Final report on the evaluation of RRM/CRRM algorithms

Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin

Learning algorithms for the control of routing in integrated service communication networks

There is a high degree of uncertainty regarding the nature of traffic on future integrated service networks. This uncertainty motivates the use of adaptive resource allocation policies that can take advantage of the statistical fluctuations in the traffic demands. The adaptive control mechanisms must be 'lightweight', in terms of their overheads, and scale to potentially large networks with many traffic flows. Adaptive routing is one form of adaptive resource allocation, and this thesis considers the application of Stochastic Learning Automata (SLA) for distributed, lightweight adaptive routing in future integrated service communication networks. The thesis begins with a broad critical review of the use of Artificial Intelligence (AI) techniques applied to the control of communication networks. Detailed simulation models of integrated service networks are then constructed, and learning automata based routing is compared with traditional techniques on large scale networks. Learning automata are examined for the 'Quality-of-Service' (QoS) routing problem in realistic network topologies, where flows may be routed in the network subject to multiple QoS metrics, such as bandwidth and delay. It is found that learning automata based routing gives considerable blocking probability improvements over shortest path routing, despite only using local connectivity information and a simple probabilistic updating strategy. Furthermore, automata are considered for routing in more complex environments spanning issues such as multi-rate traffic, trunk reservation, routing over multiple domains, routing in high bandwidth-delay product networks and the use of learning automata as a background learning process. Automata are also examined for routing of both 'real-time' and 'non-real-time' traffics in an integrated traffic environment, where the non-real-time traffic has access to the bandwidth 'left over' by the real-time traffic. It is found that adopting learning automata for the routing of the real-time traffic may improve the performance to both real and non-real-time traffics under certain conditions. In addition, it is found that one set of learning automata may route both traffic types satisfactorily. Automata are considered for the routing of multicast connections in receiver-oriented, dynamic environments, where receivers may join and leave the multicast sessions dynamically. Automata are shown to be able to minimise the average delay or the total cost of the resulting trees using the appropriate feedback from the environment. Automata provide a distributed solution to the dynamic multicast problem, requiring purely local connectivity information and a simple updating strategy. Finally, automata are considered for the routing of multicast connections that require QoS guarantees, again in receiver-oriented dynamic environments. It is found that the distributed application of learning automata leads to considerably lower blocking probabilities than a shortest path tree approach, due to a combination of load balancing and minimum cost behaviour

Stochastic Dynamic Programming and Stochastic Fluid-Flow Models in the Design and Analysis of Web-Server Farms

A Web-server farm is a specialized facility designed specifically for housing Web servers catering to one or more Internet facing Web sites. In this dissertation, stochastic dynamic programming technique is used to obtain the optimal admission control policy with different classes of customers, and stochastic uid- ow models are used to compute the performance measures in the network. The two types of network traffic considered in this research are streaming (guaranteed bandwidth per connection) and elastic (shares available bandwidth equally among connections). We first obtain the optimal admission control policy using stochastic dynamic programming, in which, based on the number of requests of each type being served, a decision is made whether to allow or deny service to an incoming request. In this subproblem, we consider a xed bandwidth capacity server, which allocates the requested bandwidth to the streaming requests and divides all of the remaining bandwidth equally among all of the elastic requests. The performance metric of interest in this case will be the blocking probability of streaming traffic, which will be computed in order to be able to provide Quality of Service (QoS) guarantees. Next, we obtain bounds on the expected waiting time in the system for elastic requests that enter the system. This will be done at the server level in such a way that the total available bandwidth for the requests is constant. Trace data will be converted to an ON-OFF source and fluid- flow models will be used for this analysis. The results are compared with both the mean waiting time obtained by simulating real data, and the expected waiting time obtained using traditional queueing models. Finally, we consider the network of servers and routers within the Web farm where data from servers flows and merges before getting transmitted to the requesting users via the Internet. We compute the waiting time of the elastic requests at intermediate and edge nodes by obtaining the distribution of the out ow of the upstream node. This out ow distribution is obtained by using a methodology based on minimizing the deviations from the constituent in flows. This analysis also helps us to compute waiting times at different bandwidth capacities, and hence obtain a suitable bandwidth to promise or satisfy the QoS guarantees. This research helps in obtaining performance measures for different traffic classes at a Web-server farm so as to be able to promise or provide QoS guarantees; while at the same time helping in utilizing the resources of the server farms efficiently, thereby reducing the operational costs and increasing energy savings

