Manjrasoft
Manjrasoft
Cloud Computing:
The Next Revolution in Information Technology
1
Manjrasoft
Manjrasoft
Green Cloud Computing
2
Energy-Efficient Cloud Computing:
Opportunities and Challenges
Dr. Rajkumar Buyya
Cloud Computing and Distributed Systems (CLOUDS) Lab
Dept. of Computer Science and Software Engineering
The University of Melbourne, Australia
www.cloudbus.org
www.buyya.com
www.manjrasoft.com
Manjrasoft
Innovative Solutions for Cloud Computing
Dr Rajkumar Buyya
Chief Executive Officer
Manjrasoft Pty Ltd
Room 5.31, ICT Building, 111, Barry Street, Carlton,
Melbourne, VIC 3053, Australia
P: +61-3-8344 1344 | F : +61-3-9348 1184
E: raj@manjrasoft.com
http://www.manjrasoft.com
Major Sponsors/Supporters
Manjrasoft
Outline
Manjrasoft

Cloud Computing at a Glance


Powering Cloud Infrastructure




4
Trends, Foundations, Issues, Taxonomy
Green Cloud Computing: Framework
Energy-Efficient Resource Management


Energy Consumption, Costs, Implications
Power-Aware Computing


Cloud Benefits and Challenges
Within a Cloud Data Center
Across Multiple Data Centers (InterCloud)
Summary and Thoughts for Future
Manjrasoft
Clouds offer Subscription-Oriented IT
Services: {compute, apps, data,..} as a
Service (..aaS)
Public Cloud
Cloud
Manager
Clients
Private
Cloud
Other
Cloud Services
5
Govt.
Cloud Services
Cloud Computing
Manjrasoft
3 Main Types or Personalities
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
6
Animoto, Sales Force, Google
Document
User Applications
Scientific Computing, Enterprise ISV, Social
Networking, Gaming
SaaS
PaaS
IaaS
User-level and
infrastructure level
Platform
Cloud Programming Environment and Tools:
Web 2.0, Mashups, Concurrent and Distributed
Programming, Workflow
Cloud Hosting Platforms: QoS Negotiation
Admission Control, Pricing, SLA Management,
Monitoring
Amazon EC2, GoGrid, RightScale,
Jovent
Infrastructure
Cloud Physical Resources: Storage, virtualized
clusters, servers, network.
Cloud Economy
Google AppEngine, MapReduce,
Aneka, Microsoft Azure
Public Cloud
(IaaS)
Manjrasoft
User
User
Middleware
Master Node
Private Cloud
(Heterogeneous Resources)
Hybrid Cloud
Slave Nodes
8
Slave Nodes
(Cluster)
Several Benefits……
Manjrasoft
Service
Oriented
Elastic
Virtualized
Cloud
Computing
Dynamic
(& Distributed)
Autonomic
Market
Oriented
(Pay As You Go)
9
Shared
(Economy of
Scale)
Dark side…..
Manjrasoft
10
•
Gartner Report 2007: IT industry contributes
2% of world's total CO2 emissions
•
U.S. EPA Report 2007: 1.5% of total U.S.
power consumption used by data centers which
has more than doubled since 2000 and costs
$4.5 billion
Outline
Manjrasoft

