Observing Slow Crustal Movement in Residential User Traffic

Transcription

1 Observing Slow Crustal Movement in Residential User Traffic Kenjiro Cho (IIJ), Kensuke Fukuda (NII), Hiroshi Esaki (Univ. of Tokyo), Akira Kato (Keio Univ.), Jun Murai (Keio Univ.) August 16 28

2 motivation many media coverage on explosive traffic growth by video content YouTube is just the beginning[cisco28b] but technical sources report only modest traffic growth worldwide MINTS: 5-6% in U.S. and worldwide Cisco visual networking index: worldwide growth of 5% per year over last few years why is traffic growth important? one of the key factors driving research, development and investiment in technologies and infrastructures with annual growth of %, it grows -fold in years with annual growth of 5%, it grows 58-fold in years key question: what is the macro level impact of video and other rich media content on traffic growth at the moment? 2 / 22

3 residential broadband subscribers in Japan 28.7 million broadband subscribers as of March 28 DSL:12.7 million, FTTH:12.2 million, CATV:3.3 million shift from DSL to FTTH: about to exceed DSL Mbps bi-directional fiber access costs 4USD/month significant impact to backbones Number of subscribers [million] 15 5 DSL CATV FTTH Year 3 / 22

4 traffic growth in backbone rapidly growing residential broadband access low-cost high-speed services, especially in Korea and Japan Japan is the highest in Fiber-To-The-Home (FTTH) traffic growth of the peak rate at major Japanese IXes modest growth of about 4% per year since 25 4 Aggregated IX traffic [Gbps] Traffic volume Growth rate Year 3 Annual growth rate 2 4 / 22

5 data collection across major ISPs focus on traffic crossing ISP boundaries (customer and external) tools were developed to aggregate MRTG/RRDtool traffic logs only aggregated results published not to disclose individual ISP share challenges: mostly political or social, not technical (B1) (B2) (B3) external 6IXes external domestic external international JPNAP/JPIX/NSPIXP local IXes private peering/transit external provider edge ISP customer edge (A1) (A2) RBB customers non-rbb customers DSL/CATV/FTTH leased lines data centers dialup 5 traffic groups at ISP cusomer and external boundaries 5 / 22

6 methodology for aggregated traffic analysis month-long traffic logs for the 5 traffic groups with 2-hour resolution each ISP creates log lists and makes aggreagated logs by themselves without disclosing details biggest workload for ISP creating lists by classifying large number of per-interface logs some ISPs have more than, logs! maintaining the lists frequent planned and unplanned configuration changes data sets 2-hour resolution interface counter logs from Sep/Oct/Nov 24, May/Nov by re-aggregating logs provided by 7 ISPs IN/OUT from ISPs view 6 / 22

7 traffic growth 22-68% increase in 27 RBB: 22% increase for inbound, 29% increase for outbound a sharp increase in international inbound due to popular video services Traffic (Gbps) A1(in) A1(out) A2(in) A2(out) 24/9 25/5 26/5 27/5 28/5 Traffic (Gbps) 15 B1(in) B1(out) 5 B2(in) B2(out) B3(in) B3(out) 24/9 25/5 26/5 27/5 28/5 7 / 22

8 changes in RBB weekly traffic in 24, inbound and outbound was almost equal in 28, outbound (downloading to users) became larger both constatnt portion and daily fluctuations grew in 28 implies a shift from p2p to video (e.g, YouTube) 8 / 22

9 analysis of per-customer traffic in one ISP one ISP provided per-customer traffic data Sampled NetFlow data from edge routers accommodating fiber/dsl RBB customers week-long data from Apr 24, Feb 25, Jul 27, Jun 28 Feb 25 and Jun 28, before and after the advent of YouTube 9 / 22

10 ratio of fiber/dsl active users and total traffic volumes in 28, 8% of active users are fiber users, consuming 9% of traffic active users (%) total volume (%) 25 fiber DSL fiber DSL / 22

