Mobile Data Offloading : How much can WiFi deliver?

1. Mobile Data Offloading : How much can WiFi deliver? Presented by Gregory Teodoro Paper by Kyunghan Lee, Injong Rhee, Joohyn Lee, Song Chong, and Yung Yi

2. What is this about? • Mobile Data Offloading is the use of networking technologies to deliver data originally mean for cellular networks. • This paper is a quantitative study on performance of 3G mobile data offloading through WiFi Networks • Study done using over 100 iPhone users and data collection. • Purpose is to discover how much of an effect data offloading has on mobile data traffic and battery life.

3. Introduction • Mobile data traffic is growing at unprecedented rates. • Prediction is that by 2014 an average broadband mobile user will use 7GB of traffic each month, almost 5.4 times as much as is used now. • Prediction that 66% of this data is through mobile video data. • Proposed solutions to this problem have issues. • Scaling network capacity by building more towers and base stations, or upgrading stations comes at a huge cost, with no gain. • Revenue is independent from actual data usage. • Switch to pure-usage pricing. • May backfire, as it singles out particular user groups.

4. WiFi Offloading instead? • Most viable solution at moment. Why? • Building WiFi hot spots is significantly cheaper. • Can piggy back off a user’s own WiFi AP. • Already a wide-spread deployment of WiFi APs. • Addresses the “Time-to-Capacity” issue for current needs of additional WiFi

5. Types of Offloading • On-The-Spot • Uses spontaneous connectivity to WiFi and transfer data on the spot. • When a user leaves WiFi coverage, offloading ends and unfinished transfers through cellular networks. • Smart phones already give priority to WiFi than cellular interface. • Delayed • Each data transfer is given a “deadline” when it must be sent out. • Sends the data piece by piece as a user enters and exits different WiFi areas. • If data is not sent out before deadline, it is finished using the cellular networks.

6. User and Network Payoffs • How does this help the Users and Network Providers? • Using WiFi to offload data lowers overall cost of data transfers. • Users may benefit from lowered subscription prices due to lowered costs. • Proper use of data transfer delays via delayed offloading can help users select more specific plans. • Fundamentally tied to mobility patterns and WiFi availability.

7. Findings Summary • On-the-spot offloading can offload ~65% of total traffic load. • This is without using delayed offloading at all. • Delayed Offloading only gained 2-5% efficiency when 100 seconds used • Admittedly incredibly different from other findings on the same idea. • When upwards of an hour is used, the gain becomes ~29%. • On-the-spot offloading can achieve 55% energy savings due to reduction in transfer times. • Once again, 100 second delays offer only 3% energy savings gain. • Increasing delays to an hour the gain increases by 20%. • A prediction based offloading strategy (such as Breadcrumbs) must predict over several minutes to be useful. • Interconnection time can be as long as 40 minutes, making prediction hard. • Average completion time of data transfers is much shorter than delay deadlines. • Even with delayed offloading, uploading a 30 MB video is still faster than using a 3G network.

8. Experiment Setup • Uses an application called Dtap that records WiFi connectivity, and sends recorded data to servers. • Scanned every 3 minutes for an AP. • Records GPS location as well as duration, data rate, and time. • Does not perform offloading in and of itself. • Why? Offloading for arbitrary data such as this drained too much battery. • 97 volunteers who own iPhone 3G/3GS in Korea were asked to use Dtap for a period of 18 days. • Diverse occupational background and various major cities used. • Collected 705 valid daily traces.

9. Temporal coverage per user, time and hourly mobility.

10. Key Observations • Temporal Coverage • Performance of offloading highly depends on the time duration a user stays in a WiFi covered area. • Average coverage across all users are 70% for all day, and 63% for daytime only. • Differences caused by users who are more likely to have WiFi coverage at home. • Different from other findings. Why? • Other reports use measurements through war-driving, and do not account for natural mobility of a user. • Users typically spend far more time at home or an office than traveling. • To prove this, they record traveling distances as well. (See Figure 3c)

11. Key Observations (Cont.) • Spatial coverage measured as well. • The fraction of an area that is under any WiFi coverage. • Only given a rough estimate as users do not naturally walk around the entire area. • Does give a usable lower bound. • Conclusion : About 8.3% is spatially covered. • Combined Findings • Temporal coverage is 3.5~8 times larger than spatial coverage. What does this mean? • Indicates that most users stay inside a WiFi network for a long time once connected. • Average connection time is 2 hours for all day, and 52 minutes for daytime only.

