GREEN CLOUD COMPUTING

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Edited by Jun-Dong Cho

School of Information and Telecommunication Eng.

Sungkyunkwan University

Dec. 6 2012

Contents

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1. Basics of Cloud Computing

2. Green Cloud Computing- A Data Center Perspective

3. Energy Aware Data Center Management– Cyber

Physical Design Approach

Basics of Cloud Computing

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Software-as-a-Service (SaaS): A wide range of application services delivered via various business models normally available as public offering

Platform-as-a-Service (PaaS): Application development platforms provides authoring and runtime environment

Infrastructure-as-a-Service (IaaS): Also known as elastic compute clouds, enable virtual hardware for various uses

Cloud Computing

SOFTWARE AS A SERVICE-Consume it

PLATFORM AS A SERVICE-Build on it

INFRASTRUCTURE AS A SERVICE-Migrate to it

“Cloud computing is a large-scale distributed computing paradigm that is

driven by economies of scale, in which a pool of abstracted, virtualized,

dynamically scalable, managed computing power, storage, platforms, and

services are delivered on demand to external customers over the Internet.”

IaaS

Cloud Programming Environment and Tools, Cloud Hosting Platforms

Cloud Physical Resources: Storage, virtualized clusters, servers, network.

Scientific Computing, Enterprise ISV, Social Networking, Gaming

Amazon EC2, GoGrid, RightScale, Jovent

Animoto, Sales Force, Google Document

User Applications

User-level and infrastructure level Platform

Google AppEngine, MapReduce, Aneka, Microsoft Azure

Infrastructure

SaaS

P

aaS

Clo

ud

Econ

om

y

Several Benefits……

Autonomic

Elastic

Market

Oriented

(Pay-per-Use)

Virtualized

Service

Oriented

Dynamic

(& Distributed)

Shared

(Economy of

Scale)

Cloud Computing

No Up-Front Investment (open source) No Provisioning Delay No Idling Computing Resource

Easy to start an online service start-up

a software that

executes other

software as if it

was running on a

physical resource

directly.

Green Cloud Computing – Energy Star

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1. Take advantage of renewable energy

whenever it can.

2. Distribute workload based on local cooling

capacity to avoid creating hot spots

3. Skew workload and shutdown unused servers.

Big Data Processing

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= The Google File System (HDFS) +

MapReduce (Simplified Data Processing on Large Cluster)

www.apache.org

Cloudera

HortonWorks

Open Source Platform

Big Data Processing - Data Scientist

1.Math/Statistics

2.Algorithm/Programming Skill 1.Data Mining : Mahout, R 2.Opinion Mining 3.Social Network Analytics 4.Cluster Analysis

3.Passion and Understanding of Business

Big Data Processing: Success Applications

- Fraud Detection: Bank of America, Chase

- Netflix (25M+ users, 30M movie play per day,

3M searches/day)

- Bioinformatics - DNA: 120GB/human

(Cloudburst, Crossbow, Hadoop-BAM)

- Trulia (2013 IPO)

Exascale

Computing

Computing with next generation

fabrication/materials technologies

ultrafast, low-power light source for

on-chip data transmission.

Green

low-cost

data-centres

Access for all

through Clouds

The Road to Exascale Computing

5MW for one petaflop system

1.25GW , 2018

GPU? Nanophotonics

B. Brain vs E. Brain

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Brain Watson

(Morphy the robot, IBM) Road-runner (IBM)

Complexity 100 Billion

neurons

10 racks of IBM Power 750

servers with 2,880

processor cores

6,912 AMD x dual core

+ 12,960 IBM CELL

Performance

1 KHz (synaptic

rate)

100 Peta FLOPS

80 Tera FLOPS 1.7 Peta FLOPS

Power

Consumption 20 Watts 200 KW 3.9 MW Power

Memory 750 GB ~ 6.4 TB 15 TB of RAM 107 TB memory

Brain vs Computer

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Digital Neurosynaptic Core

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IBM & Cornell Univ. "A Digital

Neurosynaptic Core Using Embedded

Crossbar Memory with 45pJ per Spike in

45nm”

Green Cloud Computing- A Data Center Perspective

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What is in Datacenter?

Rack or Cabinet.

Servers (CPU, memory, …)

Storage (NAS, SAN, …)

Network Equipment.