Cloud Computing at a Glance


Powering Cloud Infrastructure




11
Trends, Foundations, Issues, Taxonomy
Green Cloud Computing: Framework
Energy-Efficient Resource Management


Energy Consumption, Costs, Implications
Power-Aware Computing


Cloud Benefits and Challenges
Within a Cloud Data Center
Across Multiple Data Centers (InterCloud)
Summary and Thoughts for Future
Powering Cloud Infrastructure
Manjrasoft
•
•
Modern data centers, operating under the Cloud
computing model, are hosting a variety of
applications ranging from those that run for a few
seconds (e.g. serving requests of web applications
such as e-commerce and social networks portals) to
those that run for longer periods of time (e.g.
simulations or large dataset processing).
However, Cloud Data Centers consume
excessive amount of energy:
•
According to McKinsey report on “Revolutionizing Data
Center Energy Efficiency” :
•
•
12
A typical data center consumes as much energy as 25,000 households.
The total energy bill for data centers in 2010 was over $11
billion and energy costs in a typical data center doubles
every five years.
Where Does the Power Go?
Manjrasoft
Server/Storage
50%
Power Consumption in the Datacenter
Computer Rm. AC
34%
Conversion
7%
Network
7%
Lighting
2%
Compute resources and
particularly servers are at
the heart of a complex,
evolving system!
Source: APC
13
Clouds Impact on the
Environment
Manjrasoft

Data centers are not only expensive to
maintain, but also unfriendly to the
environment.

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Carbon emission due to Data Centers worldwide is
now more than both Argentina and the Netherlands
emission.
High energy costs and huge carbon footprints are
incurred due to the massive amount of electricity
needed to power and cool the numerous servers
hosted in these data centers.
Outline
Manjrasoft

Cloud Computing at a Glance


Powering Cloud Infrastructure




15
Trends, Foundations, Issues, Taxonomy
Green Cloud Computing: Framework
Energy-Efficient Resource Management


Energy Consumption, Costs, Implications
Power-Aware Computing


Cloud Benefits and Challenges
Within a Cloud Data Center
Across Multiple Data Centers (InterCloud)
Summary and Thoughts for Future
Background
Manjrasoft
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
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Traditionally, HPC (commodity clusters) & Data center
community has focused on performance (speed).
At the same time, microprocessor vendors have not
only doubled the number of transistors (and speed)
every 18-24 months, but
they have also doubled
the power densities.
Moore’s Law for
Power Consumption:
Research Motivations of Power
Aware/Energy Efficient Computing
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

Rapid uptake of Cloud Data Centers for hosting industrial
applications
Reducing the operational costs of powering and cooling Data
Centers:



The tremendous increase in computer performance has come with an even
grater increase in power usage.
According to Eric Schmit, CEO of Google, what matter most to Google is
“not speed but power, because data centers can consume as much
electricity as a city.”
Improving reliability


As a rule of thumb, for every 10°C increase in temperature, the failure rate
of a system doubles.
Computing environment affected the correctness of the results.

17
The 18-node Linux cluster produced an answer outside the residual (i.e., a silent error)
when running in dusty 85°F warehouse but produced the correct answer when running
in a 65°F machine-cooled room.
Reliability/Implications
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Reliability of
Leading Edge
Supercomputer
(D. Reed, 2004)
Estimated Cost of
An hour of system
downtime (W.
Feng, (ACM
Queue, 2003):
Power Aware Computing
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Power Aware (PA) computing/communication:



System level power management

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The objective of PA computing/communications is to improve power
management and consumption using the awareness of power
consumption of devices.
Power consumption is one of the most important considerations in mobile
devices due to the limitation of the battery life.
Recent devices (CPU, disk, communication links, etc.) support multiple
power modes.
Resource Management and Scheduling Systems can use these
multiple power modes to reduce the power consumption.
DVS (Dynamic Voltage Scaling)
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DVS (Dynamic Voltage Scaling) technique




Reducing the dynamic energy consumption by lowering the supply voltage at
the cost of performance degradation
Recent processors support such ability to adjust the supply voltage
dynamically.
The dynamic energy consumption =  * Vdd2 * Ncycle


Vdd : the supply voltage
Ncycle : the number of clock cycle
An example

deadline
Power
Power
deadline
5.02
2.02
10 msec
25 msec
(a) Supply voltage = 5.0 V
20
10 msec
25 msec
(b) Supply voltage = 2.0 V
DVS-based Power Aware Scheduling
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
Motivation:


