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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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6
0
Thermal-aware Server Task Placement
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910
1112
S1
S3
S5
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20
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S1
S2
S3
0
500
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1500
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3500
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S1
S2
S30
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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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