the edge: evolution or revolution? - acm-ieee-sec.orgacm-ieee-sec.org/2017/edge computing sec...
TRANSCRIPT
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November 2016
The Edge: Evolution or Revolution?
Pablo Rodriguez, CEO
alpha.companyOctober 2017ACM SEC, Symposium on Edge ComputingSan Jose, California
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2Pablo RodriguezTelefnica Digital
Internet Design
Lacked: Mobilty Security & Privacy Economics Sense that the Internet was free
Content Distribution
2
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P. Rodriguez, Scalable Content Distribution in the Internet, Ph.D. Thesis, EPFL 1997-2000
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4
Overlays Evolution
Hype
Realism
Growth
CachingIP Multicast
CDNsAkamai
Enterprise CDNsEdge Insertion
Layer-7 SwitchesSatellite CDNs
Video
1999
2000
2001
2002
2003
2004
Disappointment
Computing
P2P
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5
My Upbringing EPFL/Eurecom Inktomi, Adquired by Yahoo! Netli, now part of Akamai Tahoe Networks, now part of Nokia Bell-Labs Microsoft Research Telefonica
Impact
research
prototype
launch
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Three Edge Example projects
1. Wireless Proxies (Bell Labs) 2. P2P Content Distribution (MSFT) 3. Video CDN (Telefonica)
Large File
Two peers,left peer is twice faster
6 pieces1 2 3
654
1 2 3 4 5 6
Client
Content
IP
BSS
Core Network
GGSN W-PEP
Application Optimizations(e..g. compression)
TCP Optimizations
(e.g. connections sharing, ACK regulator)
MAC optimizations
(e.g. Qos, FEC, scheduling)
Session Optimizations(e.g. DNS Boosting)
(1)
(2)
(3)
Windows Updates Telefonica Video CDN
Wireless Proxies
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Telefnica Alpha / Edge Computing / 7
1Back to the Future
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The Problem The Internet has been growing very fast, both in
the number of users and in the available content Overloaded servers and network links. Frustrated users.
The World Wide Wait
The Web can kill the Internet, B. Metcalfe 1995
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Not ready for Flash-Crowd Events
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P. Rodriguez, Scalable Content Distribution in the Internet, Ph.D. Thesis, EPFL 1996-2000
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Centralized
Origin Server
Caches
Name: www.foo.com IP: 192.12.12.5
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Hierarchical
Origin Server
Caches
Name: www.foo.com IP: 192.12.12.5
Hierarchical Caching Overlay
P. Rodriguez, E. Biersack, Web Caching: Hierarchical vs Distributed Caching, IEEE/ToN 1999
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1 2 65
P2P File Distribution
Server
3 4
1 5 6 2 4
1 2 3 4 5 6
3
T. Karagiannis et al., Planet Scale Software Updates, Sigcomm2006. Windows Updates
P. Rodriguez, E. Biersack, Parallel Access for Mirrors in the Internet, Infocom 2000
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Nano Data Centers
Home Edge Cloud functionality
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THE CLOUD
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November 2016
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Univac video
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Connected devices have never collected so much data about their environment (vision, location, temperature...). That real-world data is massive(e.g. a self-driving car generates about 10 gigabytes per mile).Pushing it back to the cloud will become increasingly difficult. Existing infrastructures will not be able to handle its volume.
IoT objects become more and more sophisticatedTHE ADVENT OF EDGE COMPUTING
UGC IoT
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24Telefnica Alpha / Edge Computing /
1. An enormous amount of data is already being generated
10 Gb / mile 400 Gb / secLytro Cinema VRAutonomous Car
THE ADVENT OF EDGE COMPUTING
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Two trends that will disrupt the cloud paradigmTHE ADVENT OF EDGE COMPUTING
Increasing data Real-time needs
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Challenge #2: How many Edges?
1-20 20-80milliseconds milliseconds
1
Visual-tactile feedback
ms Human Features
ms
Network & Spatial
ms
Use CaseRequirements
1
1
Tactile Internet
5G (target)
1
Car at 140 km/h, 3.9 cm
Wearable Assistance
4
Tell that a sound is a human
voice
7
Vestibulo-ocular reflex
Visual reaction time
1
Speed of light per 200 km
5
Augmented Reality (gaming)
7
Autonomous Driving
5
Two people in a room, latency added by speed
of sound
8
Car at 90 km/h, 20 cm
TV max inter picture
Virtual Reality
20
WiFi (average)
20
An airbag to release
30
RTT UK N-S (IP) (564 miles)
30
Perception of a screen change after clicking a
button
50
4G/LTE (average) 70
RTT USA W-E (IP) (2514 miles)
80
Screen to brain propagation
100
Auditory reaction time
130
3G (average)150
Human blink
150
Crisp UI with a computer
80
Augmented Reality
(non gaming)
100
TeleRobotics
Telefnica Alpha / Edge Computing / July 2017
10
10
10
10
80-150milliseconds
>150milliseconds
THE ADVENT OF EDGE COMPUTING
Many edges, per use case and workload
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November 2016
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November 2016
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Evolution of Video traffic (Global)
Peak Internet traffic will grow at a compound annual growth rate of 35% from 2016 to 2021, compared to 26% for average Internet traffic.