21
Develop Resource Management and Scheduling Algorithms
that aim at minimizing the energy consumption at the same
meet the job deadline.
Exploit industrial move towards Utility Model / SLA-based
Resource Allocation for Cloud Computing
Taxonomy of Power Management
Techniques
Manjrasoft
Power Management Techniques
Static Power Management (SPM)
Hardware Level
Circuit Level
Logic Level
Software Level
Architectural Level
Dynamic Power Management (DPM)
Hardware Level
Single Server
OS Level
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Software Level
Multiple Servers, Data
Centers and Clouds
Virtualization Level
Data Center Level
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Yes
Virtualization
No
Single resource
System resources
Multiple resources
Homogeneous
Target systems
Heterogeneous
Minimize power / energy
consumption
Data center level
Goal
Minimize performance
loss
Meet power budget
DVFS
Power saving techniques
Resource throttling
DCD
Workload consolidation
Arbitrary
Workload
23
Real-time applications
HPC-applications
Outline
Manjrasoft

Cloud Computing at a Glance


Powering Cloud Infrastructure




24
Trends, Foundations, Issues, Taxonomy
Green Cloud Computing: Framework
Energy-Efficient Resource Management


Energy Consumption, Costs, Implications
Power-Aware Computing


Cloud Benefits and Challenges
Within a Cloud Data Center
Across Multiple Data Centers (InterCloud)
Summary and Thoughts for Future
Cloud Providers Measures
Manjrasoft

Cloud service providers need to adopt measures to
ensure that their profit margin is not dramatically
reduced due to high energy costs.



25
Amazon.com’s estimate the energy-related costs of its data centers
amount to 42% of the total budget that include both direct power
consumption and the cooling infrastructure amortized over a 15-year
period.
Google, Microsoft, and Yahoo are building large data centers in
barren desert land surrounding the Columbia River, USA to exploit
cheap hydroelectric power.
There is also increasing pressure from Governments
worldwide to reduce carbon footprints, which have a
significant impact on climate change.
 Carbon Tax (July 2012 in Australia) on industries
Manjrasoft


26
Green Cloud: “performance” 
“energy efficiency”
As energy costs are increasing while availability
dwindles, there is a need to shift focus from
optimising data center resource management for pure
performance alone to optimising for energy
efficiency while maintaining high service level
performance.
We propose Green Cloud computing model that
achieves not only efficient processing and utilisation of
computing infrastructure, but also minimise energy
consumption.
Green Cloud Computing
Manjrasoft
Revenue
Power
Consumption
27
Cloud Usage Model
Manjrasoft
Cloud
Datacenter A
LAN and Gateway
router
(Network Devices)
End User
Cloud
Datacenter B
Internet
Service
Provider
VM and Storage
(Server)
Air Conditioning, and
Chiller
(Cooling Devices)
Routers
UPS, PDU, lighting
(Electrical Devices)
Internet
28
Cloud
Datacenter C
Datacenter
Cloud
Computing
Manjrasoft
29
Green Cloud Computing
Architecture
Outline
Manjrasoft

Cloud Computing at a Glance


Powering Cloud Infrastructure




30
Trends, Foundations, Issues, Taxonomy
Green Cloud Computing: Framework
Energy-Efficient Resource Management


Energy Consumption, Costs, Implications
Power-Aware Computing


Cloud Benefits and Challenges
Within a Cloud Data Center
Across Multiple Data Centers (InterCloud)
Summary and Thoughts for Future
Manjrasoft
Case Study 2: Dynamic VM
Consolidation
User
User
User
VM provisioning SLA negotiation Application requests
Global resource managers
Virtual
Machines
and
users’
applications
Consumer, scientific and business
applications
Virtualization layer
(VMMs, local resources managers)
31
Pool of
physical
computer
nodes
Power On
Power Off
Three Sub-Problems
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
When to migrate VMs?
•
•

