Abstract. The growing heterogeneity and scalability of Internet services has complicated, beyond human capabilities, the management of network devices. Therefore, a new paradigm called autonomic networking is being introduced to control, in an efficient and automatic manner, this complex environment. This approach aims to enhance network elements with capabilities that allow them to choose their own behavior for achieving high-level directives. This so called autonomic network element should be able to optimize its configuration, ensure its protection, detect/repair unpredicted conflicts between services requirements and coordinate its behavior with other network elements.In this paper, we present a research activity that investigates this new concept, and applies it to facilitate the configuration and the optimization of a multi-services IP network. This approach is a first step toward building a self-configured and self-optimized IP network that automatically supports the QoS requirements of heterogeneous applications without any external intervention. Different paradigms have been explored in order to model this behavior and to render network equipment autonomic. A laboratory prototype has been developed to highlight the autonomic behavior of the network to achieve heterogeneous QoS requirements of multimedia and data applications.
The optimization is an essential process for operators to ensure the best networks performance as well as an adequate return on investment. In the case of a multiservice IP network, this process consists on providing high-quality services to end users with the aim of maximizing their satisfaction. To achieve this goal, we propose an analytical model to quantify the Quality of Experience (QoE) parameter that evaluates the perceived service quality as well as the user satisfaction level. This model aims to optimize services utility according to the user preferences and the available bandwidth. It consists on evaluating: i) the estimated user's utility function according to the user's available bandwidth, ii) the network delivered utility function and finally iii) the QoE and the service cost. We define also a recursive process that allow to auto-adapt the services utility for the end users by adjusting the service importance parameter. Finally, in order to validate our proposed model and the service utility adaptation process, we carried out a set of simulations that we describe in the last section of this paper.
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