CDNized content will grow to 70% globally
+20pp
Incremental Edge Computing Examples
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Virtual CDNs at the EDGE
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Propriety and ConfidentialProduct Innovation . Customer Centric Networks
Access Aggregation Core Peering
Access
AccessAggregation
Aggregation
Aggregation
X.000.000 X00 X0 2-4>10
CDNs Today
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Propriety and ConfidentialProduct Innovation . Customer Centric Networks
Access
Aggregatio
nCore Peering
Access
Access
Aggregation
Aggregatio
n
Aggregation
X.000.000 X00 X0 2-4>10
CDNs tomorrow Virtual CDNs at the Edge
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Propriety and ConfidentialProduct Innovation . Customer Centric Networks
The connected home of the past
InternetCPE
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The connected home of today
http://www.ericsson.com/res/docs/2014/virtual-cpe-and-software-defined-networking.pdf
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CPE
IPv4 NAT
SwitchAccess Point ModemDHCP
FWTR-069 UPnP
STB
SwitchAccess Point Mdem
CPE FW
TR-069
NAT
UPnP
DHCP
IPv4/IPv6
STB
FROM
TO
Home environment
Home environment
Network environment
Network environment
Simplification removes all incompatibilities
New functions (e.g. IPv6) only needed in network environment
Operation and service deployment are greatly simplified
Innovation in services: The vCPE Principle
Credit: Antonio Elizondo, Diego Lopez, Telefonica GCTO
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Challenge #3: Where is the killer app?THE ADVENT OF EDGE COMPUTING
Edge computing is still in its infancy and a framework to facilitate its adoption is not yet available.
Some use cases are only emerging (e.g. VR/AR will be a niche for some time and industrial IoT is a very narrow vertical).
How to create the market demand? Nobody will develop apps that require
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The killer app will come from the sweet-spot between bandwidth & latency.
latency
bandwidth throughput1Gbps
1 ms
1000 ms
100 ms
10 ms
Virtual Reality
Tactile Internet
Autonomous DrivingAugmented
Reality
Most suitable-for-the-edge use cases are heavy on video and computer vision
Emerging Robotics
THE ADVENT OF EDGE COMPUTING
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First, its the AI, stupid.ENABLING EDGE COMPUTING
AI-powered applications will catalyze the adoption of edge computing. It is all set to become the most preferred architecture for running data-driven, intelligent applications.
IDC estimates that today, only 1% of application across all industries have some type of cognitive technology. In two years that number will exceed 50%.
Edge computing prioritizes agility over power. Endpoints will never be as powerful as the cloud can be. On the other hand, they gain agility from the speed of the information loop that occurs in the edge, processing just the information that is needed.
The cloud will then become a place where learning happens.
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AI Fields and Applications
Use input from sensors to deduce aspects of the world. Computer vision. Speech, facial and object recognition.
Recognise, interpret, process, and simulate human affects, emotions and social skills.
SENSE
INFER
ACT
Perception
Social Intelligence
Representation of "what exists, qualification and commonsense.
Read and understand the languages that we speak. Machine translation.
Systems that identify/assess creativity or generate outputs that can be considered creative.
Knowledge Representation
Natural Language Processing
Creativity
Robots to be able to handle such tasks as object manipulation and navigation.Motion and Manipulation
Make logical deductions dealing with uncertain or incomplete information.Reasoning and
problem solving
Algorithms that improve automatically through experience.
Set goals and achieve them. Also in cooperation (swarm intelligence).Planning
LEARN
ENABLING EDGE COMPUTING
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Secondly, specialised hardware will be neededENABLING EDGE COMPUTING
To enable edge computing, besides AI processors and algorithms, theres the increasingly important task of creating engineering systems to maximise performance.
New and specialised chips and systems are needed to take AI to the next level. Boutique chips will be developed to deliver better performance and massively reduce training requirements and improve costs. The objective is to build computational platforms that deliver the performance and energy efficiency needed to build AI with a maximum level of accuracy.
