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Taming User-Generated Content in Mobile Networks via Drop Zones

Ionut Trestian Supranamaya Ranjan Aleksandar Kuzmanovic Antonio Nucci Northwestern University Narus Inc. Taming User-Generated Content in Mobile Networks via Drop Zones. http://networks.cs.northwestern.edu. http://www.narus.com. Powerful New Mobile Devices.

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Taming User-Generated Content in Mobile Networks via Drop Zones

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  1. Ionut Trestian Supranamaya Ranjan Aleksandar Kuzmanovic Antonio Nucci Northwestern University Narus Inc. Taming User-Generated Content in Mobile Networks via Drop Zones http://networks.cs.northwestern.edu http://www.narus.com

  2. Powerful New Mobile Devices • The iPhone 4 has a 5 MP camera • The HTC Evo has a 8 MP camera

  3. Online Social Networks • Social network websites among the most popular websites on the Internet • User desire to create virtual records of their lives using photos, videos, sounds

  4. 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”

  5. Postponed Delivery – Drop Zones • Assume users can tolerate upload delays (we will show later that this is indeed the case)

  6. 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

  7. Research Questions Where to place Drop Zones such that they absorb the most content possible? What is the relationship between postponed content delivery intervals users can tolerate and needed infrastructure?

  8. Outline • Technical details • Mobile user behavior • Algorithmic details • Evaluation • Further Implications

  9. Trace Technical Details Close to 2 million MMS images, videos etc uploaded by 1,959,037clients across the United States during a seven day interval

  10. Trace Technical Details Base Station 1 1. Intra-session movement 2. Inter-session movement Base Station 2 RADA Start (contains BSID) RADA Update (contains BSID) RADA Stop (contains BSID) RADIUS Server Therefore we have a snapshot of user presence across locations (base-stations)

  11. Outline • Technical details • Mobile user behavior • Algorithmic details • Evaluation • Further Implications

  12. Location Ranking Comfort zone 3 All users spend most of their time in their top 3 locations

  13. Sending Probability vs. Location Rank Most of the sending also happens in their top 3 locations

  14. Sent Content over Base-Stations Certain base-stations popular but not overly

  15. Users Already Delay Uploads 40% of uploads at least 10 hour old

  16. Outline • Technical details • Mobile user behavior • Algorithmic details • Evaluation • Further Implications

  17. 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

  18. Drop Zone ILP Formulation

  19. Outline • Technical details • Mobile user behavior • Algorithmic details • Evaluation • Further Implications

  20. Greedy vs. Optimal Our algorithm stays within 2% of Optimal over all time spans

  21. Greedy vs. Simple Heuristic Our algorithm compared to a simple popularity heuristic

  22. Required Infrastructure Main metric, savings in infrastructure

  23. Average Content Delay Average delay experienced a lot lower than set target

  24. Average Distance to Drop Zone Average distance actually grows as more Drop Zones are added

  25. Average Number of Pieces Batched Batching content leads to energy savings

  26. Outline • Technical details • Mobile user behavior • Algorithmic details • Evaluation • Further Implications

  27. Further Implications • Content size keeps increasing, how long until the next upgrade? • What if we had higher coverage radio technology?

  28. Increase in Content Size This gives 14 years under LTE assuming content doubles each year

  29. Higher Coverage Radio 65% of content 2 km away !

  30. Missed Opportunities More opportunities with more infrastructure !

  31. 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

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