Power Supply (Input Power Unit, Power Distribution Unit, UPS, …)

Air Conditioner.

Security.

Where do you Want to Compute Today?

10/08/2007 17

Big Four Data Centers

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AWS (Amazon Web Service)

Rackspace (Founder: Richard Yoo)

Azure (MS)

Google AppEngine (+ BigQuery: Big data analysis service)

Emergence of Mega Data Centers

Electrical & Mechanical Systems of Data Center

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A typical datacenter during dot com bubble

Facebook datacenter in Prineville Oregon

Rack (or Cabinet)

A standardized frame or enclosure for mounting multiple equipment modules.

Dimension

19 inches or 23 inches wide.

Height is measured in Rack Unit (“U”). 1U is 1.75 inches (4.45cm)

42U or 44U high are common.

Server

Servers can be 1U to 4U high.

Some companies design servers themselves

Facebook (open compute), Google, Amazon, …

Most will just purchase from Dell, HP and so on.

Facebook’s 1.5U server

Network Equipment

Placed in a rack or on top of a rack.

Handle different types of connections

Internal (topology is important)

External (Internet or other data centers)

Power Distribution Unit

Distributes power to racks

There are two types

Floor mounted

Rack mounted

Cooling – Raised Floor Goal is to keep 16 ~ 24°C But 26 to 27°C is ok according to

Google.

Cold Aisle Cool air is pushed out from floor

(needs power).

Hot Aisle Hot air is flowing upward and

sucked out.

Usually cables and AC will be under floor.

This is a traditional way

Cold Aisle Hot Aisle

Green Cloud Computing

- Sustainability

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What matter most to Google is “not speed but power”

Currently it is estimated that US data centers in

2010 consume upto 2.2% of total electricity usage.

(10% of total industrial electricity usage in Korea)

Server energy demand doubles every 4-6 years.

This results in large amounts of CO2 produced by

burning fossil fuels.

• Carbon emission due to Data Centers worldwide is now more than both Argentina and the Netherlands emission.

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Energy Cost Explosion

Server energy demand doubles every 4-6 years.

Reducing the operational costs of powering and cooling

Performance/Watt is not following Moore’s law.

Schedule VMs to conserve energy

Optimize data center designs to reduce power

Improve reliability

For every 10°C increase in temperature, the failure rate of a system doubles. Computing environment affected the correctness of the results.

Motivation for Green Data Center

Challenges

Power Consumption in the Datacenter

Computer Rm. AC 34%

Server/Storage 50%

Conversion 7%

Network 7%

Lighting 2%

Source: APC

Compute resources and particularly servers are at the heart of a complex, evolving system!

Where Does the Power Go?

Sustainable Data Center

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Data Centers (2008) - HP

Measuring computing energy efficiency

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Efficiency=work done

energy used inChips

×

Energy used inChips

Energy Provided toComputers

×

Energy provide to Computers

Energy entering

the building or 1/PUE

Data center energy efficiency = Underutilized Datacenters

cooling and power distribution, ineffecient cooling

Computer efficiency = Power supply dissipates 25-35% and DC-DC voltage

regulator can lose 25% of total energy.

Chip efficiency = MIPS/Watt

Power Usage Effectiveness(PUE)

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PUE = Overall Power Drawn by the facility

Power Delivered to the Data Centers (IT Load)

It is the responsibility of the IT manager to reduce the

consumption of IT load (renewing servers, virtualizing, etc.),

and the responsibility of the infrastructure manager to reduce the auxiliary consumption (more efficient equipment, free-cooling, etc.)

Green Cloud or Brown Cloud?

Cloud

datacenters

Location Estimated power

usage

Effectiveness

% of Dirty

Energy

Generation

% of Renewable

Electricity

Google Lenoir 1.21 50.5% Coal,

38.7% Nuclear

3.8%

Apple Apple, NC 50.5% Coal,

38.7% Nuclear

3.8%

Microsoft Chicago, IL 1.22 72.8% Coal,

22.3% Nuclear

1.1%

Yahoo La Vista, NE 1.16 73.1% Coal,

14.6% Nuclear

7%

• Ideally, for every server virtualized, save – ~$700 and ~7,000 kWh / year – 4 tons of CO2 emissions / year

• Plus – Power down underutilized physical servers, saving 40% – Desktop management, saving 35% / year

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Data Center Layout Outlet temperature Tout

Cold supply

temperature Ts

Inlet temperature Tin

Must less than 25C

Cooling – Penthouse

This is a new way used by Facebook and set up two floors within datacenter. Lots of innovations in this area.

