Large-scale Measurements of Wireless Network Behavior

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Large-scale Measurements of Wireless Network Behavior Sanjit Biswas (biswas@samsara.com), John Bicket (jbicket@samsara.

1 Large-scale Measurements of Wireless Network Behavior Sanjit Biswas John Bicket Edmund L. Wong Raluca Musaloiu-E Apurv Bhartia Dan Aguayo

Over 30 years of unlicensed devices 1999: 802.11a 2002: 802.11g 2009: 802.

2 Over 30 years of unlicensed devices 1999: a 2002: g 2009: n Over a billion new devices added each year phones, laptops, security cameras, headsets, fitness trackers 1999: U-NII 1985: ISM band created 1989: New power limits 1998: b 1999: Bluetooth 2 Source: WhitePaper-FI NAL pdf

limit (3000W/MHz), Billions of unlicensed devices Open channel

3 How does unlicensed spectrum perform in the real world? Billions of cellular devices Controlled channel access, High power limit (3000W/MHz), Billions of unlicensed devices Open channel access, Low power limit (1W total), works well in the lab what about the real world? 3

4 Outline How our measurements were collected What does the application and device workload look like? How prevalent is interference? Lessons and conclusions 4

5 Meraki s unique perspective Centralized management platform for thousands of networks worldwide Time-series data of application, client and device statistics 5

This study: data from thousands of networks Collected data over a one week period from 20,667 networks, in Jan. 2014 and Jan. 2015 5.

6 This study: data from thousands of networks Collected data over a one week period from 20,667 networks, in Jan and Jan M clients across wide variety of deployment types Industry # networks Architecture/Engineering 127 Construction 333 Consulting 365 Education 4,075 Finance/Insurance 737 Government/Public Sector 1,112 Healthcare 1,382 Hospitality 493 Industrial/Manufacturing 1,220 Legal 264 Media/Advertising 427 Non-Profit 640 Real Estate 386 Restaurants 296 Retail 2,355 Tech 983 Telecom 442 VAR/System Integrator 2,876 Other 2,154 Total 20,667 6

7 7 How are people using WiFi in 2015?

8 Usage by application Application Category TB (% total) % increase # clients Miscellaneous web Other 239 (13%) 67% 4,623, YouTube Video & music 202 (11%) 93% 1,934, Media accounts for large fraction of traffic (YouTube, Netflix, itunes) MB / client Netflix Video & music 188 (9.8%) 76% 161,014 1,224 Non-web TCP Other 156 (8.2%) 51% 3,656, Miscellaneous secure web Other 147 (7.7%) 94% 5,115, itunes Video & music 102 (5.4%) 66% 2,230, Miscellaneous video Video & music 98 (5.1%) 61% 1,383, Windows file sharing File sharing 87 (4.5%) 48% 740, CDNs Other 75 (3.9%) 81% 3,157, UDP Other 61 (3.2%) 60% 3,705, Facebook Social 53 (2.8%) 127% 3,579, Heavy networks may traffic shape bitrate adaptive, latency insensitive traffic 8

9 Usage by device type OS TB (% total) % increase # clients % increase MB / client % increase Windows 589 (30%) 43% 822,761 28% % Apple ios 545 (28%) 92% 2,550,379 34% % Mac OS X 445 (23%) 44% 313,976 24% 1,487 17% Android 177 (9.1%) 172% 1,535,859 61% % Unknown 78 (4.0%) -9.2% 228, % % Chrome OS 62 (3.2%) 275% 178, % % All 1,950 (100%) 62% 5,578,126 37% % Smartphones outnumber laptops 4:1, but usage on laptops is much higher Average usage per device growing faster on smartphones 9

10 What types of radios are out there? Moore s law advances in signal processing have led to more streams, higher bandwidths However, n does not imply MIMO iphone 5, iphone 6 use single stream 11n/ac Due to antenna space, power, fast enough Implication for protocol design: New technologies take time to have an impact Co-existence is common, even with 5+ year old standards Jan Jan g 99.9% 99.9% n 95.7% 97.7% 5 GHz 48.9% 64.9% 40 MHz channels 23.4% 63.8% ac 2.5% 18.0% Two streams 7.7% 19.3% Three streams 2.4% 3.8% Four streams 0.7% 1.8% 10

11 11 Interference

with dual concurrent radios Meraki MR18: newer design with scanning radio

12 Hardware platform Full control of hardware and firmware, along with identical hardware makes it easy to look at data Meraki MR16: older design with dual concurrent radios Meraki MR18: newer design with scanning radio for spectrum analysis 2x2 2.4GHz n 2x2 5GHz n 1x1 2.4/5GHz scanning radio 12

13 Nearby networks Network s Networks per AP 2.4 GHz (now) 527, GHz (six months ago) 230, GHz (now) 35, GHz (six months ago) 19, Cumulative fraction of links GHz 6 months ago 2.4 GHz now 5 GHz 6 months ago 5 GHz now Sudden increase in 2014: personal hotspots, guest access SSIDs Difficult to measure changes in client throughput, but inter-ap link delivery rates in the 2.4GHz band are 10% lower Delivery ratio

14 Channel utilization Cumulative Fraction of Channel Utilization Measurements GHz (PM) 5GHz (AM) 2.4GHz (PM) 2.4GHz (AM) Number of Interfering APs Channel Utilization Band Utilization (%) Energy detect is triggered 20% of the time for the median 2.4GHz radio, 50% for top 10 th percentile Nearby network count doesn t predict utilization better to measure directly 14

What is causing the interference? Cumulative Fraction of Total Utilization Measurements 1 0.8 0.6 0.4 0.2 0 2.

15 What is causing the interference? Cumulative Fraction of Total Utilization Measurements GHz 5GHz Decodable Traffic (%) 15 Much of the channel utilization caused by other traffic Helpful for MAC protocol design to be able to decode headers

16 Related work Link measurements [Aguayo04]: data from 38-node outdoor b network with intermediate links [Reis06], [Halperin11]: data from 15 node indoor network showing selective fading [Gollakota08]: effects of interference and cancellation methods Network studies [Ghosh11] study of AT&T s hotspot network with 240k client devices [Afanasyev10] Google WiFi s 500 node outdoor network with 30k client devices [Gember11] study of UWisc network with 32k devices This paper s primary contributions are measurements across many networks and a look at real-world interference 16

17 Lessons and conclusions At large scale, you see extremes of distributions Access points with 10,000 nearby networks (passenger bus in Manhattan) Cat 5/6 cable problems in large networks Overloaded RADIUS servers causing client device auth problems Network design needs to adapt over time In 2006, very few smartphones in use now they are majority of devices Rethink assumptions around device roaming, addressing and hardware Application workloads shifted from web to video traffic shaping more useful Network operators and protocol designers should assume significant interference from legacy devices too many out there to ignore 17

18 Thanks! Anonymized dataset available at: 18

Cisco Meraki Cisco and/or its affiliates. All rights reserved.

Cisco Meraki Cisco and/or its affiliates. All rights reserved. Cisco Meraki 2014 Cisco and/or its affiliates. All rights reserved. Cisco Meraki Market Trends New Wireless Hardware New Wireless Software Timing and Availability Q&A 2014 Cisco and/or its affiliates.