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Telefnica Alpha / Edge Computing / 44
3Data Control at theEdge
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Biggest risk for the Web: Losing control of Data,
Sir Tim Berners-Lee 2017
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Personal Data at the Edge is far more valuable than aggregated data. The more private/intimate and the closer to the context of the user, the more valuable it becomes.
$0.10 $0.18 $0.62 $6.50 $32.15 $54.50
Anonymous profile
with 1 identifier
Anonymous profilewith 34
identifiers
Value per "friend" per
Facebook profile
Location Data per
contact in DB
Demographics data
per contact
Buying Behavior &Preferences
Increase in value ofcontact as it becomes attached to an identity
x2 x6 x65 x320 x545
Source: Atos Group
Papadopoulos et al., If you are not paying for it, you are the product, IMC 2017
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Revenue comparisons
World GDP = $70,000 BTelco Revenue (>50% Wireless) = $2,000BPersonal Data (Ads) Revenue = $500BVideo/TV Revenue = $182BTransit Revenue (Level 3) = $6 BAkamai 2011 Revenue = $1.2 B
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Edge Functionality Evolution
Application Improvements(e.g. Compression, Caching, Edge insertion, Cyber Security)
Protocol Optimizations(e.g. Delay-jitter algorithm, ACK regulator, Pacing)
Analytics/Algorithms(e.g. Vision, AI/ML as a service, Big Data, Cognitive Decision, Situational awareness)
Privacy/Data Control(e.g. IID protection, Data Control, Anti Tracking, Transparency, Data Banks)
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EDGE TREND: In-Network Privacy Control Functionality
PARENTAL FILTERMALWARE
VIRUSAD MANAGEMENT
PRIVACY DATA PROTECTBLOCK OF TRACKING
IDENTITY TRANSLATION-PROTECTION
Naylor et al, McTLS: Enabling secure in-network Funcionatlity in TLS, Sigcomm 2015
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Personal Data: Give Data and Value Back
Individuals leave data traces (location, calls, web traces, shopping, etc)Such info is useful for Credit Ratings, Retail Industry, Govs, Online
Advertising, Predictions and Analytics (Sexual Orientation, Political Views)Individuals have multiple virtual Data Souls and PersonalitiesIndividuals with access and control of that data could use it to their benefitWe still live in a data desert: we dont know much about ourselves or the
world (where do people like me go, where is it safe, where can I find a job)Give value back to our customers
- For sale: Your Data: By you, Hotnets 2011
Erramii et al, For sale: Your Data: By You, Hotnets 2011
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Personal Data Banks
- My Data Soul, P. Rodriguez TEDx Talk- www.rodriguezrodriguez.com
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Telefnica Alpha / Edge Computing / 64
4 Main Takeaways
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Its about moving computation and storage closer to where data is created and acted uponIts so far being transformationalThere will be many edges
The disruptive opportunities will come from use cases that require high bandwidth and low latency; mainly video and computer vision.
The Edge will enable AI in Real Time as a service
Specialised Hardware will be the catalyser
It will require really huge investments and cross industry strategic partnerships with sticky relationships. It will not happen by chance.
Privacy, Security and Data Value and control will drive a large percentage of Edge use cases
The edge in a nutshellMAIN TAKEAWAYS
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alpha.company
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November 2016
Slide Number 1Internet DesignSlide Number 3Overlays EvolutionMy UpbringingThree Edge Example projectsSlide Number 7The ProblemNot ready for Flash-Crowd EventsSlide Number 10CentralizedHierarchicalP2P File DistributionSlide Number 14Slide Number 15Slide Number 16THE CLOUDSlide Number 18Slide Number 19Slide Number 20Univac videoSlide Number 22Slide Number 23Slide Number 24Slide Number 25Slide Number 26Slide Number 27Slide Number 28Slide Number 29Slide Number 30Slide Number 31Slide Number 32Slide Number 33The connected home of the pastThe connected home of todayInnovation in services: The vCPE PrincipleSlide Number 37Slide Number 38Slide Number 39Slide Number 40Slide Number 41Slide Number 42Slide Number 43Slide Number 44Slide Number 45Slide Number 46Slide Number 47Slide Number 48Slide Number 49Slide Number 50Slide Number 51Slide Number 52Revenue comparisonsSlide Number 54Slide Number 55Slide Number 56Slide Number 57Slide Number 58Edge Functionality EvolutionSlide Number 60 EDGE TREND: In-Network Privacy Control FunctionalityPersonal Data: Give Data and Value BackSlide Number 63Slide Number 64Slide Number 65Slide Number 66Slide Number 67