio networks, the secondary users are vulnerable to several unexpected events such as primary user arrival or a deep fade or sudden increase in interference which could potentially disrupt or disconnect the transmission link. In such cases, any strategic decision or information that could lead to uninterrupted channel access and facilitate maintaining links could be modeled as a Stackelberg game. Performance characteristics for both the leader and follower nodes for the defined utility functions are given.This paper considers the problem of channel selection and dynamic spectrum access in distributed cognitive radio networks. The ability of a cognitive radio to adaptively switch between channels offers tremendous scope to optimize performance. In this paper, the dynamic spectrum access in a distributed network is modeled as a noncooperative game and the equilibrium solutions are obtained through a bimatrix game. The cost term of the utility function and the several possible definitions of "price" and how they characterize the equilibrium solutions provides a new perspective on the analysis. In distributed cognitive radio networks, the secondary users are vulnerable to several unexpected events such as primary user arrival or a deep fade or sudden increase in interference which could potentially disrupt or disconnect the transmission link. In such cases, any strategic decision or information that could lead to uninterrupted channel access and facilitate maintaining links could be modeled as a Stackelberg game. Performance characteristics for both the leader and follower nodes for the defined utility functions are give

Distributed Large Scale Network Utility Maximization

Recent work by Zymnis et al. proposes an efficient primal-dual interior-point method, using a truncated Newton method, for solving the network utility maximization (NUM) problem. This method has shown superior performance relative to the traditional dual-decomposition approach. Other recent work by Bickson et al. shows how to compute efficiently and distributively the Newton step, which is the main computational bottleneck of the Newton method, utilizing the Gaussian belief propagation algorithm. In the current work, we combine both approaches to create an efficient distributed algorithm for solving the NUM problem. Unlike the work of Zymnis, which uses a centralized approach, our new algorithm is easily distributed. Using an empirical evaluation we show that our new method outperforms previous approaches, including the truncated Newton method and dual-decomposition methods. As an additional contribution, this is the first work that evaluates the performance of the Gaussian belief propagation algorithm vs. the preconditioned conjugate gradient method, for a large scale problem.Comment: In the International Symposium on Information Theory (ISIT) 200

The Poverty of Growth with Interdependent Utility Functions

We argue that with interdependent utility functions growth can lead to a decline in total welfare of a society if the gains from growth are sufficiently unequally distributed in the presence of negative externalities, i.e., envy.Interdependent utility functions ; growth ; inequality

Retail Rate Impacts of Distributed Solar: Focus on New England

The Lawrence Berkeley National Laboratory (LBNL) recently issued a study entitled “Putting the Potential Rate Impacts of Distributed Solar into Context,” authored by Galen Barbose. The LBNL study estimates the potential rate impact of distributed solar on national average retail electricity prices, and importantly, compares that impact to the potential impact of other rate drivers such as natural gas prices, renewable portfolio standards, and utility capital expenditures.1 This brief applies a similar style analysis as used by LBNL to regional and state level data to estimate more granular impacts for New England. We estimate rate impacts for various penetration rates of net metered distributed solar and compare them to the potential rate impacts of future natural gas prices, energy efficiency gains, RPS costs, RGGI costs, and utility capital expenditures. Like LBNL, we attempt to isolate the impact of these rate drivers as well as represent uncertainty around future policy choices, commodity costs, and technology costs