Which VMs to migrate?
•

VM selection algorithms
Where to migrate VMs?
•
32
Host overload detection algorithms
Host underload detection algorithms
VM placement algorithms
Proposed “Power-Aware” Algorithms
Manjrasoft
•
Host overload detection
•
Adaptive utilization threshold based algorithms
•
•
•
Regression based algorithms
•
•
•
•
•
Minimum Migration Time policy (MMT)
Random Selection policy (RS)
Maximum Correlation policy (MC)
VM placement algorithms
•
33
Migrating the VMs from the least utilized host
VM selection algorithms
•
•
Local Regression algorithm (LR)
Robust Local Regression algorithm (LRR)
Host underload detection algorithms
•
•
Median Absolute Deviation algorithm (MAD)
Interquartile Range algorithm (IQR)
Heuristic for the bin-packing problem – Power-Aware Best Fit
Decreasing algorithm (PABFD)
Performance Metrics
Manjrasoft
•
SLA violation metrics
•
•
•

A combined metric that captures both energy
consumption and the level of SLA violations, Energy
and SLA Violation (ESV):

34
Overloading Time Fraction (OTF) - the time fraction, during
which active hosts experienced the 100% CPU utilization
Performance Degradation due to VM Migrations (PDM)
A combined SLA Violation metric (SLAV):
SLAV = OTF * PDM
ESV = Energy * SLAV:
Simulation Setup
Manjrasoft
•
•
CloudSim with a power package
A Data Center consisting:
•
•

More than 1000 Heterogeneous VMs
corresponding to Amazon EC2 instance types
Workload traces from more than 1000 VMs from
servers located in more than 500 places around
the world.

35
800 heterogeneous physical servers containing HP
ProLiant ML110 G4 and HP ProLiant ML110 G5
servers.
The data were obtained from the CoMon project, a
monitoring infrastructure for PlanetLab
Manjrasoft
Best Algorithm Combinations and
Benchmark Algorithms
Dynamic VM consolidation significantly reduces energy consumption compared to non-power aware
allocation and static allocation policies, like DVFS, NPA (non-power aware)
36
Case Study 1: Key Observations
Manjrasoft




37
Dynamic VM consolidation algorithms significantly outperforms
static allocation policies.
Heuristic-based dynamic VM consolidation algorithms substantially
outperform the optimal online deterministic algorithm (THR-1.0) due
to a vastly reduced level of SLA violations.
The MMT policy produces better results compared to the MC and
RS policies, meaning that the minimization of the VM migration time
is more important than the minimization of the correlation between
VMs allocated to a host.
Dynamic VM consolidation algorithms based on local regression
outperform the threshold-based and adaptive-threshold based
algorithms due to better predictions of host overload, and therefore
decreased SLA violations and the number of VM migrations.
Outline
Manjrasoft

Cloud Computing at a Glance


Powering Cloud Infrastructure




38
Trends, Foundations, Issues, Taxonomy
Green Cloud Computing: Framework
Energy-Efficient Resource Management


Energy Consumption, Costs, Implications
Power-Aware Computing


Cloud Benefits and Challenges
Within a Cloud Data Center
Across Multiple Data Centers (InterCloud)
Summary and Thoughts for Future
Green Cloud or Brown Cloud?
Manjrasoft
•
Ideally, for every server virtualized, save
–
–
•
Plus
–
–
•
39
~$700 and ~7,000 kWh / year
4 tons of CO2 emissions / year
Power down underutilized physical servers, saving 40%
Desktop management, saving 35% / year
But currently
Cloud
datacenters
Location
Estimated power
usage
Effectiveness
1.21
Google
Lenoir
Apple
Apple, NC
Microsoft
Chicago, IL
1.22
Yahoo
La Vista, NE
1.16
%
of
Dirty
Energy
Generation
50.5% Coal,
38.7% Nuclear
50.5% Coal,
38.7% Nuclear
72.8% Coal,
22.3% Nuclear
73.1% Coal,
14.6% Nuclear
% of Renewable
Electricity
3.8%
3.8%
1.1%
7%
Some Observations
Manjrasoft
Datacenters has heterogeneous properties

