02-08-2014, 01:35 PM
Mobile Data Offloading : How much can WiFi deliver?
[attachment=66507]
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.
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
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.
[attachment=66507]
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.
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
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.