A Sentient NetworkHow High-velocity Data and Machine Learning will Shape the Future of the Communication Services

OPNFV Summit, Burlingame CA, November 9-12, 2015

Wenjing ChuDistinguished EngineerDell ResearchMember of the Board and TSC of OPNFV

A paradigm shiftNFV/SDN is empowering

Towards … ?

“The more real-time and granular we can get, the more responsive, and more competitive, we can be.”

Peter Levine | Andreessen Horowitz

A Sentient Network

1Elastic on-demand capacityOpen software architecture promises flexible elastic capacity that can be rapidly provisioned and dynamically managed

Data-driven operation automationVirtualization unleashes the latent value in the real-time data to optimize resource allocation and assure SLA

Scalable infrastructureStandard open architecture infrastructure delivers capacity, cost efficiency, and right-sized reliability

2

3Self-learning security and privacySelf-learning algorithms from real-time data delivers ultimate security and privacy at the same time

4Machine intelligent user servicesAdvances in Machine Learning promise continuous improvements in user experience

5

New Paradigm Shift in Infrastructure: NFV/SDNDomain specialized software on standard hardware, delivered from the cloud- Dramatically cuts CapEx & OpEx- Enhances service velocity- Enables Big Data driven business model

High-velocity Cloud Empowers Business Transformation

Mobile' Infrastructure

Content' Distribution

Edge Computing

High'Velocity'Cloud

Packet Velocity• 100X more performance• 50X more customers

Service Velocity• Deploy services in minutes vs months• Empower new, innovative business

models

Data Analytics Velocity• Sub-second real-time streaming

analytics• Sentient intelligence

High-velocity Data with Machine Learning

Telemetry, IoT

sensors, System logs,

Monitors, Mobile

devices …

Transmission of data in streams

Transformation

Learning in real-

time

Action on

intelligence

A Closer Look: Data Analytics Velocity

“Meta Dimensionality” of Data

Gigabyte, Terabyte, Petabyte, Exebyte, Zettabyte, Yottabyte

uSec

, mSe

c, S

ec, m

in, d

ays,

mon

ths,

year

s…

SizeTi

me

Let’s look at some examples in networking…

Learn to optimize resource management

Automatically Adjust Resources to Maintain SLA

Systems can respond to usage spikes in real-time, to reallocate resources and maintain SLAs.

Continuous Resource Optimization by Reinforcement Learning

! Modeled as a Markov Decision Process

! Learning probability distribution by Bayesian inference

! Q-Learning, Deep Q-Network

! Consensus optimization

Wikipedia: MDP

Learn to defeat intruders

Classification by Concept Adapting Decision Tree

! Rules programming is labor intensive, error prone, static

! Let algorithm learns a DT (or a forest) on its own

! Concept adaptability: incorporate new, forget old

Packets > 10

yes no

Protocol=http

Packets > 10

noyesBytes > 60k

yes no

Protocol=ftp

Data stream

Data stream

Uncovering Unusual Hidden Activity by Monitoring Entropy

! Entropy in a moving time window captures the normal humming of the system

! Out of ordinary move of entropy plus context suggest attack vs. flash crowd

Clustering Users based on Behavior Patterns

! Non-parametric model can be used for latent features, overlapping clusters and infinite data

! Eg Dirichlet process, Gaussian process

! A cluster of ‘users’ of abnormal behavior are suspects

Learn to better service customers

! Mining telco CDR’s to evaluate risks from customer churn

! Combining location and real-time system info to pinpoint quality issues

! Machine learning algorithm offers more precision

Proactive Customer Support and Retention

The peaks indicate areas of highest risk with more precision than traditional linear regression (the dotted line).

Creative commons http://scicomp.stackexchange.com/

Collaborative Learning by Sensing User Mood

Facial expressions

Pulse rate

Skin conductivity

Brain computer interface (BCI)

Voice pitch

Remote UX metrics

Media audience response

Improve MOOC, CBT

VR/AR styleUI

20

“How is Seamless Mobility powered by High Velocity Cloud?” Seamless Mobility by Contextual Learning

Live machine learning algorithms ensure quality, security and seamless mobility.

High-velocity Cloud

High-velocity Analytics

Learn to protect privacy

Differential Privacy in Big Data and Machine Learning

! Anonymization is not enough

! Differential Privacy (!-DP) provides a formal guarantee & a mechanism for tradeoff

! DP may also help avoid False Discovery

Dr.Katrina Ligget, CalTech

Computing on and Learning from Encrypted Data

transformed+queryplain+query+

under+passive+attack

Applicationdecrypted+results

encrypted+results

DB+server

encrypted+DBProxy

SecretSecret

computation+on+encrypted+data+≈+

regular+computation

! Stores+schema++and+master+key

! No+query+execution

trusted+client?side! Data loss is prevalent

everywhere you look! Data privacy

responsibility is unclear! Practical system can be

deployed with strong encryption without the risk of key disclosure

! Different algorithm for different computation

Dr. Laruca Popa, UC Berkeley

So, Any Takeaways for OPNFV ?

• Collect data• Put data in an open format• Consider privacy and security on day one• Don’t tie data to a specific implementation of a specific design• Must consider the time dimension of data, e.g. TSDB, streaming

“The future is already here – it’s just not very evenly distributed.”William Gibson