–
–
–
–
40
Source: Best
Geographically distributed datacenters (different
environmental factors and electricity prices)
Each resource site has different CPU configurations
Each site has different energy efficiency
Different Carbon-footprint
Practices for Data Centers: Lessons Learned from Benchmarking 22 Data Centers by Lawrence Berkeley National Laboratory’s report
40
Green Cloud Architecture
Manjrasoft
End User
d) Allocate
service
Private
Cloud
a) Request
a cloud
service
Green Broker
Routers
c) Request
energy
efficiency
information
Internet
b) Request
any green
offer
Carbon Emission
Directory
e) Request
service
allocation
41
Public Cloud A
Public Cloud B
Green Offer
Directory
Manjrasoft

Third Party: Green Offer and Carbon
Emission Directory
Carbon Emission Directory



Contains data on Power Usage Effectiveness (PUE), cooling
efficiency, carbon footprint, network cost
Helps user to select cloud services with minimum carbon
footprint
Incentive for providers


Require more carbon transparency from providers


Government role by enforcing policies such as Carbon Tax
Green Offer Directory

Incentive for users


42
Advertising tool to increase the market share, e.g. Google
Choosing more carbon efficient hours
Lists services with their discounted prices and green hours
User: Green Broker
Manjrasoft
•
A typical Cloud broker
–
User
–
Lease Cloud services
Schedule applications
Green Broker
Cloud Request Services
QoS
Application
Profiling
Cloud
Offers
CO2 Analysis Services
Cost
Calculator
CO2 Emission
Calculator
Cloud
Leasing
Green Broker
–
Green
Information
System
Brokering Services such as
scheduling, monitoring
Green
Policies
•
–
Scheduler
–
Private Cloud
43
Public Cloud
1st layer: Analyze user
requirements
2nd layer: Calculates cost
and carbon footprint of
services
3rd layer: Carbon aware
scheduling
Provider: Green Middleware
Manjrasoft
44
Case Study: IaaS Cloud
Manjrasoft
45

Carbon Emission Directory: Stores all carbon
emission rates for each IaaS provider

Green Offer Directory: Receives number of
VMs that can be initiated at a particular time for
maximum energy efficiency

Green Broker: Computes schedule with the
lowest carbon emission based on application
requirements
Manjrasoft



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:
Carbon
Emission

46
Datacenter
Efficiency
Energy
Efficiency of VM
Schedule greedily the most urgent deadline jobs
on the most power efficient resource site.
Simulation Setup
Manjrasoft

Parallel Workload: first week of LLNL Thunder
trace from Parallel Workload Archive (PWA)


Configuration of Cloud resource sites2:
1D.
47
Deadline generated based methodology proposed by
Irwin et al. (2004)1
2
Irwin, L. Grit, and J. Chase, “Balancing risk and reward in a market-based task service,” in Proc. of the 13th IEEE International Symposium on High
Performance Distributed Computing, Honolulu, USA, 2004.
L. Wang and Y. Lu, “Efficient Power Management of Heterogeneous Soft Real-Time Clusters,” in Proc. of the 2008 Real-Time Systems Symposium,
EDF: Carbon-Efficient (CEGP) VS EST
(Early Start-time) Algorithm (EST)
Manjrasoft
48
Case Study 2: Summary
Manjrasoft
•
•
•
•
49
Presented a Carbon Aware Green Cloud Framework to
improve the carbon footprint of Cloud computing.
Proposed framework provides incentives to both users
and providers to utilize and deliver the most “Green"
services.
Proposed a Carbon Efficient Green Policy (CEGP) for
IaaS providers.
Green Policy CEGP can save up to 23% energy while
reducing the carbon footprint by about 25%.
Outline
Manjrasoft

Cloud Computing at a Glance


Powering Cloud Infrastructure




50
Trends, Foundations, Issues, Taxonomy
Green Cloud Computing: Framework
Energy-Efficient Resource Management


Energy Consumption, Costs, Implications
Power-Aware Computing


Cloud Benefits and Challenges
Within a Cloud Data Center
Across Multiple Data Centers (InterCloud)
Summary and Thoughts for Future
Conclusions
Manjrasoft

Clouds are essentially Data Centers hosting application
services offered on a subscription basis. However, they
consume high energy to maintain their operations.




