edge analytics: use cases for industry 4 - profi ag · cisco confidential 18 use cases / case...
TRANSCRIPT
Theo Ruland Data and Analytics Group, Cisco
April 19th 2016
Edge Analytics: Use Cases for Industry 4.0
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Data & Analytics Portfolio
Edge Analytics
Demo
Agenda
Use Cases
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The Digital Economy is Disrupting Everything
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Data Integration & Management
Data Preparation
Data Virtualization
Edge Analytics
Connected Streaming Analytics
Workload Automation
ServiceGrid Asset Management
Collaboration Analytics
Network Deployment
Analytics
Contact Center
Analytics
Network Compliance Analytics &
Management
Analytics to Support Cisco Architectures
Energy & Asset Optimization
Process Integration & Workflow Automation
Energy Analytics &
Management
Site Analytics
Service Provider Analytics
Analytics to Support Vertical Industries
Data Center Analytics
ParStream
Cisco’s Data & Analytics Portfolio
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Edge Analytics
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Unstructured Data Structured Data Data Streaming at the Edge
Traditional Data Warehouse Big Data Store
A New Approach is Needed to Reach and Analyze That Data
REAL-TIME LONG-TERM SHORT-TERM Make a Decision NOW Make a Decision Today Plan for a Business Decision
“Most data will be processed at the edge” (mobile devices, appliances, routers)
37%
Widely Distributed, Streaming, Short Shelf Life, Too Big to Move Digital Enterprises Create Data at the Edge
Three years from now, where will most data generated by IoT solutions be processed?
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Cisco Streaming Analytics - CSA CSA is an embeddable, horizontal and hierarchically scalable distributed in-memory streaming database designed and built for analytics at the edge: • CSA runs in routers, switches, small compute and commercial servers
• The streaming engine enables multi-stream, concurrent processing of “raw streams” and the generation of multiple “derived streams” – from simple aggregates to advanced machine learning algorithms
• Interfaces allow ANSI SQL northbound for applications, pluggable framework analytics models and data source connectors
.
Seamless Query over Live and Stored Data
In Memory Processing, with an option to commit to integrated storage
Generate Events
Visualize Analyze, Export
Raw Streams Generate Derived Streams
!
Integrated Querybase
CSA applies predicates, aggregations, and joins with metadata tables, contextual data to identify anomalies and detect trends:
101011110100100101010101010 100100101010101010101101010100100101010101010101010110
1010111101001001010101010100100100101010101010101 10101001001001010101010101010101010010010010101011010101010101001001001010101010101010101001100100
1010111101001001010101010100100100101010101 1010100100100101010101010101010101001001001010101101010101010100100100101010101010101010
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Cisco ParStream
Cisco ParStream is a distributed massively parallel processing columnar database designed and built for the Internet of Things. Its small footprint enables implementation at network edges and inside the network, as well as in data centers. Its innovative compression and bit mapped indexing technologies enable Cisco ParStream to meet IoT historian and buffering requirements—such as providing sub-second response times on billions of data records and thousands of columns, while continuously importing new data.
• Continuously import high-velocity data with low latency
• Analyze and filter billions of records
• Query data structures with thousands of columns
• Get answers in milliseconds, without Cubes
• Execute thousands of concurrent queries
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ParStream Responds to Distinct Requirements for IoT
REAL-TIME INSIGHTS Use cases require near Real Time Analytics
millisecond query response time
DATA GROWTH Data is growing faster and bigger because of number of sensors
10B+ rows 5TB+
FAST DATA Data streamed from sensors requires fast ingestion
1M+ rows per sec
EDGE ANALYTICS IoT data is mostly generated at the ‘Edges’ of the network
1000+ locations
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Choose Your Database Based on Your Use-case
< 1..10 ms
1 min
1 hr
10..100 ms
1 sec
10 min
4 hrs
Res
pons
e Ti
me
Big Data
Massively Parallel (MPP) Real-Time
Hadoop OLTP Reporting
In-Memory DB
Gigabyte Terabyte Petabyte
OLAP Batch-Analytics
Real-Time IoT Analytics
Stream-Analytics
Operations Analytics
Complex Event Processing
High
Low
Cisco ParStream
Cassandra
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SQL API / JDBC /ODBC
Immediate Queries
In-Memory and Disk Technology
Multi-Dimensional Partitioning
Massively Parallel Processing (MPP)
Shared Nothing Architecture
3rd Generation Columnar Storage
High Speed Parallel Loader with Low Latency
C++ UDx API
ParStream’s Patented Technology Provides a Competitive Advantage
Lockless Architecture Enables ultra-fast query and data import performance
Massive Parallel Processing Delivers linear scalability and high query throughput
Small Footprint Enables analytics at the edge with a low TCO
High Performance Compressed Indexes Provide ultra-high query performance
High Performance Compressed Index (HPCI)
2
3
4
1
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Delivering actionable insights requires use-case specific applications & data integration
12
Network Analytics
Query
4 billion records
4 billion records
M2M pattern – siloed vertical solutions POS
Analytics
Query
POS POS POS
Network Analytics
Big Data pattern – horizontal integration POS
Analytics ?
