| Popis: |
The increasing scale, technology advances, and services of today's networks have dramatically complicated their management in such a way that in the near future human administrators will not be able to achieve efficient management. To control this complexity, a promising approach aiming to create self-managed networks has been introduced. This approach, called autonomic computing, aims to design network equipment able to self-adapt their configuration and to self-optimize their performance depending on their situation, in order to fulfill high-level objectives defined by human operators. This chapter presents Autonomic NEtwork Management Architecture (ANEMA) that implements several policy forms to achieve autonomic behaviors in the network. ANEMA captures the high-level objectives of the human administrators and users and expresses them in terms of utility function policies. The goal policies describe the high-level management directives needed to guide the network to achieve the previous utility functions. The behavioral policies capture behaviors that should be followed by network equipment to react regarding their context changes by considering the described goal policies. In order to implement this utility-based analytical approach, the chapter proposes a set of extensions to the Distributed Management Task Force (DMTF) Common Information Model. These extensions represent the metrics used to calculate the Quality of Experience (QoE) as perceived by the user as well as the processes that implement it. This QoE will help the service provider to maximize the client's satisfaction while guaranteeing a good return on investment. In order to highlight the benefit of some aspect of ANEMA applied in case of multiservice IP network management, a testbed has been implemented and simulation scenarios have been achieved. |
