1. به نام خدا
Big Data and a New Look at Communication Networks Babak Khalaj Sharif University of Technology Department of Electrical Engineering

2. Big Data Trend.. Explosive Growth of Data: from terabytes to petabytes
Lot of information “hidden” in the data that is not readily evident and is never analyzed
Wide range:
Traditional: Web (Google, Amazon,…), e-commerce…
Science: Remote sensing, bioinformatics, scientific simulation, …
Smart Grids
Telecom Operator databases
Our focus is on exploiting Big Data in a new front: “Communication Networks” 2
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3. Mobile Operators view: Financial side
Segments addressed so far through Big Data:
Optimizing and monetizing mobile data
Mobile data fraud and revenue assurance
Approaches:
Statistical data summarization and feature extraction
Clustering in high dimensional spaces 3
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4. Can we further expand the Big Data view in performance side?
Key question:
Does “big data” knowledge really help in hierarchical structures?
Large part of our knowledge in such networks is “local”
Already tried to depart from a central structure and moved toward “distributed” algorithms
It seems “big data” is not really aligned with such direction!
So, lets revisit our view of future mobile networks.. 4
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5. Future Network View Cloud processing architecture
IoT and large number of nodes and resource sharing options
Dumber nodes and more central control 5
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6. Physical Distribution: RRH and Cloud-RAN
Digital Unit (DU) pooling
Joint processing through CoMP, joint scheduling and joint interference alignment/cancellation
Optimization not just at wireless level any more (includes fiber backbone as well) 6
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7. Alignment of Telecoms and IT
Software Defined Networking (SDN):
APIs (application programming interfaces) used for network configuration
Network Function Virtualization (NFV):
Commodity hardware is used to run networking functions in software 7
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8. Vision of BD in Communication Networks
Issues to note in our analytics view:
Growth of mobile broadband
Enhanced connectivity and adoption of M2M
Power of cloud computing
Easy and cheaper access to big data technologies
Can not store all data in a large database and run offline queries on top
Achieving real-time and predictive analytics 8
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9. Big Data in Communication Networks
Large communication networks always dealt with “Big Data”
Channel state over a wireless network coverage area
Users usage patterns and context information
User data transported by the network
Data generated by network elements
Million Dollar Question: Ignoring the complexity for now, can we exploit big data to improve network performance and reduce operator costs? 9
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10. Back to Future View: SON side
Self Organizing Networks (SON): SON makes more sense as we also combine it with Big Data:
Big data to make networking smarter is just the beginning of what the explosive growth in information is going to provide in terms of efficiency.
Cognitive view of wireless networks now goes far beyond just frequency sensing/utilization
Mobile data traffic explosion-> operators must „densify“ their network (HetNets)
SON is key For HetNets (Traffic Steering: making the resources appear as “one network”) 10
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11. SON Functionalities 11
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12. Big Data and Network Optimization12
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13. Back to the other side of the coin..
So, clouds now provide new path in processing large amounts of data
Need for a new look at how we design, operate and optimize our hierarchical networks:
Local: Select and gather information
Local-Global information exchange
Global: Process information
Global-Local: Send back action plans 13
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14. Issues to consider: Privacy issues for access to Big Data
Probing: Practicality of access to Big Data distributed over network with nodes that can not extract local data
LTE has the standards for data interfaces but this is not true for GSM or 3G and small cells.  14
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15. 3 Key Aspects of Big Data Analytics
Machine Learning
Statistics Graph Analysis and Signal Processing
Big Data
Database Systems
and Parallel Processing 15
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16. First Step to Address Big Data: Partitioning
Identify Sources of Big Data: Information Sources and/or Network States
Extract the Information
Machine Learning View
Signal Processing View
Exploit the Information through proper actions 16
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17. I. Identify the Source/Network State
At what “scale” to look at data?
Macro vs. Micro view
How to sample data?
Sampling domain, type and rate 17
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18. II. Extract Machine Learning view
Behavior mining: user, packet, network levels
Signal Processing view
Network Signal Processing and Adaptive Networking 18
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19. III. Exploit Smart Resource Allocation Smart Handovers between Hetnets
Policy and Charging Engines 19
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20. Conclusion
Access to Big Data inevitable in future communication networks (fixed and mobile)
More centralized processing through different levels of cloud computing
Business Intelligence goes beyond financial benefits to technical benefits
How to accommodate big data in real-time remains a big challenge.. 20
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21. Thanks! 21
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