Network
Cloud / Data Center
Client Fraud Analytics
Source POS POS POS
No Data Integration
Individual Solutions
Dedicated Ressources
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Delivering actionable insights from IoT data requires big + fast data, real-time analytics and decentralization
13
Network Analytics
Big Data pattern – horizontal integration POS
Analytics ?
Network
Cloud / Data Center
Client Fraud Analytics
Source POS POS POS 50 billion devices
Shared WAN
Network Analytics
IoT pattern – de-centralization at scale POS
Analytics ?
Fog
Cloud / Data Center
Client Fraud Analytics
Edge
Source POS POS POS
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Geo-Distributed setup substantially reduces network traffic enabling continuous monitoring (sampling proofed insufficient)
Database
More Than 20 Billion Records Returned
Query Search Results 40 Records Found
4 Billion Records
Central Analytics Intelligence
Query Search Results 40 Records Found
7 Records
Hybrid Edge / Centralized Intelligence
<100 Records
Overcoming Bandwidth Limitations and Reducing Report Delays Requires Analytics to be Pushed Closer to the Data Source
Fog Computing Delivers Real-time Insights by Minimizing Network Traffic
4 Billion Records
4 Billion Records
4 Billion Records
4 Billion Records
18 Records
5 Records
12 Records
8 Records
ParStream ParStream ParStream ParStream ParStream
Application Application
ParStream Geo-Distributed Server ParStream
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Analytics At the Edge Architecture
• Systems & Databases • Command & Control • Infrastructure Management • Services Management • Business Systems
• Data Actuation & Enrichment
• Control & Action Engines • Contextual & Semantic
Engines
• Infrastructure Events & Logs • Network Events & Logs • Application Events & Logs • Wi-Fi /Mobile Events & Logs • Sensors/MT Connect • ESB’s • Flume/Thrift/Kafka, etc.
Streaming Data Connected Streaming
Analytics Trulink
Streaming Connector Framework
Real-time Enrichment & Sessionization
Services Framework
APIs
Handler
Handler
Handler
Handler
DV
Connected Streaming Analytics TruCQ
Real Time Analytics
ANSI SQL on Streams
In-Memory Models
Fast inserts and Edge Storage for Raw Data & Historical Aggregates
Analytics & Reporting Apps
Real-Time Dashboards
Visualization Framework
Real-Time Service API
Report Builder
• Cloud Services • Web Apps • Mobile Apps • Monitoring & Alerts • Action Engines
Embedded Postgres
Raw Data
Raw
Dat
a
Aggregates
Shared Stream Query Processor
Advanced Analytics
Raw Data
ParStream
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Edge Node
Fog Node
Data Center
Cloud
IoT Devices
Preparing for the “Hyper-distributed” World
Routers
Fog Nodes (UCS)
Network Switches
Data Centers
Cloud
Many times you want to store and analyze the data locally before sending northward
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Cisco’s Plans
Cisco’s Plans is to Dominate the IoT Market • Include ParStream as a critical component in our IoT framework • Use ParStream in several existing and new IoT customer projects • Use ParStream to help forge new IoT partnerships
Cisco Does Not Plan to Enter the Database Market • Continue to sell ParStream opportunistically outside IoT • Do not promote ParStream as standalone database
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Use Cases / Case Studies IoT Analytics at the Edge
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IoT Enterprise Use Case: Oil & Gas
Edge Location
Regional
Processing Platforms Fog Processing
Sensors & Edge Processing
Data Center
Headquarters IT Central
Offshore Platform
HQ
Edge Analytics with CSA
Data Platform and Applications
Deployed on Cisco Devices
Deployed in Customer Data Center or Regional Center
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Oil & Gas Analytics Use Case
Temp. Link
Flow Link
Flow Link
Temp Link
Broker
Broker Link
Link
Historian Database
Connected Streaming Analytics
Broker Broker
Link
Dashboard
Data Lake
Data Virtualization
Edge Location Sensors & Edge Processing
Processing Platforms Fog Processing
Regional Data Center
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Customer Proof Point for IoT in Renewable Energy: Real-time Analytics for Wind Turbines
Business Challenge • Optimize wind turbine performance by quickly adjusting to changing
environmental factors (e.g., wind direction, temperature, etc.) • Minimize turbine downtime thru predictive maintenance.
