Hadoop As The Platform For The Smartgrid At TVA

Hadoop as the Platform for the
Smartgrid at TVA
August 26, 2010

Topics

• Introduction
• Retrospective on the openPDC project
• What Is Hadoop?
• Current Smartgrid Obstacles
• Cloudera Enterprise as The New Smartgrid Platform
• Summary

Copyright 2010 Cloudera Inc. All rights reserved 2

Today’s speaker – Josh Patterson

• josh@cloudera.com
• Master’s Thesis: self-organizing mesh networks
• Published in IAAI-09: TinyTermite: A Secure Routing Algorithm
• Conceived, built, and led Hadoop integration for the
openPDC project at TVA
• Led small team which designed classification techniques
for timeseries and Map Reduce
• Open source work at http://openpdc.codeplex.com
• Now: Solutions Architect at Cloudera


What is the openPDC?

• The openPDC is a complete set of applications for
processing streaming time-series data in real-time
• Measured data is gathered with GPS-time from multiple input
sources, time-sorted and provided to user defined actions,
dispersed to custom output destinations for archival
• NERC funded
• Started at the Tennessee Valley Authority (TVA)
• Now in use by many government controlled power
companies around the world


openPDC Topology


openPDC: Why?

Northeast Blackout of 2003
• Significant failure of US power grid in 2003 due to cascading
effects
• SCADA provided a limited at best view of what happened
• NERC mandated that companies collect high resolution data
and store for later analysis
• Power grid in US is aging rapidly, cost of needed overhaul is
significant


How “Big Data” Challenged the openPDC Project

“We Need More Power, Scotty”

• Data was sampled 30 times a second
• Number of sensors (Phasor Measurement Units / PMU) was
increasing rapidly (was 120, heading towards 1000 over next 2
years, currently taking in 4.2 billion samples per day)
• Cost of SAN storage became excessive
• Little analysis possible on SAN due to poor read rates on large
amounts (TBs) of data


Major Themes for Storage and Processing Needs

• Scale Out, not Up
• Linear scalability in cost and processing power
• Robust in the face of hardware failure
• No vendor lock in


Storage Needs: The Data Deluge

• At 1000 PMU sensors we were looking at needing to store 500TB of data
• The Data Deluge
• “Eighteen months ago, Li & Fung, a firm that manages supply chains for retailers,
saw 100 gigabytes of information flow through its network each day. Now the
amount has increased tenfold.”
• http://www.economist.com/opinion/displaystory.cfm?story_id=15579717

• Internet of Things
• HP's Peter Hartwell: "one trillion nanoscale sensors and actuators will need the
equivalent of 1000 internets: the next huge demand for computing!“

Processing Needs: Needle in a Haystack

• The “Haystack” in PMU data typically involved in
scanning through TBs of info to find the one particular
event we were interested in
• RDBMs simply do not work with high resolution
timeseries data
• Need for Ad-Hoc processing on data to explore network
effects and look at how events cascade across the grid


The Solution: Hadoop

• A scalable fault-tolerant distributed system for data storage
and processing (open source under the Apache license)

• Two primary components
• Hadoop Distributed File System (HDFS): self-healing high-bandwidth
clustered storage
• MapReduce: fault-tolerant distributed processing

• Key value
• Flexible -> store data without a schema and add it later as needed
• Affordable -> cost / TB at a fraction of traditional options
• Broadly adopted -> a large and active ecosystem
• Proven at scale -> dozens of petabyte + implementations in
production today
Copyright 2010 Cloudera Inc. All Rights Reserved. 11

HDFS As Cheap and Scalable Storage

• HDFS is robust in the face of machine failure
• A big thing was cost – we could linearly grow our cluster
as needed by just adding new machines
• Ran on commodity hardware – we didn’t have to buy
expensive (and relatively slow), proprietary SAN setups


MapReduce Provides a Powerful Parallel Processing
Framework
• We found Map Reduce to be the perfect framework to
quickly process large amounts of PMU (timeseries) data
• Created a machine learning algorithm in Map Reduce
which detected “unbounded oscillations” in grid data
• Map Reduce based oscillation scan of a few TBs takes
minutes
• A scan of comparable data from a SAN would take days
or weeks


What is common across Hadoop-able problems?

