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CLOUD COMPUTING
Resource Management - I
PROF. SOUMYA K. GHOSH
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
IIT KHARAGPUR
2
Different Resources in Computing
Source: http://www.cse.hcmut.edu.vn/~ptvu/gc/2012/GC-pp.pdf
3
Resources types
Source: http://www.cse.hcmut.edu.vn/~ptvu/gc/2012/GC-pp.pdf
• Physical resource
 Computer, disk, database, network, scientific instruments.
• Logical resource
 Execution, monitoring, communicate application .
4
Resources Management
Source: http://www.cse.hcmut.edu.vn/~ptvu/gc/2012/GC-pp.pdf
• The term resource management refers to the operations
used to control how capabilities provided by Cloud
resources and services cane be made available to other
entities, whether users, applications, services in an efficient
manner.
• Currently it is estimated that servers consume 0.5% of the world’s total
electricity usage.
• Server energy demand doubles every 5-6 years.
• This results in large amounts of CO2 produced by burning fossil fuels.
• Need to reduce the energy used with minimal performance impact.
5
Data Center Power Consumption
Ref: Efficient Resource Management for Cloud Computing Environments, by Andrew J. Younge,
Gregor von Laszewski, Lizhe Wang, Sonia Lopez-Alarcon, Warren Carithers,
Motivation for Green Data Centers
Economic
• New data centers run on the
Megawatt scale, requiring millions of
dollars to operate.
• Recently institutions are looking for
new ways to reduce costs
• Many facilities are at their peak
operating stage, and cannot expand
without a new power source.
Environmental
• Majority of energy sources are fossil
fuels.
• Huge volume of CO2 emitted each year
from power plants.
• Sustainable energy sources are not
ready.
• Need to reduce energy dependence
6
Green Computing ?
• Advanced scheduling schemas to reduce energy consumption.
• Power aware
• Thermal aware
• Performance/Watt is not following Moore’s law.
• Data center designs to reduce Power Usage Effectiveness.
• Cooling systems
• Rack design
7
Research Directions
How to conserve energy within a Cloud environment.
• Schedule VMs to conserve energy.
• Management of both VMs and underlying infrastructure.
• Minimize operating inefficiencies for non-essential tasks.
• Optimize data center design.
8
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
Steps towards Energy Efficiency
9
VM scheduling on Multi-core Systems
• There is a nonlinear relationship
between the number of processes
used and power consumption
• We can schedule VMs to take
advantage of this relationship in
order to conserve power
Power consumption curve on an Intel Core i7 920 Server
(4 cores, 8 virtual cores with Hyperthreading)
Scheduling
90
100
110
120
130
140
150
160
170
180
0 1 2 3 4 5 6 7 8
Watts
Number of Processing Cores
10
Power-aware Scheduling
• Schedule as many VMs at once on
a multi-core node.
• Greedy scheduling algorithm
• Keep track of cores on a given
node
• Match VM requirements with node
capacity
Scheduling
11
485 Watts vs. 552 Watts !
12
Node 1 @ 170W
V
M
V
M
V
M
V
M
V
M
V
M
V
M
V
M
Node 2 @ 105W
Node 3 @ 105W Node 4 @ 105W
Node 1 @ 138W
V
M
V
M
V
M
V
M
V
M
V
M
V
M
V
M
Node 2 @ 138W
Node 3 @ 138W Node 4 @ 138W
VS.
VM Management
• Monitor Cloud usage and load.
• When load decreases:
• Live migrate VMs to more utilized nodes.
• Shutdown unused nodes.
• When load increases:
• Use WOL to start up waiting nodes.
• Schedule new VMs to new nodes.
Management
13
Node 1
VM VM VM VM
Node 2
Node 1
VM VM VM VM
Node 2
Node 1
VM VM VM VM
Node 2 (offline)
VM
Node 1
VM VM VM VM
Node 2
1
2
3
4
14
Minimizing VM Instances
• Virtual machines are loaded!
• Lots of unwanted packages.
• Unneeded services.
• Are multi-application oriented, not service oriented.
• Clouds are based off of a Service Oriented Architecture.
• Need a custom lightweight Linux VM for service oriented science.
• Need to keep VM image as small as possible to reduce network latency.
Management
15
Typical Cloud Linux Image
• Start with Ubuntu 9.04.
• Remove all packages not
• required for base image.
• No X11
• No Window Manager
• Minimalistic server install
• Can load language support on demand (via package
manager)
• Readahead profiling utility.
• Reorder boot sequence
• Pre-fetch boot files on disk
• Minimize CPU idle time due to I/O delay
• Optimize Linux kernel.
• Built for Xen DomU
• No 3d graphics, no sound, minimalistic kernel
• Build modules within kernel directly
VM Image
Design
16
Energy Savings
• Reduced boot times from 38 seconds to just 8 seconds.
• 30 seconds @ 250Watts is 2.08wh or .002kwh.
• In a small Cloud where 100 images are created every hour.
• Saves .2kwh of operation @ 15.2c per kwh.
• At 15.2c per kwh this saves $262.65 every year.
• In a production Cloud where 1000 images are created every minute.
• Saves 120kwh less every hour.
• At 15.2c per kwh this saves over 1 million dollars every year.
• Image size from 4GB to 635MB.
• Reduces time to perform live-migration.
• Can do better.
VM Image
Design 17
Summary - 1
• Cloud computing is an emerging topic in Distributed Systems.
• Need to conserve energy wherever possible!
• Green Cloud Framework:
• Power-aware scheduling of VMs.