Use Case • Real-time and continuous monitoring of data from 20,000 wind turbines
(each with 150 sensors), including analysis of 30TB of historical data
ParStream‘s Technology Value Proposition • Real-time monitoring of continuous data-flow for immediate insights/actions • Historical analysis thru enabling storage and analytics in an integrated
platform by immediately importing and storing readings from turbines.
Benefits/Results (estimated) • 15% improvement in productivity • Decreased downtime • $158M of annual economic benefits
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How it works with ParStream - Technical Setup
Once per day Every 1 minute
Tool 1 Tool 2 Tool 3
Cisco ParStream • Turbine information • Historical information • Environmental information • others
• The devices at the windfarms stream aggregated data into the cloud
• Aggregated data is stored in the cloud and feeds software programs
• Granular data is kept at the windfarm for inspection and advanced analytics
• Previously data was uploaded daily, with ParStream upload every minute
• Data about weather, locations, prices etc. adds context and meaning to turbine data
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Mazak SmartBox Industry-Shaping Collaboration
• The SmartBox is a mini electrical cabinet about the size of a typical household medicine chest
• It can be mounted on the side of a machine enclosure; this enables the box to be connected to a machine tool in several ways
• Cisco's real-time analytics capabilities are embedded into a Cisco Industrial Ethernet (IE) 4000 switch inside SmartBox; measuring vibrations and temperature
• It provides real-time visibility and insights into data right on the factory floor
• Access to real-time manufacturing data to improve overall productivity and responsiveness to customer / market changes
• Identify and easily fix downtime-related inefficiencies to improve overall equipment utilization
Expected Business Results About SmartBox Cisco Solution
In October 2015, we announced an industry-shaping collaboration with Mazak Corporation and MEMEX on “SmartBox,” a technology that enables real-time data and analytics from manufacturing machines.
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Cisco IE4k Switch
IoE Analytics
Data Sources: PLC I/O PAC
Machine Data
Cisco Analytics Dashboards
HMI Integration Dashboards & Alerts
MTConnect
XML
Sensor Data
JSON
Process Manufacturing: Machine Protect
Operator alert when machine parameters cross normal threshold
Real time machine stats with baseline recommendations
Trends that indicate potential machine failure
Use Case
Manufacturing Machines
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FogNode
JDBC
ConnectedStreamingAnaly2cs
MTCHandler
StreamingModelScoring
AnomalyAlert&DataExport
ModelConfigura@on
API
DataIngest
AlertStream
Media@on
CSA
WebUI
MTConnectAgents
MC1 MC2
MC3 MC4
JDBC
hLps
HCN8800
E1060
HCN8800
Orbitec Cisco IE4k Switch
ParStream
Mazak Edge Analytics Architecture
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Streaming Analytics on Mazak’s Smart Box
IE4000 Switch Cisco Streaming Analytics
Software Inside
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Business Results
Identify and easily fix machine downtime
Increase equipment utilization
Improve productivity
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