Self-organizing overlay networks for Autonomic Manager selection

Recent work in network management has led to the incorporation of Autonomic Computing (AC) concepts in management tasks to reduce cost and complexity. IBM was the first to propose the concept of AC for systems management [1]. Entities called Autonomic Managers (AMs) run an Autonomic Control Loop (ACL) to perform self-management functions. Every AM is responsible for monitoring one or more elements. All nodes of an overlay network are autonomic entities but only a subset is active in the management process depending on available resources. This paper proposes an efficient self-organizing mechanism for the autonomic management of overlay networks that reduces delays and error ratios of sensed data. A game theory approach to solve the AM selection problem is used. The game is based on a modified regret matching scheme. Advantages are that the approach is highly distributed and incurs limited network overhead. More importantly, it adapts to the dynamic network conditions. Simulation results show that our scheme achieves high gains in terms of delays and error rates compared to a straightforward scheme where nodes report to the closest AM.