Network Self-Management Pseudo-Predictability based on Case-Based Reasoning

Network Self-Management Pseudo-Predictability based on Case-Based Reasoning: Network self-management, among other objectives, looks for solutions (systems, frameworks and/or strategies) in which the human intervention is drastically reduced in the analysis and planning of new network states, which in turn, is based on a set of SLAs (Service Level Agreement) definitions and traffic profile. The self-management system/framework has to find a solution meeting SLA and traffic requirements within a reasonable computational time (execution time) in order to achieve a near real time operation. In general, it is necessary to adopt approaches based on reasoning and learning in self-management systems in order to avoid and/or reduce human intervention [1]. As such, self-management systems/frameworks do require the storage of network states. In this context, it is important to identify critical states and, if possible, to predict when they will occur in order to facilitate the self-management process. In this paper, a proposal for pseudo-predictability based on Case-Based Reasoning (CBR) is presented. The pseudo-predictability CBR method considers previous network states to infer future network state scenario. It was developed for the self-management framework defined in [2] and, in general, further promotes the scalability of self-managed systems.