Upper floor is used to direct air and lower floor contains racks.

Cool air enters thru the upper floor and falls down.

Hot air rises and goes out of exhaust air plenum.

Picture of upper floor (“penthouse”).

Liquid Cooling Systems

Cooling large quantities of water at night, while temperatures are low

and power more plentiful

Haeinsa (1398) - free air

Choose position and shape of windows to

ventilate inside by natural wind

Place charcoal, salt and lime to keep inside humidity.

Arrangement for ventilation.(Use Only 25%)

Positioning for natural convection

Seokguram (774) - free water

Porosity : Free for heat transfer

Positioning for ventilation. (It place at hill side)

Undeground water: Keep low temperature at lower part.

Condensation: Keep Low humidity.

Free Cooling

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Green Cloud Computing

- United Management

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Can we manage data center level power consumption through unified control?

IC server/chassis room

firmware

O/S

Application

(middleware)

Dynamic voltage scaling Dynamic frequency scaling Circuitry redundancy

Fan speed scaling

CPU Load balancing

Thermal-aware VM

Thermal-aware

data center

job scheduling

software

dimension

physical

dimension

Reactive

Solutions

Proactive

Approach

Green Cloud Framework

Virtual Machine Controls

Scheduling

Power Aware

Thermal Aware

Management

VM Image Design

Migration Dynamic

Shutdown

Data Center Design

Server & Rack

Design

Air Cond. & Recirculation

Framework

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Public Cloud B

PrivateCloud

End User

Carbon Emission Directory

Public Cloud A

a) Request

a cloud

service

d) Allocate

service

e) Request

service

allocation

c) Request

energy

efficiency

information

Green Offer Directory

b) Request

any green

offerRouters Internet

Green Broker

Green Cloud Architecture

Dynamic VM Consolidation

Carbon Efficient Green Policy

(CEGP) Collect resource requests from user and resource site

information such as VMs, carbon emission rate, DCiE, CPU power efficiency

Sort jobs based on deadline

Sort resource sites based on carbon footprint:

Schedule greedily the most urgent deadline jobs on the most power efficient resource site.

Carbon Emission

Datacenter Efficiency

Energy Efficiency of VM

big.LITTLE Processing - ARM

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In the Idle state, server consumes 60-70% of maximum power consumption

FAN, HDD, PSU occupy large parts in power consumptions of server.

Data Center Management system

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From the view point of energy efficiency, it is better to

keep CPU load high, when the server is switched on.

Energy Aware Data Center

Management– Cyber Physical

Design Approach

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What are Cyber Physical Systems?

- CPSs are the integrations of computation and

physical processes.

- Cyber capability in every physical component

and resource-constrained.

- Complex at multiple temporal and spatial scales

- Dynamically reorganizing/reconfiguring.

The Next Computing Revolution

Mainframe computing (60’s – 70’s)

Large computers to execute big data processing

applications

Desktop computing & Internet (80’s – 90’s)

One computer at every desk to do business/personal

activities

Ubiquitous computing (00’s)

Numerous computing devices in every place/person

Millions for desktops vs. billions for embedded processors

Cyber Physical Systems (10’s)

Trend Toward Ubiquitous Connectivity

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Trend Toward Sensor Rich Environment

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Cyber-Physical Systems (CPS) Computing systems tightly coupled with Environmentally

coupled physical world, Applying interference on system itself and the surrounding environment

Cyber-Physical System (CPS)

Computing node

Space in physical

environment interacted

by single node

Aggregate impact in space

because of interactions

from multiple nodes

Cyber-physical interactions

Task Scheduling of Cyber-Physical Systems

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2 4

Physical Side

Server farms inside data

centers

Heat dissipation of one

server may heat up other

servers

task scheduling in spatial

domain

Cyber Side

Bio-sensor networks for monitoring

task scheduling in temporal domain

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Joint optimization of physical and

computing systems

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Cyber-physical coupling-Example

Moving jobs(cyber) from servers in zone A to servers in zone B

How will the temperature distribution change?

How will the performance change?

Will this lower the overall power consumption?

