Taming User-Generated Content in Mobile Networks via Drop Zones

Taming User-Generated Content in Mobile Networks

via Drop ZonesIonut TrestianSupranamaya RanjanAleksandar KuzmanovicAntonio Nucci

Northwestern UniversityNarus Inc.

http://networks.cs.northwestern.edu http://www.narus.com

Ionut TrestianTaming User-Generated Content in Mobile Networks

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The iPhone 4 has a 5 MP cameraThe HTC Evo has a 8 MP camera

Powerful New Mobile Devices

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Social network websites among the most popular websites on the Internet

User desire to create virtual records of their lives using photos, videos, sounds

Online Social Networks

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Current Cellular Networks Cannot Cope

AT&T officials warned that the Internet will “not be able to cope with the increasing amounts of video and user-generated content being uploaded”

Most providers are changing billing plans to address this problem

The current efforts conducted by some providers are focused on “educating customers about what represents a megabyte of data and improving systems to give them real-time information about their data usage”

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Postponed Delivery – Drop Zones

Assume users can tolerate upload delays (we will show later that this is indeed the case)

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Drop Zones

Certain locations will have better connectivity(e.g. 4G)

Client Side - Application running in the background, users upload content, they are given the option to delay

Network Side - Device that intercepts delayed uploads and schedules them over the backhaul link

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Research Questions

Where to place Drop Zones such that they absorb the most content possible?

What is the relationship between postponedcontent delivery intervals users can tolerate and

needed infrastructure?

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Outline

Technical details

Mobile user behavior

Algorithmic details

Evaluation

Further Implications

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Trace Technical Details

Close to 2 million MMS images, videos etc uploaded by 1,959,037 clients across the United States

during a seven day interval

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Trace Technical Details

1. Intra-session movement

RADA Start(contains BSID)

RADA Update(contains BSID)

2. Inter-session movement

RADA Stop(contains BSID)

RADIUSServer

BaseStation 1

BaseStation 2

Therefore we have a snapshot of user presence across locations (base-stations)

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Outline

Technical details

Algorithmic details

Evaluation

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Location Ranking

All users spend most of their time in their top 3 locations

Comfort zone

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Sending Probability vs. Location Rank

Most of the sending also happens in their top 3 locations

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Sent Content over Base-Stations

Certain base-stations popular but not overly

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Users Already Delay Uploads

40% of uploads at least 10 hour old

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Outline

Technical details

Algorithmic details

Evaluation

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Drop Zone Algorithmic Details

Placement problem, what base-stations to collocate Drop Zones at so that we cover the most content possible

This is an NP hard set covering problem

We adapt a greedy solution – in each step select the remaining base station that can cover the most content until all content is covered

We compare our greedy solution with an ILP we implemented in cplex

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Drop Zone ILP Formulation

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Outline

Technical details

Algorithmic details

Evaluation

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Greedy vs. Optimal

Our algorithm stays within 2% of Optimal over all time spans

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Greedy vs. Simple Heuristic

Our algorithm compared to a simple popularity heuristic

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Required Infrastructure

Main metric, savings in infrastructure

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Average Content Delay

Average delay experienced a lot lower than set target

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Average Distance to Drop Zone

Average distance actually grows as more Drop Zones are added

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Average Number of Pieces Batched

Batching content leads to energy savings

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Outline

Technical details

Algorithmic details

Evaluation

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Content size keeps increasing, how long until the next upgrade?

What if we had higher coverage radio technology?

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Increase in Content Size

This gives 14 years under LTE assuming content doubles each year

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Higher Coverage Radio

65% of content 2 km away !

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Missed Opportunities

More opportunities with more infrastructure !

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Conclusions

A Drop Zone architecture reduces infrastructural deployment requirements

Our approach can effectively tame the exponentially increasing user-generated content surge for the next 14 years, under the LTE technology assumption

Slight increases in radio technology coverage can bring substantial gains

Taming User-Generated Content in Mobile Networks via Drop Zones

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