Analysis of QoS Requirements for e-Health Services and Mapping to Evolved Packet System QoS Classes

E-Health services comprise a broad range of healthcare services delivered by using information and communication technology. In order to support existing as well as emerging e-Health services over converged next generation network (NGN) architectures, there is a need for network QoS control mechanisms that meet the often stringent requirements of such services. In this paper, we evaluate the QoS support for e-Health services in the context of the Evolved Packet System (EPS), specified by the Third Generation Partnership Project (3GPP) as a multi-access all-IP NGN. We classify heterogeneous e-Health services based on context and network QoS requirements and propose a mapping to existing 3GPP QoS Class Identifiers (QCIs) that serve as a basis for the class-based QoS concept of the EPS. The proposed mapping aims to provide network operators with guidelines for meeting heterogeneous e-Health service requirements. As an example, we present the QoS requirements for a prototype e-Health service supporting tele-consultation between a patient and a doctor and illustrate the use of the proposed mapping to QCIs in standardized QoS control procedures

Contribution to resource management in cellular access networks with limited backhaul capacity

La interfaz radio de los sistemas de comunicaciones móviles es normalmente considerada como la única limitación de capacidad en la red de acceso radio. Sin embargo, a medida que se van desplegando nuevas y más eficientes interfaces radio, y de que el tráfico de datos y multimedia va en aumento, existe la creciente preocupación de que la infraestructura de transporte (backhaul) de la red celular pueda convertirse en el cuello de botella en algunos escenarios. En este contexto, la tesis se centra en el desarrollo de técnicas de gestión de recursos que consideran de manera conjunta la gestión de recursos en la interfaz radio y el backhaul. Esto conduce a un nuevo paradigma donde los recursos del backhaul se consideran no sólo en la etapa de dimensionamiento, sino que además son incluidos en la problemática de gestión de recursos. Sobre esta base, el primer objetivo de la tesis consiste en evaluar los requerimientos de capacidad en las redes de acceso radio que usan IP como tecnología de transporte, de acuerdo a las recientes tendencias de la arquitectura de red. En particular, se analiza el impacto que tiene una solución de transporte basada en IP sobre la capacidad de transporte necesaria para satisfacer los requisitos de calidad de servicio en la red de acceso. La evaluación se realiza en el contexto de la red de acceso radio de UMTS, donde se proporciona una caracterización detallada de la interfaz Iub. El análisis de requerimientos de capacidad se lleva a cabo para dos diferentes escenarios: canales dedicados y canales de alta velocidad. Posteriormente, con el objetivo de aprovechar totalmente los recursos disponibles en el acceso radio y el backhaul, esta tesis propone un marco de gestión conjunta de recursos donde la idea principal consiste en incorporar las métricas de la red de transporte dentro del problema de gestión de recursos. A fin de evaluar los beneficios del marco de gestión de recursos propuesto, esta tesis se centra en la evaluación del problema de asignación de base, como estrategia para distribuir el tráfico entre las estaciones base en función de los niveles de carga tanto en la interfaz radio como en el backhaul. Este problema se analiza inicialmente considerando una red de acceso radio genérica, mediante la definición de un modelo analítico basado en cadenas de Markov. Dicho modelo permite calcular la ganancia de capacidad que puede alcanzar la estrategia de asignación de base propuesta. Posteriormente, el análisis de la estrategia propuesta se extiende considerando tecnologías específicas de acceso radio. En particular, en el contexto de redes WCDMA se desarrolla un algoritmo de asignación de base basado en simulatedannealing cuyo objetivo es maximizar una función de utilidad que refleja el grado de satisfacción de las asignaciones respecto los recursos radio y transporte. Finalmente, esta tesis aborda el diseño y evaluación de un algoritmo de asignación de base para los futuros sistemas de banda ancha basados en OFDMA. En este caso, el problema de asignación de base se modela como un problema de optimización mediante el uso de un marco de funciones de utilidad y funciones de coste de recursos. El problema planteado, que considera que existen restricciones de recursos tanto en la interfaz radio como en el backhaul, es mapeado a un problema de optimización conocido como Multiple-Choice Multidimensional Knapsack Problem (MMKP). Posteriormente, se desarrolla un algoritmo de asignación de base heurístico, el cual es evaluado y comparado con esquemas de asignación basados exclusivamente en criterios radio. El algoritmo concebido se basa en el uso de los multiplicadores de Lagrange y está diseñado para aprovechar de manera simultánea el balanceo de carga en la intefaz radio y el backhaul.Postprint (published version