NUM-Based Rate Allocation for Streaming Traffic via Sequential Convex Programming

In recent years, there has been an increasing demand for ubiquitous streaming like applications in data networks. In this paper, we concentrate on NUM-based rate allocation for streaming applications with the so-called S-curve utility functions. Due to non-concavity of such utility functions, the underlying NUM problem would be non-convex for which dual methods might become quite useless. To tackle the non-convex problem, using elementary techniques we make the utility of the network concave, however this results in reverse-convex constraints which make the problem non-convex. To deal with such a transformed NUM, we leverage Sequential Convex Programming (SCP) approach to approximate the non-convex problem by a series of convex ones. Based on this approach, we propose a distributed rate allocation algorithm and demonstrate that under mild conditions, it converges to a locally optimal solution of the original NUM. Numerical results validate the effectiveness, in terms of tractable convergence of the proposed rate allocation algorithm.Comment: 6 pages, conference submissio

Network Utility Maximization With Nonconcave Utilities Using Sum-of-Squares Method

The Network Utility Maximization problem has recently been used extensively to analyze and design distributed rate allocation in networks such as the Internet. A major limitation in the state-of-the-art is that user utility functions are assumed to be strictly concave functions, modeling elastic flows. Many applications require inelastic flow models where nonconcave utility functions need to be maximized. It has been an open problem to find the globally optimal rate allocation that solves nonconcave network utility maximization, which is a difficult nonconvex optimization problem. We provide a centralized algorithm for off-line analysis and establishment of a performance benchmark for nonconcave utility maximization. Based on the semialgebraic approach to polynomial optimization, we employ convex sum-of-squares relaxations solved by a sequence of semidefinite programs, to obtain increasingly tighter upper bounds on total achievable utility for polynomial utilities. Surprisingly, in all our experiments, a very low order and often a minimal order relaxation yields not just a bound on attainable network utility, but the globally maximized network utility. When the bound is exact, which can be proved using a sufficient test, we can also recover a globally optimal rate allocation. In addition to polynomial utilities, sigmoidal utilities can be transformed into polynomials and are handled. Furthermore, using two alternative representation theorems for positive polynomials, we present price interpretations in economics terms for these relaxations, extending the classical interpretation of independent congestion pricing on each link to pricing for the simultaneous usage of multiple links

Robust Allocation of Reserve Policies for a Multiple-Cell Based Power System

This paper applies a robust optimization technique for coordinating reserve allocations in multiple-cell based power systems. The linear decision rules (LDR)-based policies were implemented to achieve the reserve robustness, and consist of a nominal power schedule with a series of linear modifications. The LDR method can effectively adapt the participation factors of reserve providers to respond to system imbalance signals. The policies considered the covariance of historic system imbalance signals to reduce the overall reserve cost. When applying this method to the cell-based power system for a certain horizon, the influence of different time resolutions on policy-making is also investigated, which presents guidance for its practical application. The main results illustrate that: (a) the LDR-based method shows better performance, by producing smaller reserve costs compared to the costs given by a reference method; and (b) the cost index decreases with increased time intervals, however, longer intervals might result in insufficient reserves, due to low time resolution. On the other hand, shorter time intervals require heavy computational time. Thus, it is important to choose a proper time interval in real time operation to make a trade off

Measuring the impact of market coupling on the Italian electricity market using ELFO++

This paper evaluates the impact on the Italian electricity market of replacing the current explicit auction mechanism with market coupling. Maximizing the use of the cross-border interconnection capacity, market coupling increases the level of market integration and facilitates the access to low-cost generation by consumers located in high-cost generation countries. Thus, it is expected that a high-priced area such as Italy could greatly benefit from the introduction of this mechanism. In this paper, the welfare benefits are estimated under alternative market scenarios for 2012, employing the optimal dispatch model ELFO++. The results of the simulations suggest that the improvement in social surplus is likely to be significant, especially when market fundamentals are tight.Market coupling; market integration; Italian day-ahead electricity market.

Trade, FDI, and Congestion - The small and very open Economy

Typically, a small and open economy trades goods at given world prices. Here, we present a model of a very open small economy, where capital and labor are internationally mobile, too. When investing into infrastructure, the economy’s government attracts not only mobile capital but mobile labor, also. These capital and labor inflows into the economy reinforce each other. They contribute to rising welfare for land owning indigenous households. But all potential benefits for land renting immigrant households are capitalized into higher land rents. - The paper is also an attempt to give an account of the recent economic boom in Ireland.Foreign Direct Investment, Small Open Economy, Open City