The size and complexity of enterprise computer systems are growing rapidly. As a result, system management has become increasingly difficult and expensive. In fact, management costs are typically estimated at 50%-70% of the total cost of ownership. Despite large investments in management software and personnel, enterprise computer systems are usually managed sub-optimally. This situation calls for a fundamental change in the way systems are managed. Recent studies suggest that systems manage themselves autonomously. Initial studies towards that goal reside in the fields of Autonomic Computing and Self* Systems. Many of those studies devise mechanisms for system monitoring, data filtering, problem determination, problem fixing and system adjustment and reconfiguration. The mechanisms are based, in large part, on methods from Artificial Intelligence, Statistics, Operations Research and Software Engineering. Current enterprise system management solutions - theoretical and practical - are centralized. However, the typical enterprise system is distributed. We claim that a centralized solution presents several risks. For instance, it introduces a management bottleneck, it incurs communication overheads, etc. We hence advocate that enterprise system management should be addressed in a distributed manner. Combining distribution and AI, we believe that an agent-based solution is appropriate. In this paper we present in more detail the suggested approach. Copyright 2006 ACM.
|Number of pages||4|
|Journal||Proceedings of the International Conference on Autonomous Agents|
|State||Published - 1 Dec 2006|