Characterizing Thermal Interference

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2 4

1

Spatial Distance

ijL

3 1 2 4

Temporal Distance ijc

Cross interference between node i and node j as a function of spatial distance and temporal distance

),( ijij LcFceInterferen

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Thermal-aware Server Task Placement

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Server task distribution

Power consumption distribution

Temperature distribution

Energy cost

CoolShift (MS) : a thermo-aware modeling tool for data centers as

a cyber-physical system.

Spatio-temporal job schedule that minimizes

the total energy (cooling + computing)

consumption

Spatial job scheduling (placement) determines temperature distribution at any time using a linear thermal model

Temperature distribution determines the equipment peak air inlet temperature

Peak air inlet temperature determines upper bound to CRAC temperature setting

CRAC temperature setting determines it’s efficiency (Coefficient of Performance)

The lower the peak inlet temperature the higher the CRAC efficiency

Balancing utilization over time reduces the peak computing resource utilization leaving room for thermal-aware spatial scheduling at all time

Q. Tang, T. Mukherjee, S. K. S. Gupta, and

P. Cayton, ''Sensor-based Fast Thermal

Evaluation Model for Energy-efficient High-

performance Datacenters", In the

International Conf. Intelligent Sensing

Info.Proc. (ICISIP2006), Dec 2006.

T. Mukherjee, G. Varsamopoulos, S. K. S.

Gupta, and S. Rungta, '‘Measurement-

based Power Profiling of Datacenter

Equipment", (Extended Abstract) In the

Workshop on Green Computing (with

CLUSTER2007), Austiin, USA, Sep 2007.

Thermal-aware Job Scheduling

Problem PROBLEM: Given a set of incoming jobs, find a job scheduling (i.e. job start times) and placement (i.e. server assignment) to minimize the total data center energy consumption subject to meeting of job deadlines.

T. Mukherjee, A. Banerjee, G. Varasamopoulos, and S. K. S. Gupta, ‘Spatio-temporal Thermal-Aware Job

Scheduling to Minimize Energy Consumption in Virtualized Heterogeneous Data Centers", Elsevier Journal

on Computer Networks (ComNet), Special Issue on Virtualized Data Centers, ACCEPTED (2009).

Cooling Energy

Computing Energy Job Migration Overhead Supply Temperature Upper Bound

Capacity Constraint: server assigned less server available

Server Required: Required no. of servers assigned for jobs

Deadline Constraint: job finish time less than deadline

Arrival Constraint: job start time later than arrival

Cooling-aware workload placement Genetic Scheduling: Exploring Design Space

)(maxmin ii

TW

Searching the best scheduling sequence by using Genetic Algorithm

Based on both thermodynamics laws and real-time measurements, we can adapt with dynamics in workload, fan speed, etc.

Focus is on predicting hot spots early enough, giving data center operators enough time to react.

Given real-time sensor data and workload information, a zone thermal model that builds a relationship among the cold-aisle vent temperature, the location of the server, the local temperature distribution and the workload from nearby servers, to predict the intake future temperature at each server.

Predict the temperatures

surrounding the servers w/ Cyber-physical interactions

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ThermoCast

Go Green!!!

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• Combine concepts of both Cyber and Physical aspects

to find a best trade-off b/w energy and performance

constrained with temperature.

• Integrated server, rack, and cooling strategies.

• Designing the next generation of Cloud computing

systems to be more efficient.

References

R. Buyya, A. Beloglazov, J. Abawajy, Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges, Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA2010), Las Vegas, USA, July 12-15, 2010.

A. Beloglazov, R. Buyya, Y. Lee, A. Zomaya, A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems, Advances in Computers, Volume 82, 47-111pp, M. Zelkowitz (editor), Elsevier, Amsterdam, The Netherlands, March 2011.

S. Garg, C. Yeo, A Anandasivam, R. Buyya, Environment-Conscious Scheduling of HPC Applications on Distributed Cloud-oriented Data Centers, Journal of Parallel and Distributed Computing, 71(6):732-749, Elsevier Press, Amsterdam, The Netherlands, June 2011.

Qinghui Tang, Thermal-Aware Scheduling in Environmentally Coupled Cyber-Physical Distributed Systems

Wiley Press, New York, USA, Feb 2011

THANK YOU

&

ANY Questions?

Wish u a Green Day

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