Proposed heuristics for energy-efficient dynamic VM
consolidation that significantly reduce energy
consumption, while providing a low level of SLA
violations.
Presented a Carbon Aware Green Cloud Framework to
improve the carbon footprint of Cloud computing
Open Issues:

51
 high operational cost + environmental impact

EE Data Structures + Algorithms
EE Resource Management for other workloads (e.g., workflows)
References
Manjrasoft

Keynote Paper


Taxonomy + EE InterClouds:


52
R. Buyya, A. Beloglazov, J. Abawajy, EnergyEfficient 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.
Wiley Press, New York, USA,
Feb 2011
Thanks for your attention!
Manjrasoft

Are there any


Questions?
Comments/Suggestions
Manjrasoft
We welcome you to:
Study/Research with Us | Do Business with us!
http:/www.cloudbus.org | www.Manjrasoft.com
rbuyya@unimelb.edu.au | raj@manjrasoft.com
53
Manjrasoft
Manjrasoft
Green Cloud Computing
54
Simulation Results: ESV
Manjrasoft
7
ESV, x0.001
6
5
4
3
2
1
0
8
8
8
5
5
5
5
5
5
3
2
2
3
3
2
0.
0.
0.
1.
1.
1.
2.
2.
2.
1.
1.
1.
1.
1.
1.
RS MC M T R S MC MT RS MC MT RS MC MT RS MC MT
R R
M QR R
M AD D
M RR R
M LR R
M
H
L
R
H
R
I
Q
R
A
R
R
D L L
T T
I Q
M M
R
L
H
A
I
T
L
M
55
Simulation Results: Energy
Manjrasoft
130
Energy, kWh
120
110
100
90
80
70
60
8
8
8
5
5
5
5
5
5
3
2
2
3
3
2
0.
0.
0.
1.
1.
1.
2.
2.
2.
1.
1.
1.
1.
1.
1.
S
S
S
S
S
R MC M T R MC MT R MC MT R MC MT R MC MT
R R
M QR R
M AD D
M RR R
M LR L R
M
H
R
H
R
I
Q
R
A
R
R
D
L
T T
I Q
L
M M
L
A
I
LR
TH
M
56
Simulation Results: SLAV
Manjrasoft
9
SLAV, x0.00001
8
7
6
5
4
3
2
1
0
8
8
8
5
5
5
5
5
5
3
2
2
3
3
2
0.
0.
0.
1.
1.
1.
2.
2.
2.
1.
1.
1.
1.
1.
1.
S
S
S
S
S
R MC M T R MC MT R MC MT R MC MT R MC MT
R R
M QR R
M AD D
M RR R
M LR L R
M
H
R
I
R
T T H HR
IQ QR M MA AD L L RR
L
I
L
T
M
57
Simulation Results: the Number of
VM Migrations
Manjrasoft
VM Migrations, x1000
22.5
20.0
17.5
15.0
12.5
10.0
7.5
5.0
58
8
8
8
5
5
5
5
5
5
3
2
2
3
3
2
0.
0.
0.
1.
1.
1.
2.
2.
2.
1.
1.
1.
1.
1.
1.
S
S
S
S
S
R MC MT R MC M T R MC MT R M C MT R MC MT
R R
M QR R
M AD D
M RR R
M L R LR
M
H
R
H
R
I
Q
R
A
R
R
D
L
T T H
I Q
L
M M
L
A
I
LR
T
M