A Computer ScientistLooks at (Energy) Sustainability

Randy H. KatzUniversity of California, Berkeley

NSF Sustainable Energy-EfficientData Management Workshop

2 May 2011

1

Working Definition

• Sustainability: strategies that meet society’s present needs without compromising the ability of future generations to meet their own needs

• Satisfaction of basic economic, social, and security needs now and in the future without undermining the natural resource base and environmental quality on which life depends– Energy, Water, Natural Resources, Environment, … – Green Manufacturing, Transportation, Agriculture, ...– Increase efficiencies and minimize bad side-effects

• Use less, use what you need, use better

2

Role ofInformation Technology

• “Energy permits things to exist; information, to behave purposefully.” W. Ware, 1997

• Observe-Analyze-Act– Observe: Sense, Monitor, Collect– Analyze: Organize, Model, Infer, Plan– Act: Control, Actuate, Execute, Manage

3

Energy “Spaghetti” Chart

4

Qua

ds (1

015 B

TUs)

http://www.eia.doe.gov2008 data 4

Sources and LoadsDispatchable Sources

Oblivious Loads

Non-Dispatchable Sources

Aware Loads 5

Supply- versus Load- Following

Most expensive, least efficient energyLatency involved in bringing capacity on line

(or probability of exceeding) 6

BaseCapacity

IntermediateCapacity

PeakerCapacity

Load-followingSupplyDemand Response:

Incentivize reduced loads duringtimes of peak demand

Demand Side Management:Shift demand to reduce peak loads,e.g., Supply-following Loads

Load Duration Curve

7

21st Century Energy Infrastructure

• Energy: the limited resource of the 21st Century• Role of IT: Information Age approach

– Overarching Principle: bits follow current– Pervasive actionable awareness of energy availability

and consumption, on fine time scales• Exploit information to match sources to loads,

manage buffers, integrate renewables, signal demand response, and take advantage of locality

8

Inst

rum

enta

tion

Mod

els

Controls

Building OS

Plug LoadsLightingFacilities

BuildingIn

stru

men

tatio

nM

odel

s

Routing/Control

Grid OS

Demand ResponseLoad Following

Supply Following

Grid

Facility-to-Building

Facility-to-Building

Facility-to-Building

Gen-to-Building

Inst

rum

enta

tion

Mod

els

Control

CompressorScheduling

Temperature Maintenance

Supply-FollowingLoads

Storage-to-Building

Inst

rum

enta

tion

Mod

els

Power-AwareCluster Manager

Load Balancer/Scheduler

Web ServerWeb App Logic

DB/Storage

Machine RoomMR-to-Building

Energy Networks

Gen-to-Grid

uGrid-to-Grid

Building-to-Grid Building-

to-Grid

Datacenter as aSupply-Following Load

1. Degree of Freedom: On-demand + scheduled workloads

2. Principle: Power proportionality from non-power proportional components

3. Sustainability: Maximize use of renewable sources

9

Supply-side Challenge:Wind

• High variability of wind energy is an impediment to its large-scale penetration in traditional Grid/Load architectures

Single Location Multiple Locations vs. Time of Year

Std dev / mean+/-11MW in 20 min

10

11

Load-side Challenge:Power Proportionality

• Scheduling agility: workload awareness and resource allocation• Wikipedia interactive workload + HPC batch workload

Time

Static Load Provisioning:100% overprovisioning over worst expected case

Dynamic Load Arrival

Dynamic Capacity Management

Over- provisioning or Spinning Reserve

Work Capacity

Energy-AwareSystem Architecture

12

Energy Usage

Price/Renewable Energy

Wake/Sleep Utilization, Response Times,Dropped Requests

Add/Remove NodesDelay/Degrade Jobs

Database / SAN

Load Balancer /Job Scheduler

Clients

TRANSITION

AWAKE

SLEEPING

Web AppWeb AppWebServerWorkload/

Request Statistics

Web AppWeb AppAppServer

Web Server

Web ServerBatch Server

Inte

rnet

Power Subsystem: Metering, Distribution, Battery ControlGrid

Electrical Power

Cluster Manager

Load Monitor

Workload Prediction

Cluster Provisionin

g

EnergyPolicy

Charge/DischargeBattery

Observe Analyze Act

Server Efficiencies

Operating Range

Server Class Machines (similar figure for netbook/embedded class nodes)

13

Better Better

Measured

Effectively Scaling Work Capacity and Power

14Measured experiment on LoClu

Information Overlay to the Energy Grid

17

Conventional Electric Grid

GenerationTransmission

DistributionLoad

Intelligent Energy Network

Load IPS

Source IPS

energy subnet

Intelligent Power Switch

Conventional Internet

Aware Co-operative Grid

18

• Observe-Analyze-Act:• Deep instrumentation• Waste elimination• Efficient Operation

• Shifting, Scheduling, Adaptation

• Forecasting• Tracking• Market

• Availability• Pricing• Planning

Power Proportional Cluster as a Model Systemapplied to the Smart Grid—now distributed

19

Smart Buildings

CoryHall

SodaHall

SDH

Smart Buildings

BMS

Cyber Physical Building

Ligh

tTr

ansp

ortProcess

Loads

Occupant Demand

Legacy Instrumentation & Control Interfaces

Pervasive Sensing

Activity/UsageStreams

BIM BITS

PIB

Activity Models

Multi-Objective Model-Driven Control

Building IntegratedOperating System

External

HVA

C

Ele

ctric

al

Faul

t, A

ttack

, Ano

mal

y D

etec

t &M

anag

emen

t

Control Plan and Schedule

Physical Models

Human

-Buil

ding

Interf

ace

20Observe – Analyze – Act

Building Observation

21

22

Envir

onm

enta

lOpe

ratio

nal

Building Analysis

23

Lighting

Servers

PDUs, CRACs

BuildingAnalysis

HVACIT and Plug Load

Soda Hall@Berkeley:IT-intensive CS Building

Building Action

24

Soda Hall

Etchevery

Hall50 Ton Chiller 200 Ton Chiller

10 months 2 months

Scott McNally Bldg Manager

Building OS

25

sMAP: Simple Measurement and Actuation Profile for Physical Information

StreamFS: StorageSystem for energysensor data

Scheduling and Slack/Supply-following Loads

26

Summary• Awareness of Load and Supply

– Load-Following: match load with managed supply– Demand Response: reduce load to meet supply– Supply-Following: schedule work to exploit

knowledge of available supply—essential for non-dispatchable sources like wind and solar

• Key idea: make information actionable– Observe-Analyze-Act– Information overlay on cluster, machine room,

building-scale “grids”– Interface sensors, facilities, clusters, and buildings

to information buses at a variety of scales

27

Conclusions• Smart Clusters, Smart Buildings, Smart Grids

– Use less energy• Right provisioning for expected + reserve vs. peak

– Use the energy you need:• Power proportionality

– Use better energy• Integrate renewables

28

Thank You!