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Localised Routing Algorithms with Quality of Service Constraints. Development and performance evaluation by simulation of new localised Quality of Service routing algorithms for communication networks using residual bandwidth and mean end-to-end delay as metrics.
School of Computing, Informatics and MediaLocalised QoS routing is a relatively new, alternative and viable approach to solve the problems of traditional QoS routing algorithms which use global state information resulting in the imposition of a large communication overhead and route flapping. They make use of a localised view of the network QoS state in source nodes to select paths and route flows to destination nodes. Proportional Sticky Routing (PSR) and Credit Based Routing (CBR) have been proposed as localised QoS routing schemes and these can offer comparable performances. However, since network state information for a specific path is only updated when the path is used, PSR and CBR operate with decision criteria that are often stale for paths that are used infrequently.
The aim of this thesis is to focus on localised QoS routing and contribute to enhancing the scalability of QoS routing algorithms. In this thesis we have developed three new localised QoS routing schemes which are called Score Based QoS Routing (SBR), Bandwidth Based QoS Routing (BBR) and Delay Based Routing (DBR). In some of these schemes, the path setup procedure is distributed and uses the current network state to make decisions thus avoiding problems of staleness. The methods also avoid any complicated calculations. Both SBR and BBR use bandwidth as the QoS metric and mean delay is used as the QoS metric in DBR. Extensive simulations are applied to compare the performance of our proposed algorithms with CBR and the global Dijkstra驴s algorithm for different update intervals of link state, different network topologies and using different flow arrival distributions under a wide range of traffic loads. It is demonstrated by simulation that the three proposed algorithms offer a superior performance under comparable conditions to the other localised and global algorithms
Air Traffic Management Abbreviation Compendium
As in all fields of work, an unmanageable number of abbreviations are used today in aviation for terms, definitions, commands, standards and technical descriptions. This applies in general to the areas of aeronautical communication, navigation and surveillance, cockpit and air traffic control working positions, passenger and cargo transport, and all other areas of flight planning, organization and guidance. In addition, many abbreviations are used more than once or have different meanings in different languages.
In order to obtain an overview of the most common abbreviations used in air traffic management, organizations like EUROCONTROL, FAA, DWD and DLR have published lists of abbreviations in the past, which have also been enclosed in this document. In addition, abbreviations from some larger international projects related to aviation have been included to provide users with a directory as complete as possible. This means that the second edition of the Air Traffic Management Abbreviation Compendium includes now around 16,500 abbreviations and acronyms from the field of aviation
Reinforcement Learning
Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field
Anales del XIII Congreso Argentino de Ciencias de la Computaci贸n (CACIC)
Contenido:
Arquitecturas de computadoras
Sistemas embebidos
Arquitecturas orientadas a servicios (SOA)
Redes de comunicaciones
Redes heterog茅neas
Redes de Avanzada
Redes inal谩mbricas
Redes m贸viles
Redes activas
Administraci贸n y monitoreo de redes y servicios
Calidad de Servicio (QoS, SLAs)
Seguridad inform谩tica y autenticaci贸n, privacidad
Infraestructura para firma digital y certificados digitales
An谩lisis y detecci贸n de vulnerabilidades
Sistemas operativos
Sistemas P2P
Middleware
Infraestructura para grid
Servicios de integraci贸n (Web Services o .Net)Red de Universidades con Carreras en Inform谩tica (RedUNCI
Anales del XIII Congreso Argentino de Ciencias de la Computaci贸n (CACIC)
Contenido:
Arquitecturas de computadoras
Sistemas embebidos
Arquitecturas orientadas a servicios (SOA)
Redes de comunicaciones
Redes heterog茅neas
Redes de Avanzada
Redes inal谩mbricas
Redes m贸viles
Redes activas
Administraci贸n y monitoreo de redes y servicios
Calidad de Servicio (QoS, SLAs)
Seguridad inform谩tica y autenticaci贸n, privacidad
Infraestructura para firma digital y certificados digitales
An谩lisis y detecci贸n de vulnerabilidades
Sistemas operativos
Sistemas P2P
Middleware
Infraestructura para grid
Servicios de integraci贸n (Web Services o .Net)Red de Universidades con Carreras en Inform谩tica (RedUNCI