Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

Decentralized Constraint Satisfaction

We show that several important resource allocation problems in wireless networks fit within the common framework of Constraint Satisfaction Problems (CSPs). Inspired by the requirements of these applications, where variables are located at distinct network devices that may not be able to communicate but may interfere, we define natural criteria that a CSP solver must possess in order to be practical. We term these algorithms decentralized CSP solvers. The best known CSP solvers were designed for centralized problems and do not meet these criteria. We introduce a stochastic decentralized CSP solver and prove that it will find a solution in almost surely finite time, should one exist, also showing it has many practically desirable properties. We benchmark the algorithm's performance on a well-studied class of CSPs, random k-SAT, illustrating that the time the algorithm takes to find a satisfying assignment is competitive with stochastic centralized solvers on problems with order a thousand variables despite its decentralized nature. We demonstrate the solver's practical utility for the problems that motivated its introduction by using it to find a non-interfering channel allocation for a network formed from data from downtown Manhattan

Power-controlled cognitive radio spectrum allocation with chemical reaction optimization

Cognitive radio is a promising technology for increasing the system capacity by using the radio spectrum more effectively. It has been widely studied recently and one important problem in this new paradigm is the allocation of radio spectrum to secondary users effectively in the presence of primary users. We call it the cognitive radio spectrum allocation problem (CRSAP) in this paper. In the conventional problem formulation, a secondary user can be either on or off and its interference range becomes maximum or zero, respectively. We first develop a solution to CRSAP based on the newly proposed chemical reaction-inspired metaheuristic called Chemical Reaction Optimization (CRO). We study different utility functions, accounting for utilization and fairness, with the consideration of the hardware constraint, and compare the performance of our proposed CRO-based algorithm with existing ones. Simulation results show that the CRO-based algorithm always outperforms the others dramatically. Next, by allowing adjustable transmission power, we propose power-controlled CRSAP (PC-CRSAP), a new formulation to the problem with the consideration of spatial diversity. We design a two-phase algorithm to solve PC-CRSAP, and again simulation results show excellent performance. © 2002-2012 IEEE.published_or_final_versio

Computer Science Research Institute 2004 annual report of activities.

This report summarizes the activities of the Computer Science Research Institute (CSRI) at Sandia National Laboratories during the period January 1, 2004 to December 31, 2004. During this period the CSRI hosted 166 visitors representing 81 universities, companies and laboratories. Of these 65 were summer students or faculty. The CSRI partially sponsored 2 workshops and also organized and was the primary host for 4 workshops. These 4 CSRI sponsored workshops had 140 participants--74 from universities, companies and laboratories, and 66 from Sandia. Finally, the CSRI sponsored 14 long-term collaborative research projects and 5 Sabbaticals

Mapping lessons from ants to free flight: An ant-based weather aviodance algorithm in free flight airspace

The continuing growth of air traffic worldwide motivates the need for new approaches to air traffic management that are more flexible both in terms of traffic volume and weather. Free Flight is one such approach seriously considered by the aviation community. However the benefits of Free Flight are severely curtailed in the convective weather season when weather is highly active, leading aircrafts to deviate from their optimal trajectories. This paper investigates the use of ant colony optimization in generating optimal weather avoidance trajectories in Free Flight airspace. The problem is motivated by the need to take full advantage of the airspace capacity in a Free Flight environment, while maintaining safe separation between aircrafts and hazardous weather. The experiments described herein were run on a high fidelity Free Flight air traffic simulation system which allows for a variety of constraints on the computed routes and accurate measurement of environments dynamics. This permits us to estimate the desired behavior of an aircraft, including avoidance of changing hazardous weather patterns, turn and curvature constraints, and the horizontal separation standard and required time of arrival at a pre determined point, and to analyze the performance of our algorithm in various weather scenarios. The proposed Ant Colony Optimization based weather avoidance algorithm was able to find optimum weather free routes every time if they exist. In case of highly complex scenarios the algorithm comes out with the route which requires the aircraft to fly through the weather cells with least disturbances. All the solutions generated were within flight parameters and upon integration with the flight management system of the aircraft in a Free Flight air traffic simulator, successfully negotiated the bad weather