11 PDF of daily traffic per user 2 lognormal distributions: asymmetric, symmetric high-volume high-volume dist: not growing much total(left) fiber(middle) DSL(right) in 5(top),8(bottom) mode: 3.5MB,32MB/day(25), 5MB,94MB/day(28).5 (a) Total (25) In Out.5 (b) Fiber (25) In Out.5 (c) DSL (25) In Out Probability density Probability density Probability density Daily traffic per user (bytes) Daily traffic per user (bytes) Daily traffic per user (bytes).5 (d) Total (28) In Out.5 (e) Fiber (28) In Out.5 (f) DSL (28) In Out Probability density Probability density Probability density Daily traffic per user (bytes) Daily traffic per user (bytes) Daily traffic per user (bytes) 11 / 22

12 CCDF of daily traffic per user only outbound (download for users) increased Cumulative distribution (a) 25 In Out Daily traffic per user (bytes) Cumulative distribution (b) 28 In Out Daily traffic per user (bytes) 12 / 22

13 CDF of traffic volume consumed by top heavy-hitters graph: the top N% of heavy-hitters use X% of the total traffic highly skewed distribution in traffic usage no noticeable change from 25 to 28 probably because client-type users also have long-tailed distributions Cumulative traffic In (25) Out (25) In (28) Out (28) Cumulative heavy hitters 13 / 22

14 correlation of inbound/outbound volumes per user fiber (left) and DSL (right) in 25 (top) and 28 (bottom) 2 clusters: one below the unity line, another in high volume region no clear boundary: heavy-hitters/others, client-type/peer-type (a) Fiber (25) Daily inbound traffic (byte) Daily inbound traffic (byte) Daily outbound traffic (byte) Daily outbound traffic (byte) (d) DSL (28) 9 (c) Fiber (28) Daily inbound traffic (byte) Daily inbound traffic (byte) 11 (b) DSL (25) Daily outbound traffic (byte) Daily outbound traffic (byte) / 22

15 protocols/ports ranking classify client-type/peer-type with threshold: MB/day upload protocol port total client peer total client peer (%) type type (%) type type TCP * (< 24) (http) (rtsp) (https) (ftp-data) (>= 24) (gnutella) (winmx) (winny) (rtmp) (bittorrent) UDP * (dns) others ESP GRE ICMP / 22

16 temporal behavior of TCP port usage 3 types: port 8, well-kown port but 8, dynamic ports total users (top), client-type (middle), peer-type (bottom) in 25 (left) and 28 (right) 16 / 22

17 summary of per-customer traffic analysis overall traffic is still dominated by heavy-hitters, mainly using p2p but its traffic decreased in population share and volume share current slow growth is due to stalled growth of dominant aggressive p2p traffic client-type traffic slowly moving towards high-volume circumstantial evidence: driven by video and other rich media 17 / 22

18 growth model based on lognormal distributions fitting client-type outbound volumes to lognormal dist. p(x) = 1 xσ 2π exp( (ln x µ)2 2σ 2 ) E(x) = exp(µ + σ 2 /2) by definition, mean grows much faster than mode simplistic growth projections for outbound traffic per user (MB/day) for client-type users mode mean 24 Apr Feb Jul Jun growth/yr Jun Jun Jun / 22

19 outbound traffic growth of client-type users 4 Traffic Volume per user [MB/day] 3 2 Mean Mode 24/4 25/4 26/4 27/4 28/4 29/4 2/4 211/4 Year 19 / 22

20 conclusion apparent slow growth attributed to decline of p2p traffic but p2p willl not go away anytime soon p2p could evolve for large scale distribution crustal is slowly swelling with video content similar to how web traffic was perceived in late 9es network capacity also grows 5% per year (by various sources) difficult to predict future traffic (lognormal!) many challenges ahead technical factors: content caching, CDN, QoS economic factors: access cost, capacity/equipment costs political/social factors: net-neutrality, content management 2 / 22

21 acknowledgments IIJ, SoftBank Telecom, K-Opticom, KDDI, NTT Communications, SoftBank BB for data collection support ministry of internal affairs and communications for coordination 21 / 22

22 references [CFEK26] K. Cho, K. Fukuda, H. Esaki, and A. Kato. The impact and implications of the growth in residential user-to-user traffic. In SIGCOMM26, Pisa, Italy, Aug. 26. [Cisco28b] Cisco. Approaching the zettabyte era. ns75/ns827/ white paper c ns827 Networking Solutions White Paper.html, June 28. [Cisco28a] Cisco. visual networking index forecast and methodology, networking solutions sub solution.html, June 28. [Odlyzko28] A. M. Odlyzko. Minnesota Internet traffic studies / 22