QoEScope: Adaptive IP service management for heterogeneous enterprise networks

In the recent years, a progressively growing number of computing and communication services have undertaken the migration from their conventional media to the new unified platform, IP networks. As a consequence, business success of service providers becomes largely determined by the effectiveness of their service management schemes, which require rapid identification of problems and resolution of network-related anomalies. However, this is a non-trivial task in heterogeneous enterprise networks due to service providers' invisibility of the health and performance of the underlying carrier network. In addition, the gap between quality of service (QoS) measurements reflecting network performance and quality of experience (QoE) metrics indicating user-perceived service quality further makes effective service management more challenging. In this paper, we present a unified service management system called QoEScope, which combines scalable end-to-end probing, accurate topology inference in the presence of implicit routers, adaptive bridging between QoS measurement and QoE metrics, and intelligent root cause analysis. Extensive testbed emulations and Internet experiments demonstrate that QoEScope is a highly practical and effective IP service management solution for heterogeneous enterprise networks.

[1]  Xu Chen,et al.  Automating Network Application Dependency Discovery: Experiences, Limitations, and New Solutions , 2008, OSDI.

[2]  Fangzhe Chang,et al.  Topology inference in the presence of anonymous routers , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[3]  Paramvir Bahl,et al.  Towards highly reliable enterprise network services via inference of multi-level dependencies , 2007, SIGCOMM.

[4]  Robert D. Nowak,et al.  Multiple-Source Internet Tomography , 2006, IEEE Journal on Selected Areas in Communications.

[5]  Paul Barford,et al.  Network discovery from passive measurements , 2008, SIGCOMM '08.

[6]  Shueng-Han Gary Chan,et al.  Traceroute-Based Topology Inference without Network Coordinate Estimation , 2008, 2008 IEEE International Conference on Communications.

[7]  Ramesh Govindan,et al.  Heuristics for Internet map discovery , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[8]  Taieb Znati,et al.  An Optimized Clustering and Selective Probing Framework to Support Internet Quality-of-Service Routing , 2006, Simul..

[9]  Jonathan M. Smith,et al.  Networkmd: topology inference and failure diagnosis in the last mile , 2007, IMC '07.

[10]  Mark Handley,et al.  Designing DCCP: congestion control without reliability , 2006, SIGCOMM 2006.

[11]  Henning Schulzrinne,et al.  RTP: A Transport Protocol for Real-Time Applications , 1996, RFC.

[12]  Bill Cheswick,et al.  Mapping and Visualizing the Internet , 2000, USENIX Annual Technical Conference, General Track.

[13]  Helen J. Wang,et al.  Server-based inference of Internet link lossiness , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[14]  Robert Nowak,et al.  Network Tomography: Recent Developments , 2004 .

[15]  Jian Ni,et al.  Network Routing Topology Inference from End-to-End Measurements , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[16]  Robert D. Nowak,et al.  Maximum likelihood network topology identification from edge-based unicast measurements , 2002, SIGMETRICS '02.

[17]  Azer Bestavros,et al.  Inference and labeling of metric-induced network topologies , 2005, IEEE Transactions on Parallel and Distributed Systems.

[18]  Jian Ni,et al.  Packet doppler: network monitoring using packet shift detection , 2008, CoNEXT '08.