A scalable monitoring approach based on aggregation and refinement

Abstract

Abstract-Network monitoring is an integral part of any network management system. In order to ensure end-to-end service quality stated in service level agreements (SLAs), managers of a service provider network need to gather quality-of-service (QoS) measurements from multiple nodes in the network. For a large network with over thousands of flows with end-to-end SLAs, the information exchanged between network nodes and a central network management system (NMS) could be substantial.In this work, we propose a mechanism called aggregation and refinement based monitoring (ARM) to reduce the amount of information exchange. ARM is a generic mechanism that can be configured to run with different objectives, including threshold-based, rank-based and percentile-based. The mechanism enables the NMS to collect data from network nodes using a dynamic QoS data aggregation/refinement technique, and to process these information differently depending on its measurement objective.Our simulation results show that for these various objectives, the selective refinement process is able to validate SLAs quickly, is an order of magnitude more efficient than a simple polling scheme, and performs well across a wide range of traffic loads.