Anycast on the Move : A Look at Mobile Anycast Performance Sarah Wassermann

The appeal and clear operational and economic benefits of anycast to service providers have motivated a number of recent experimental studies on its potential performance impact for end users. For CDNs on mobile networks, in particular, anycast provides a simpler alternative to existing routing systems challenged by a growing, complex, and commonly opaque cellular infrastructure. This paper presents the first analysis of anycast performance for mobile users. In particular, our evaluation focuses on two distinct anycast services, both providing part of the DNS Root zone and together covering all major geographical regions. Our results show that mobile clients tend to be routed to suboptimal replicas in terms of geographical distance, more frequently while on a cellular connection than on WiFi, with a significant impact on latency. We find that this is not simply an issue of lacking better alternatives, and that the problem is not specific to particular geographic areas or autonomous systems. We close with a first analysis of the root causes of this phenomenon and describe some of the major classes of anycast anomalies revealed during our study, additionally including a systematic approach to automatically detect such anomalies without any sort of training or annotated measurements. We release our datasets to the networking community.

[1]  Kimberly C. Claffy,et al.  Two Days in the Life of the DNS Anycast Root Servers , 2007, PAM.

[2]  Joe Abley,et al.  Operation of Anycast Services , 2006, RFC.

[3]  Dario Rossi,et al.  A fistful of pings: Accurate and lightweight anycast enumeration and geolocation , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[4]  Lakshminarayanan Subramanian,et al.  An investigation of geographic mapping techniques for internet hosts , 2001, SIGCOMM 2001.

[5]  Dario Rossi,et al.  Characterizing IPv4 anycast adoption and deployment , 2015, CoNEXT.

[6]  Philippe Owezarski,et al.  Unsupervised Network Intrusion Detection Systems: Detecting the Unknown without Knowledge , 2012, Comput. Commun..

[7]  John P. Rula,et al.  Behind the Curtain: Cellular DNS and Content Replica Selection , 2014, Internet Measurement Conference.

[8]  Feng Qian,et al.  Cellular data network infrastructure characterization and implication on mobile content placement , 2011, PERV.

[9]  Dario Rossi,et al.  Latency-Based Anycast Geolocation: Algorithms, Software, and Data Sets , 2016, IEEE Journal on Selected Areas in Communications.

[10]  Ramesh Govindan,et al.  Diagnosing Path Inflation of Mobile Client Traffic , 2014, PAM.

[11]  David Thaler,et al.  Architectural Considerations of IP Anycast , 2014, RFC.

[12]  Bobby Bhattacharjee,et al.  Longitudinal Analysis of Root Server Anycast Inefficiencies , 2017 .

[13]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[14]  B. Huffaker,et al.  Distance Metrics in the Internet , 2002, Anais do 2002 International Telecommunications Symposium.

[15]  Ratul Mahajan,et al.  Analyzing the Performance of an Anycast CDN , 2015, Internet Measurement Conference.

[16]  John S. Heidemann,et al.  Does anycast hang up on you? , 2017, 2017 Network Traffic Measurement and Analysis Conference (TMA).

[17]  Randy Bush,et al.  Determining the Cause and Frequency of Routing Instability with Anycast , 2006, AINTEC.

[18]  Serge Fdida,et al.  Constraint-Based Geolocation of Internet Hosts , 2004, IEEE/ACM Transactions on Networking.