An Intelligent Mobile-Agent Based Scalable Network Management Architecture for Large-Scale Enterprise System

TL;DR

This paper proposes an intelligent hierarchical mobile-agent architecture for scalable management of large, complex networks, effectively reducing management overhead compared to traditional centralized and flat models.

Contribution

It introduces a novel scalable hierarchical agent-based model tailored for large-scale networks with diverse technologies, improving management efficiency and reducing overhead.

Findings

The architecture reduces management data overhead.

It outperforms centralized and flat agent models.

Cost estimation validates efficiency improvements.

Abstract

Several Mobile Agent based distributed network management models have been proposed in recent times to address the scalability and flexibility problems of centralized (SNMP or CMIP management models) models. Though the use of Mobile Agents to distribute and delegate management tasks comes handy in dealing with the previously stated issues, many of the agent-based management frameworks like initial flat bed models and static mid-level managers employing mobile agents models cannot efficiently meet the demands of current networks which are growing in size and complexity. Moreover, varied technologies, such as SONET, ATM, Ethernet, DWDM etc., present at different layers of the Access, Metro and Core (long haul) sections of the network, have contributed to the complexity in terms of their own framing and protocol structures. Thus, controlling and managing the traffic in these networks is a challenging task. This paper presents an intelligent scalable hierarchical agent based model for the management of large-scale complex networks to address aforesaid issues. The cost estimation, carried out with a view to compute the overall management cost in terms of management data overhead, is being presented. The results obtained thereafter establish the usefulness of the presented architecture as compare to centralized and flat bed agent based models.