The Veterans Emergency Housing Program

Dynamic spectrum access (DSA) schemes allow the users to share spectrum resources by taking advantage of the variations in spectrum demand over time and space. Carrying out dynamic spectrum allocation centrally, however, can be a complex task. For this reason, distributed schemes in which users can access the available channels independently may be preferable to centralized DSA schemes. Cognitive radio systems, which enable user terminals to sense their environment and form their action accordingly, are particularly well-suited for distributed systems. On the other hand, the freedom in distributed schemes gives the users the option to act selfishly, which has decisive effects on system performance. In this paper we consider a distributed multichannel wireless random access system where users selfishly access the channels in the system. We analyze the behavior of the selfish users by modeling the system as a non-cooperative game and we identify all stable operating points (Nash equilibria) of this game. We then compare the performance of this system with a number of cooperative distributed DSA schemes in terms of user utilities. Our results show that the performance of the selfish multichannel random access system can be comparable to cooperative schemes.QC 20111208. © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. QC 20111207</p

Adaptation of System Dynamics Model Execution Algorithms for Cloud-based Environment

This paper presents a process of adaptation of system dynamics models execution algorithms to cloud-based environment. System dynamics is an aspect of systems theory as a method to understand the dynamic behaviour of complex systems. Existing modeling algorithms used in popular modeling solutions are either not available for free use or have several disadvantages which prevent them from being used in distributed cloud environment. Adaptation of execution algorithms aimed not only to adapt execution process to distributed parallel environments with higher reliability and wider range of possible applications, but also to improve system dynamics model execution performance. For example, existing algorithms of model execution which are not ready for distributed environments will fail to complete modeling task in case of hardware failure, and optimized ones are able to smoothly transfer execution process from one node to another with minimal impact on overall model execution progress. Such capabilities help to save many resources and, especially, time on execution re-runs. In this paper described algorithms and approaches designed for sdCloud solution which are focused on transferring execution of system dynamics models into distributed cloud-based environment and shown extra benefits brought to modeling process by shift to the cloud

Dynamic, Stochastic, Computational, and Scalable Technologies for Smart Grids

The smart electric power grid will evolve into a very complex adaptive system under semi-autonomous distributed control. its spatial and temporal complexity, non-convexity, non-linearity, non-stationarity, variability and uncertainties exceed the characteristics found in today\u27s traditional power system. the distributed integration of intermittent sources of energy and plug-in electric vehicles to a smart grid further adds complexity and challenges to its modeling, control and optimization. Innovative technologies are needed to handle the growing complexity of the smart grid and stochastic bidirectional optimal power flows, to maximize the penetration of renewable energy, and to provide maximum utilization of available energy storage, especially plugin electric vehicles. Smart grids will need to be monitored continuously to maintain stability, reliability and efficiency under normal and abnormal operating conditions and disturbances. a combination of capabilities for system state prediction, dynamic stochastic power flow, system optimization, and solution checking will be necessary. the optimization and control systems for a smart-grid environment will require a computational systems thinking machine to handle the uncertainties and variability that exist. the importance and contributions of the computational intelligence field for developing the dynamic, stochastic, computational, and scalable technologies needed for sensemaking, situational awareness, control and optimization in smart grids are presented in this paper. © 2011 IEEE

Precis of neuroconstructivism: how the brain constructs cognition

Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment

MAS2DES-Onto: Ontology for MAS-based Digital Ecosystems

Multi-Agent Systems (MASs) have received much attention in recent years because of their advantages on modeling complex distributed systems, such Digital Ecosystems (DESs). Many existing modeling languages that support the design of such systems are based on ontologies to assist the representation of agents knowledge. However, in the context of DESs, there is still a need for more general conceptual models to represent the specific characteristics of DESs in terms of win-win interaction, engagement, equilibrium, and self-organization. Then, concepts such behavior, roles, rules, and environment are needed. This paper describes an ontologybased approach by proposing MAS2DES-Onto, as the conceptual model, which considers the essential static and dynamic aspects of MASs by a clear representation of their concepts and relationships to support the design and development of DESs. To validate and conduct experimental tests, we integrate MAS2DES-Onto into a framework to automatically generate MAS-based DESs. Results show the efficiency and effectiveness of our approach.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Organization of Multi-Agent Systems: An Overview

In complex, open, and heterogeneous environments, agents must be able to reorganize towards the most appropriate organizations to adapt unpredictable environment changes within Multi-Agent Systems (MAS). Types of reorganization can be seen from two different levels. The individual agents level (micro-level) in which an agent changes its behaviors and interactions with other agents to adapt its local environment. And the organizational level (macro-level) in which the whole system changes it structure by adding or removing agents. This chapter is dedicated to overview different aspects of what is called MAS Organization including its motivations, paradigms, models, and techniques adopted for statically or dynamically organizing agents in MAS.Comment: 12 page

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

Dynamic Model-based Management of Service-Oriented Infrastructure.

Models are an effective tool for systems and software design. They allow software architects to abstract from the non-relevant details. Those qualities are also useful for the technical management of networks, systems and software, such as those that compose service oriented architectures. Models can provide a set of well-defined abstractions over the distributed heterogeneous service infrastructure that enable its automated management. We propose to use the managed system as a source of dynamically generated runtime models, and decompose management processes into a composition of model transformations. We have created an autonomic service deployment and configuration architecture that obtains, analyzes, and transforms system models to apply the required actions, while being oblivious to the low-level details. An instrumentation layer automatically builds these models and interprets the planned management actions to the system. We illustrate these concepts with a distributed service update operation