A Scalable Monitoring Approach based on Aggregation and Refinement

Network monitoring is an integral part of any network management system. In order to ensure service quality given the end-to-end service level agreements (SLA), managers of a service provider network need to gather Quality of Service (QoS) measurements from multiple nodes in the network. For a 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 an algorithm called ARM (Aggregation and Refinement based Monitoring) to reduce the amount of information exchange. 

ARM is a generic algorithm that can be configured to run with different objectives, including threshold-based, rank-based and percentile-based. The algorithm collects data using a histogram-based dynamic QoS data aggregation/refinement technique at each network node and processes these information differently depending on the measurement objectives. 

Our simulation results show that for these various objectives, the selective refinement process is able to isolate problematic links quickly, is an order of magnitude more efficient than a simple polling scheme and performs well across a wide range of traffic loads. For threshold-based objective, it also outperforms two centralized, highly optimized schemes that cannot be implemented in practice.