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
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.
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.
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
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.
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.
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.
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.
Temporal coverage per user, time and hourly mobility.
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)
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.
•
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.
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.
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 onthe-spot only.
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.
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.
Findings
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.
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.
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.
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.