13. Findings (Cont.) • End-to-End Rates • Average data rate is ~1.97Mbps • Average is however highly skewed by night time. • Highest data rates are during night, around 2.76 Mbps, and 1.26 Mbps during the day. • Why? Users are more likely to be connected to personal, home APs during the night. • Findings point to offloading during the night to be very effective because of this. • Provided users can accept a delay of data transfer until night. • User mobility has a lower correlation with data rate than temporal coverage.

14. Offloading Efficiency • With the findings and tracings, a simulation can be created. • Using the simulation, Offloading Efficiency is defined as the total bytes transferred via WiFi, divided by all the bytes generated. • To further understand mobile traffic, projection data released from CISCO is used.

15. Offloading Efficiency (Cont.) • The amount of traffic offloaded to WiFi from a 3G network is then measured. • Experiment assumes that all transfers of video and data are delayed. • Surprisingly, on-the-spot offloading also achieves extremely high offloading efficiency. • Further more, due to the above, if most mobile traffic is placed onto smart phones, 65% of data traffic can be offloading to WiFi automatically. • Why? Average users spend more time in WiFi zones than traveling between them ala war-driving. • With delayed added, offloading efficiency increases substantially. • 100 seconds or less was negligible. • Long deadlines can bring efficiency up to 88% • Admittedly unrealistic, as users may ignore delayed transfers and opt for on-the-spot only.

16. Completion Time • Deadlines of 30 minutes to an hour may be unrealistic. • Findings do indicate that most transfers finish long before this. • Example. Photos given a 60 second deadline finished only 6 seconds after the same transfer without offloading. • For larger files, users may complete in the same time with delayed offloading as they would using no offloading at all. • Delayed Offloading has a longer completion time than on-the-spot offloading. • However it uses 3G networks far less. • Under certain circumstances (bad or little WiFi access), delayed can be faster than on-the-spot.

17. Energy Savings • There is a fundamental trade-off between energy consumption and delay transfers. • 3G networks more widely available, but transfer slower. • Result? More battery powered is used to transfer a file. • Using delay offloading, WiFi can be used, so less time and battery is spent on transferring. • Energy consumption per minute using 3G or WiFi is roughly the same • Main difference comes in as a difference in transfer time.

18. Findings

19. Traffic Types • Bursty, small-sized traffic benefits the least from delayed offloading. • Larger, bigger-sized traffic got the most benefit from delayed offloading. • In terms of type of traffic, texts and photos benefited the least from delayed, and more from on-the-spot. • Video and Multimedia backups benefited the most from delayed offloading. • Regardless of data type, both benefited from some form of offloading.

20. Impacts of WiFi Deployment • For simulation, the deployment found during testing was used, then slowly thinned out by eliminating APs. • Two elimination methods were used, one random, and one based on the activity of an AP. • Eliminating half of the APs using the one based on activity, getting rid of the last used first had little effect. • Eliminating half of the APs through a random means halved offloading efficiency. Why? • Most of the traffic went through popular APs, such as coffee shops, offices, or other public APs. • Findings. Implies that careful deployment plans can yield improvements in capacity without increasing density.

21. On this paper… • Strengths • Relatively large sized study, and used that information to run effective simulations. • Very detailed reports on their findings • Many different visual means to convey information was used. • Informative and keeps a constant comparison to other papers in the same field. • Weaknesses • Rather redundant, some points are mentioned and proven multiple times. • References other papers via name/number, but does not offer a quick blurb as to what the other paper had found. • Are the findings useful? Paper admits offloading is already handled by smart phones automatically. • Mentions that findings give concrete evidence that mobile companies can save money on data transfers and how much they approximately can save, but why would a company lower prices anyhow? • Does not really consider efficiency in areas that are not urban, or don’t have easily accessible public WiFi APs.

22. Future Work • Paper was relatively new, coming out in 2010. • Events since its release. • KDDI (Japan) rolled out the world’s largest Wi-Fi based offload network. • http://www.mobiledataoffloading.com/mobile-data-offload-news/520/ • First Wi-Fi system that can deliver Gigabit capacity and outdoor speeds of 450 MBPS released by Winncom. • http://www.mobiledataoffloading.com/mobile-data-offload-news/504/ • Company iPass formed, offering Open mobile Exchange Platforms to integrate Wi-Fi with 3G and 4G Networks. • http://www.mobiledataoffloading.com/mobile-data-offload-news/498/ • Next Offloading Conference will be in the 26th of March 2012.