A Scalable Monitoring Approach for Service Level Agreements Validation

In order to detect violations of end-to-end service level agreements (SLA) and to isolate trouble links and nodes based on a unified framework, managers of a service provider network need to gather Quality of Service (QoS) measurements form multiple nodes in the network. For a network carrying 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. Moreover, in situations where only a small number of flows violate their respective SLAs, simple polling mechanisms can lead to huge unnecessary over-head in identifying these ill-behaved flows. In this work, we proposed an algorithm called ARM (Aggregation and Refinement based Monitoring) to reduce the amount of information exchange. ARM uses a histogram-based dynamic QoS data aggregation/refinement technique at each network node and a reasoning engine at the NMS to minimize the amount of data exchange between network nodes and NMS. ARM not only reduces unnecessary reporting through selective refinement, it also performs well across a wide range of traffic loads. Our simulation results show that ARM is at least an order of magnitude more efficient than a simple polling scheme. It also outperforms two centralized, highly optimized schemes that cannot be implemented in practice.