Taming User-Generated Content in Mobile Networks via Drop Zones Ionut Trestian Supranamaya Ranjan Aleksandar Kuzmanovic Antonio Nucci Northwestern University Narus Inc.

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones 2 The iPhone 4 has a 5 MP camera The HTC Evo has a 8 MP camera Powerful New Mobile Devices

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones 3 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

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones 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 4

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Postponed Delivery – Drop Zones 5 Assume users can tolerate upload delays (we will show later that this is indeed the case)

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones 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 6

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Research Questions 7 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? What is the relationship between postponed content delivery intervals users can tolerate and needed infrastructure?

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Outline Technical details Mobile user behavior Algorithmic details Evaluation Further Implications 8

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Trace Technical Details 9 Close to 2 million MMS images, videos etc uploaded by 1,959,037 clients across the United States during a seven day interval Close to 2 million MMS images, videos etc uploaded by 1,959,037 clients across the United States during a seven day interval

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Trace Technical Details Intra-session movement RADA Start (contains BSID) RADA Update (contains BSID) 2. Inter-session movement RADA Stop (contains BSID) RADIUS Server Base Station 1 Base Station 2 Therefore we have a snapshot of user presence across locations (base-stations)

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Outline Technical details Mobile user behavior Algorithmic details Evaluation Further Implications 11

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Location Ranking 12 All users spend most of their time in their top 3 locations Comfort zone 3

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Sending Probability vs. Location Rank 13 Most of the sending also happens in their top 3 locations Most of the sending also happens in their top 3 locations

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Sent Content over Base-Stations 14 Certain base-stations popular but not overly Certain base-stations popular but not overly

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Users Already Delay Uploads 15 40% of uploads at least 10 hour old 40% of uploads at least 10 hour old

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Outline Technical details Mobile user behavior Algorithmic details Evaluation Further Implications 16

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones 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 17

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Drop Zone ILP Formulation 18

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Outline Technical details Mobile user behavior Algorithmic details Evaluation Further Implications 19

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Greedy vs. Optimal 20 Our algorithm stays within 2% of Optimal over all time spans Our algorithm stays within 2% of Optimal over all time spans

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Greedy vs. Simple Heuristic 21 Our algorithm compared to a simple popularity heuristic Our algorithm compared to a simple popularity heuristic

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Required Infrastructure 22 Main metric, savings in infrastructure

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Average Content Delay 23 Average delay experienced a lot lower than set target Average delay experienced a lot lower than set target

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Average Distance to Drop Zone 24 Average distance actually grows as more Drop Zones are added

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Average Number of Pieces Batched 25 Batching content leads to energy savings Batching content leads to energy savings

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Outline Technical details Mobile user behavior Algorithmic details Evaluation Further Implications 26

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Further Implications Content size keeps increasing, how long until the next upgrade? What if we had higher coverage radio technology? 27

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Increase in Content Size 28 This gives 14 years under LTE assuming content doubles each year

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Higher Coverage Radio 29 65% of content 2 km away !

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones Missed Opportunities 30 More opportunities with more infrastructure !

Ionut Trestian Taming User-Generated Content in Mobile Networks via Drop Zones 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 31