Nature of the data
• Complex data
• Multiple data sources
• Lots of it

Nature of the analysis
• Batch processing
• Parallel execution
• Spread data over a cluster of servers
and take the computation to the data


What Analysis is Possible With Hadoop?

• Text mining • Collaborative filtering
• Index building • Prediction models
• Graph creation and • Sentiment analysis
analysis
• Risk assessment
• Pattern recognition


Benefits of Analyzing With Hadoop

• Previously impossible/impractical to do this analysis

• Analysis conducted at lower cost

• Analysis conducted in less time

• Greater flexibility


The Storm of the Data Deluge is Brewing

• Challenges of the openPDC project were just the first
wave
• Storage requirements are accelerating
• Disk speeds are relatively constant
• Seeing signs of data deluge, GE now using open sourced
Hadoop-based timeseries classifiers developed in the
openPDC project


Coming Power Grid Stressors

• Larger fluctuations in power demands
• Ex: Millions of new electric cars all charging in the evenings
• An aging power grid that requires more capital infusion
than most companies have allocated for these purposes
• Grid infrastructure is older than most realize
• Maintenance policies generally only look at age of equipment


The Power Grid Domain is Slow to Evolve

• Power companies are slow to adopt technology
• They generally have poor maps of their overall infrastructure
• Coming pressures are going to force power companies
to have to analyze TBs and PBs of data
• Ad-Hoc analysis will be needed to explore the complex
relationships in this data


Broader Emerging Smartgrid Themes

• Simply adding lots of sensors is only a very small part of
the solution
• Collection, storage, and processing are in themselves all
difficult problems
• In order to build a more effective Smartgrid, platforms
are needed that handle these things well
• Smartgrid sensor collection is a subset of the larger
undercurrent of emerging massive sensor based
systems


Even Broader Theme: Internet of Things

• We’re collecting sensor data everywhere, not just the
Smartgrid
• Many of the techniques described above can be easily
done with Hadoop
• Open Source generalized collector system is called “Flume”
• Examples:
• Weather sensors
• Mesh networks – battlefield UAVs
• Cell Phones – Google Android as a collector


Next Generation Sensor Platform: Hadoop and
Related Projects


The Companies That Provide Real Results for
Sensor Platforms Will Win
• Much of today’s Smartgrid talk is just hype
• Few “solutions” actually fix anything, only put sensors
on things
• Analysis is where the true value lies
• But you need a complete platform to be in position to analyze
the data


Harnessing Hadoop Has Its Challenges
Ease of use – command line interface only; data
import and access requires development skills

Complexity -- > 12 different components,
different versions, dependencies and patch
requirements
Manageability – Hadoop is challenging
to configure, upgrade, monitor and
administer
Interoperability – limited support for
popular databases and analytical tools


Cloudera’s Distribution for Hadoop, version 3
The industry’s leading Hadoop distribution

Hue Hue SDK

Oozie Oozie Hive
Pig/
Hive

Flume, Sqoop HBase

Zookeeper

• Open source – 100% Apache licensed
• Simplified – Component versions & dependencies managed for you
• Integrated – All components & functions interoperate through standard API’s
• Reliable – Patched with fixes from future releases to improve stability
• Supported – Employs project founders and committers for >70% of components

Who is Cloudera?

• Enterprise software & services company providing the industry’s
leading Hadoop-based data management platform
• Founding team came from large Web companies

• Products: Cloudera Enterprise & Cloudera’s Distribution for Hadoop
• All necessary packages, matched, tested and supported
• Tools to support production use of Hadoop
• The leading distribution for the enterprise

• Contributors and committers
• Fixing, patching and adding features

26

Hear More Examples @ Hadoop World 2010
http://www.cloudera.com/company/press-center/hadoop-world-nyc/

• 2nd annual event focused on practical
applications of Hadoop

• Date: October 12th 2010

• Location: Hilton New York Confirmed speakers from

• Keynote from Tim O’Reilly – founder
O’Reilly Media

• Pre and post conference training
available for Hadoop and related projects

• 36 business and technical focused sessions


Questions?


Hadoop As The Platform For The Smartgrid At TVA

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Hadoop As The Platform For The Smartgrid At TVA

Similar to Hadoop As The Platform For The Smartgrid At TVA (20)

More from Cloudera, Inc.

More from Cloudera, Inc. (20)

Recently uploaded

Recently uploaded (20)

Hadoop As The Platform For The Smartgrid At TVA