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

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 Sharif University of Technology

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 Sharif University of Technology

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 Sharif University of Technology

Future Network View Cloud processing architecture IoT and large number of nodes and resource sharing options Dumber nodes and more central control 5 Sharif University of Technology

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 Sharif University of Technology

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 Sharif University of Technology

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 Sharif University of Technology

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 Sharif University of Technology

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 Sharif University of Technology

SON Functionalities 11 Sharif University of Technology

Big Data and Network Optimization 12 Sharif University of Technology

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 Sharif University of Technology

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 Sharif University of Technology

3 Key Aspects of Big Data Analytics Machine Learning Statistics Graph Analysis and Signal Processing Big Data Database Systems and Parallel Processing 15 Sharif University of Technology

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 Sharif University of Technology

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 Sharif University of Technology

II. Extract Machine Learning view Behavior mining: user, packet, network levels Signal Processing view Network Signal Processing and Adaptive Networking 18 Sharif University of Technology

III. Exploit Smart Resource Allocation Smart Handovers between Hetnets Policy and Charging Engines 19 Sharif University of Technology

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 Sharif University of Technology

Thanks! 21 Sharif University of Technology