시스코가 소개하는 만물인터넷 시대의 데이터 애널리틱스의 가치! 슬라이드에 대한 자세한 발표 내용은 시스코 코리아 공식 블로그(www.ciscokrblog.com/768) 나 네트워크 사이트(http://apjc.thecisconetwork.com/site/content/lang/ko/id/4537) 확인해 보실 수 있습니다.

1. © 2013 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 1© 2013 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 1
Rajiv Niles
Director, Digital Transformation & IOE Acceleration
Nov 2015

2. 1995 2000 2013 2020
“Fixed” Computing Mobility / BYOD
All over IP
Internet of EverythingInternet of Things
200M
10B
50B
People
Process
Things
Data
Third Wave of Internet

3. Instrumented
Industrial Machine
Big Data Analytics
Physical and
Human Networks
Secure, Cloud-based
Network
THINGS
PEOPLE
PROCESS
DATA

4. Source: Cisco Consulting Services
BecauseDataisMassive,Messy, and Everywhere
50BDevices Connected
by 2020

5. It’sHuge, but How Huge?
• IoT Hype Has Surpassed Big Data
• Press Has Increased 10X in One Year
• This Is Not a Market
• This Is a Collection of Loosely Related Markets
• CAGR of IoT 2013-2020 Is Projected to Be 35% per Year

6. IsThistheIoTMarket?
• Commercial / Industrial Will Pass
Consumer Around 2018
• Until 2020, Consumer IoT Will Be Greater
• Long Term, Commercial / Industrial Will
Be 2X Consumer
In # of
Devices
• Until 2020, Consumer IoT Will Be Greater
• Long term, Commercial / Industrial Will
Be 2X Consumer
In $

7. Christian Horner
Team Principal, Infiniti Red Bull Racing
“The network will play a crucial part in how we
develop the car; gathering data, learning from it
and adapting will ultimately determine our season.”

8. Edge
Devices
Data ComputingTiers
OptionalFog Nodesfor BetterPerformance
Edge
Devices
Data Center
and
Applications
IoT Data System
Fog
Node
Fog
Node
Data Center
and
Applications
3 or More Tiered System2 Tiered System

9. IoTWorldForum ReferenceModel
Levels
Application
(Reporting, Analytics, Control)
Data Abstraction
(Aggregation & Access)
Data Accumulation
(Storage)
Edge Computing
(Data Element Analysis & Transformation)
Connectivity
(Communication & Processing Units)
Physical Devices & Controllers
(The “Things” in IoT)
Collaboration & Processes
(Involving People & Business Processes)
1
2
3
4
5
6
7
Sensors, Devices, Machines,
Intelligent Edge Nodes of all types
Center
Edge

10. Internetof Things DesignIssues
Levels
1
2
3
4
5
6
7
Sensors, Devices, Machines,
Intelligent Edge Nodes of all types
Center
Edge
IT
OT
Query
Based
Event
Based
Data at
Rest
Data in
Motion
Non-real
Time
Real
Time

11. BridgingITand OT:
IntroducingIoT“Edgeware”
Devices
Edge
Edge
Computing
Device Control
• Configure (from the device provider)
• Status (from the device provider)
Device Interactions
• Discovery
• Addressing
• Protocol conversion
Middleware
• Listeners (Zigbee), brokers (MQTT)
• Event grouping / batch interactions
Data
• Normalize (standardize codes for the app)
• Filter (against pre-set criteria from the app)
• Expand (decode/expand cryptic codes)
• Aggregate (generate statistics)
• Notify/alert (to the app)
Combine the functions above
• Schedule (when to comm with the device)
• BPM (when multiple steps are needed)
Security
• Roles
• Privileges
An individual edge
software function may
serve many applications
Edge software can be sourced
completely separately from
the vertical application

12. Key Points:
• IT – OT
• Decoupling
Issue:
Devices may generate data
faster than apps can ingest it
Devices
Apps
BridgingITand OT: HandlingtheVolumeof Data
Levels
1
2
3
4
5
6
7
Sensors, Devices, Machines,
Intelligent Edge Nodes of all types
Center
Edge

13. IoEAnalyticsIntroducesNew Complexities
Key Issues:
• The velocity and volume of data may be huge
• In some cases, most of the data is unimportant
Traditional Capture
Data
Store
Data
Analyze
Data
Edge Computing
Becomes Crucial
IoT Store
Data
Analyze
Data
Capture Data
from Devices
Reduce Data
at the Edge

14. MachineDataMeetsAnalytics

15. Typical DataFlowsacrossCDVand CDP
BIAnalytics Self-Service
ESBs and
Apps
Cisco Data Virtualization
Big Data / IoT
Sources Cloud Data Sources
Traditional Data
Sources
Cisco Data Preparation
End-user Data Sets
1.IT-curated enterprise data
identified and imported
using Business Directory
and CIS
2.Business analysts bring
their personal data sets
3.Additional Big Data and
Cloud sources imported
4.Business analysts iteratively
explore, clean and enrich
data
5.Prepared data used within
analytics to drive business
results
6.CDP answer sets promoted
to IT / shared via CIS and
Business Directory
1
1
1
2 3
4
5
6

16. Requirementsfor anIoEPlatform
Reusable
Modules
Framework
For Edge Processing & Data Transport
Options
Fast Data, Deep Data, Streaming Data
Flexible
For Integration with IT
Easy
GUI Tools
Completely Open System
Multiple Vendors