Diagnosing Path Inflation of Mobile Client Traffic

Transcription

1 Diagnosing Path Inflation of Mobile Client Traffic Kyriakos Zarifis, Tobias Flach, Srikanth Nori, David Choffnes, Ramesh Govindan, Ethan Katz- Bassett, Z. Morley Mao, Matt Welsh University of Southern California March 10, 2014

2 Introduction Mobile devices are primary web devices now and we expect quick content delivery. Requires direct routes from mobile devices to content, but previous studies indicated that many routes were indirect. We investigated this problem at scale and across time. 2

3 Overview Understand the impact of Internet topology and routing on mobile users Identify and quantify the performance impact of inflated Internet routes Based on over 900,000 measurements from mobile devices, spanning over a time period of 18 months Path inflation: Cases where traffic leaves metro area even though target server is located nearby 3

4 Contributions We observed path inflation in 47% of the measurements taken at the end of We propose a taxonomy to classify root causes for path inflation. We show that in some cases access to web content is delayed by hundreds of milliseconds. 4

5 Outline Overview Optimal routing and path inflation Types of path inflation Evaluating the carrier landscape in the US Conclusion 5

6 Background Want to understand how carrier topology, routing choices and peering arrangements affect Internet service performance for mobile devices Ingress Point Mobile Carrier Public Internet Peering Point Google Server Location 6

7 Connection Setup Server A DNS Resolver Update mapping based on observed performance Server A Server B 7

8 Optimal Routing Metro Area In the optimal case, all parts are located in the same metro area 8

9 Related work Interdomain routing suffers from path inflation caused by structural limitations. [Tangmunarunkit, 2001] Clients in same area can experience different latencies when accessing Google. [Krishnan, 2009; Zhu, 2009] Routing over sub-optimal paths causes 45% RTT increase due to lack of ingress points. [Dong, 2011] US mobile carriers have only four to six ingress points. [Xu, 2011] 9

10 The Rise of Mobile Traffic Related Work Source: Ericsson Mobility Report (Nov 2013) 10

11 Dataset Measurements taken by Speedometer, an Android app developed by Google, deployed on thousands of volunteer devices Focusing on traceroutes, pings, and DNS lookups collected by devices in the US over an 18 month period 11

12 Quantifying Path Inflation AT&T Sprint T-Mobile Verizon Overall Q4/ Q2/ Q4/ Fraction of inflated paths 47% of all traceroutes to Google experienced path inflation in Q4/2011 Next step: Determine the causes for path inflation 12

13 Outline Overview Optimal routing and path inflation Types of path inflation Evaluating the carrier landscape in the US Conclusion 13

14 Types of Path Inflation Four types of path inflation based on the path segment where inflation occurs Carrier Access Inflation Interdomain Inflation Carrier Access-Interdomain Inflation Provider Backbone Inflation 14

15 Path Features Carrier Access Internet Access Google Access Local Metro Area Remote Metro Area 15

16 Carrier Access Inflation Traffic enters public Internet in remote area Local Metro Area Remote Metro Area Carrier Access Internet Access Google Access 16

17 Interdomain Inflation Traffic enters Google s network in remote area Local Metro Area Remote Metro Area Carrier Access Google Access Internet Access 17

18 Carrier Access-Interdomain Inflation Traffic enters public Internet in remote area, and accesses a Google server in local area Local Metro Area Remote Metro Area Carrier Access Internet Access Google Access 18

19 Provider Backbone Inflation Traffic enters Google s network in local area, but accesses a server in remote area Local Metro Area Remote Metro Area Carrier Access Internet Access Google Access 19

20 DNS Clustering Server A DNS Resolver Update mapping based on observed performance Server A Server B 20

21 Finding Ingress Points Find ingress points through graph aggregation of traceroutes, and DNS name resolution cr2.sffca.ip.att.net San Francisco, CA Private or unresolvable hops 21

22 Finding Peering Points Find peering points by evaluating last hop before entering Google s network POS4-0-0.GW4.SJC7.ALTER.NET San Jose, CA Mobile Carrier (e.g. Verizon) (whois: Google) 22

23 Outline Overview Optimal routing and path inflation Types of path inflation Evaluating the carrier landscape in the US Conclusion 23

24 Case Study 1: Lack of Ingress AT&T New York traffic enters public Internet in Chicago area Chicago New York 1074km extra distance 16ms extra latency 24

25 Case Study 2: Lack of Peering AT&T Seattle traffic enters Google s network in Bay area Seattle 1089km extra distance 16ms extra latency San Francisco 25

26 Case Study 3: Client Clustering Verizon clients from San Francisco and Seattle directed to same Google server Seattle 1089km extra distance 16ms extra latency San Francisco 26

27 Inflation Breakdown: AT&T Area San Francisco Count Fraction Inflated Cause(s) Ingress, Peering Extra distance 4200km Extra PLT 315ms Seattle Peering 2106km 158ms New York Ingress 2148km 161ms All clients in at least three metro areas observed increased latency due to lack of ingress and peering points 27

28 Inflation Breakdown: Verizon Area Count Fraction Inflated Cause(s) Extra distance Extra PLT San Francisco Seattle New York 28

29 Inflation Breakdown: Verizon Area San Francisco Count Fraction Inflated Cause(s) Extra distance Extra PLT Clustering 2178km 163ms Seattle Clustering 1974km 148ms New York Unknown 694km 52ms Verizon clients in some areas observed path inflation possibly due to using the same DNS resolvers 29

30 Observed Ingress Points: Sprint Seattle Omaha New York San Francisco Phoenix Kansas City Chicago Washington Los Angeles Atlanta Dallas Miami 12 or more ingress points, and observed new ones throughout measurement study 30

31 Outline Overview Optimal routing and path inflation Types of path inflation Evaluating the carrier landscape in the US Conclusion 31

32 Dataset Visualization 32

33 San Francisco Area: T-Mobile 33

34 San Francisco Area: Carrier Comparison AT&T Sprint 34

35 Path Inflation Today Path inflation in the US disappeared over time Inflated paths in other regions of the world Nawras clients in Oman are directed to Paris (France) instead of New Delhi (India) Round-trip distance increased by over 7000km Movistar clients in Spain are directed to London (UK) instead of targeting local servers in Madrid 35

36 Conclusion Observed and analyzed path inflation for mobile clients based on longitudinal measurements Identified reasons behind path inflation and quantified their performance impact While path inflation in the US is no longer a problem, it still affects performance for other clients around the world 36