G-RCA: A Generic Root Cause Analysis Platform for Service Quality Management in Large IP Networks

摘要

As IP networks have become the mainstay of an increasingly diverse set of applications ranging from Internet games and streaming videos, to e-commerce and online-banking, and even to mission-critical 911, best effort service is no longer acceptable. This requires a transformation in network management from detecting and replacing individual faulty network elements to managing the service quality as a whole. In this paper we describe the design and development of a Generic Root Cause Analysis platform (G-RCA) for service quality management (SQM) in large IP networks. G-RCA contains a comprehensive service dependency model that includes network topological and cross-layer relationships, protocol interactions, and control plane dependencies. G-RCA abstracts the RCA process into signature identification for symptom and diagnostic events, temporal and spatial event correlation, and reasoning and inference logic. G-RCA provides a flexible rule specification language that allows operators to quickly customize G-RCA into different RCA tools as new problems need to be investigated. G-RCA is also integrated with the data trending, manual data exploration, and statistical correlation mining capabilities. G-RCA has proven to be a highly effective SQM platform in several different applications and we present results regarding BGP flaps, PIM flaps in Multicast VPN service, and end-to-end throughput drop in CDN service.