This document discusses the growth of the Internet of Things (IoT) and the rise of fog computing. It notes that: - 50 billion devices are expected to be internet-connected by 2020, up from 12.5 billion in 2010, representing rapid growth. - Most of the world's data is now being generated by IoT devices like sensors and smart objects, creating big data challenges. - Fog computing is a new distributed computing model that processes data at the edge of the network, near the data sources, to help address these challenges. It extends cloud computing out to endpoints and access networks. - Open source software will be important for fog computing and IoT, as it has been

1. © 2014 Cisco and/or its affiliates. All rights reserved. 1© 2014 Cisco and/or its affiliates. All rights reserved. 1
Michael Enescu
CTO/Head of Open Source Initiatives
March 14, 2013

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• How did we get here: virtualization
• What is Fog Computing and IoT
• Where are we going: the “Open” future of IoT

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7.26.8 7.6
Rapid Adoption rate of
digital infrastructure:
5X faster than electricity
and telephony
50Billion
Internetconnectedthings
50
2010 2015 2020
0
40
30
20
10
Billionsofdevices
25
12.5
Inflection
point
Timeline
Source: Cisco IBSG, 2011

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*Cisco VNI Study 2012
of “things” are unconnected
99%
Traffic Growth
4xTransition to Cloud*
Mobility
Wi-Fi 50%of Traffic (Video over Mobile Devices)*
The Network
Has to
Change
Intelligent
Device Growth
2.5/Person
BYOD
Programmable
Mobile and Cloud
Simple
The Network Is the Platform to Connect the Previously Unconnected

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Internet
of Things
Cloud
Any
Device
Video
Virtual
Mobile
Packet
Switched
Routed
Network as
Platform
Bridged
Unconnected
DC
PC
Voice & Data
Dedicated
Fixed
Circuit
Shared
The Network Is the Platform to Connect the Previously Unconnected

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Centralized -> Decentralized
Decentralized -> Centralized
Fixed, role based model
Easier ops model, new apps
New devices, P2P, M2M
Dedicated compute loads
On-Demand, XaaS, AAA
New PIN’s, improved protocols
Mainframe & Client-Server
Virtualization & Cloud
IoT & Fog
1st 2nd 3rd
Moore Nielsen Prediction
Power+computereachAAA
Centralized -> Decentralized

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• Storage and Compute declining faster
• Network scales very differently than compute
Sensors will evolve faster than bandwidth
Distributed computing more compelling over time
• Data gravity?
Computation
Storage
Communication
Moore’s and Nielsen’s predictions hold

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1.1x109 dp/day
Data points generated by sensors.5x1012 B/week
Data generated by an offshore oil rig
1x1012 B/day
Data generated by an oil refinery
2x1013 B/hour
Data generated by a jet engine
90% of the world’s data created in last 2 years
IoT = Small sensors + Big Data + Action
IoT Traffic will grow at 82% CAGR through 2017*
brings new dimensions we are barley beginning to sense

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• Networking is changing:
50B+ Devices coming – Immense amount of data
No longer about “data transport”
Moving to “intelligence about data”: Understanding and taking actions
• Analytics are changing:
Massive data => can not move data fast enough to analytics => move analytics to the data
Real-time actions => processing compute closer to the source
• Consequently three new trends emerge:
Dramatic growth in number of applications (+optimization/specialization) for analytics at the edge
Dramatic growth in the computational complexity to ETL only essential data information to the core
The drive to instrument the “data” to be “open” not closed/locked-in (more on this later)

10. Traditional model: Store First, Query Later
• Fetch,
• Analyze,
• Report
!
Generate Actionable Events,
Integrate with Policy/Mgmt System
Store raw data or filtered data for
further mining.
Data in Motion model: Process First, Store Optional
Input Data
Input Data
Rules can express:
• Predicates and Filters
• Contextual/Dimension Data
• Aggregations
• Pattern Matching
• Categorization & Classification
• Sub-queries …
• Fetch,
• Analyze,
• Report
Data-base waiting for Queries
Query-base waiting for Data

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Time
Present
Data Sources
M2M Gateway
Intermediate Server/
Gateway
Data Center
Achieved Data
Achieved Data
Achieved Data
Data-Mining, Machine Learning,
Pattern Recognition, Cause-Effect, etc.
Future
Predict

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Cloud
Device/Smart Object
North/South Flows East/West FlowsFog
Fog Nodes can be multi-tenant
Shared, public or private (like cloud)
Highly virtualized environment
Secured & isolated tenants, QoS, workload distribution
Mixed ownership & operation
Single entity, federation of agencies
Service Mobility
Ability to migrate a running instance from cloud to edge
Fog is the distributed, hierarchically
organized platform where the Internet
meets the physical world at M2M scale

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• Operating System: Linux – by far the most successful open source project ever
• Virtualization:
Hypervisor: xen, kvm, …
Network: OpenDaylight Controller, OVS, NFV, …
• Cloud: Open Stack, Cloud Stack, oVirt, …
• Applications: …
• Internet of Things:
Eclipse M2M – over a dozen new projects: kura, krikkitt, …
Linux Foundation AllSeen
Multiple P2P

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• “All IoT Software will be Open Source”
• Why?
Open Source = collaboration (at the largest possible scale)
Open Source = credibility
Open Source dominates virtualization:
Hypervisor, OS, FS, …
Cloud compute is a direct consequence of virtualization
Of the physical machine (compute, memory, storage, I/O), the physical network
Open Source dominates development environments, applications:
Mobility, Data, Big Data, Analytics, Security, …
Fog as an extension of the cloud is no exception
From gateways, to smart devices, do any device, to sensors
Though more work and collaboration is needed
• And where is Open Source already in the Internet of Things?

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@michaelenescu
Everywhere!
Thank You