• Advanced VM & infrastructure management.
• Specialized VM Image.
• Small energy savings result in a large impact.
• Combining a number of different methods together can have a larger impact then
when implemented separately.
18
• Combine concepts of both Power-aware and Thermal-aware scheduling to
minimize both energy and temperature.
• Integrated server, rack, and cooling strategies.
• Further improve VM Image minimization.
• Designing the next generation of Cloud computing systems to be more
efficient.
19
Summary - 2
Thank you!

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Mod05lec24(resource mgmt i)

  • 1. CLOUD COMPUTING Resource Management - I PROF. SOUMYA K. GHOSH DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING IIT KHARAGPUR
  • 2. 2 Different Resources in Computing Source: http://www.cse.hcmut.edu.vn/~ptvu/gc/2012/GC-pp.pdf
  • 3. 3 Resources types Source: http://www.cse.hcmut.edu.vn/~ptvu/gc/2012/GC-pp.pdf • Physical resource  Computer, disk, database, network, scientific instruments. • Logical resource  Execution, monitoring, communicate application .
  • 4. 4 Resources Management Source: http://www.cse.hcmut.edu.vn/~ptvu/gc/2012/GC-pp.pdf • The term resource management refers to the operations used to control how capabilities provided by Cloud resources and services cane be made available to other entities, whether users, applications, services in an efficient manner.
  • 5. • Currently it is estimated that servers consume 0.5% of the world’s total electricity usage. • Server energy demand doubles every 5-6 years. • This results in large amounts of CO2 produced by burning fossil fuels. • Need to reduce the energy used with minimal performance impact. 5 Data Center Power Consumption Ref: Efficient Resource Management for Cloud Computing Environments, by Andrew J. Younge, Gregor von Laszewski, Lizhe Wang, Sonia Lopez-Alarcon, Warren Carithers,
  • 6. Motivation for Green Data Centers Economic • New data centers run on the Megawatt scale, requiring millions of dollars to operate. • Recently institutions are looking for new ways to reduce costs • Many facilities are at their peak operating stage, and cannot expand without a new power source. Environmental • Majority of energy sources are fossil fuels. • Huge volume of CO2 emitted each year from power plants. • Sustainable energy sources are not ready. • Need to reduce energy dependence 6
  • 7. Green Computing ? • Advanced scheduling schemas to reduce energy consumption. • Power aware • Thermal aware • Performance/Watt is not following Moore’s law. • Data center designs to reduce Power Usage Effectiveness. • Cooling systems • Rack design 7
  • 8. Research Directions How to conserve energy within a Cloud environment. • Schedule VMs to conserve energy. • Management of both VMs and underlying infrastructure. • Minimize operating inefficiencies for non-essential tasks. • Optimize data center design. 8
  • 10. VM scheduling on Multi-core Systems • There is a nonlinear relationship between the number of processes used and power consumption • We can schedule VMs to take advantage of this relationship in order to conserve power Power consumption curve on an Intel Core i7 920 Server (4 cores, 8 virtual cores with Hyperthreading) Scheduling 90 100 110 120 130 140 150 160 170 180 0 1 2 3 4 5 6 7 8 Watts Number of Processing Cores 10
  • 11. Power-aware Scheduling • Schedule as many VMs at once on a multi-core node. • Greedy scheduling algorithm • Keep track of cores on a given node • Match VM requirements with node capacity Scheduling 11
  • 12. 485 Watts vs. 552 Watts ! 12 Node 1 @ 170W V M V M V M V M V M V M V M V M Node 2 @ 105W Node 3 @ 105W Node 4 @ 105W Node 1 @ 138W V M V M V M V M V M V M V M V M Node 2 @ 138W Node 3 @ 138W Node 4 @ 138W VS.
  • 13. VM Management • Monitor Cloud usage and load. • When load decreases: • Live migrate VMs to more utilized nodes. • Shutdown unused nodes. • When load increases: • Use WOL to start up waiting nodes. • Schedule new VMs to new nodes. Management 13
  • 14. Node 1 VM VM VM VM Node 2 Node 1 VM VM VM VM Node 2 Node 1 VM VM VM VM Node 2 (offline) VM Node 1 VM VM VM VM Node 2 1 2 3 4 14
  • 15. Minimizing VM Instances • Virtual machines are loaded! • Lots of unwanted packages. • Unneeded services. • Are multi-application oriented, not service oriented. • Clouds are based off of a Service Oriented Architecture. • Need a custom lightweight Linux VM for service oriented science. • Need to keep VM image as small as possible to reduce network latency. Management 15
  • 16. Typical Cloud Linux Image • Start with Ubuntu 9.04. • Remove all packages not • required for base image. • No X11 • No Window Manager • Minimalistic server install • Can load language support on demand (via package manager) • Readahead profiling utility. • Reorder boot sequence • Pre-fetch boot files on disk • Minimize CPU idle time due to I/O delay • Optimize Linux kernel. • Built for Xen DomU • No 3d graphics, no sound, minimalistic kernel • Build modules within kernel directly VM Image Design 16
  • 17. Energy Savings • Reduced boot times from 38 seconds to just 8 seconds. • 30 seconds @ 250Watts is 2.08wh or .002kwh. • In a small Cloud where 100 images are created every hour. • Saves .2kwh of operation @ 15.2c per kwh. • At 15.2c per kwh this saves $262.65 every year. • In a production Cloud where 1000 images are created every minute. • Saves 120kwh less every hour. • At 15.2c per kwh this saves over 1 million dollars every year. • Image size from 4GB to 635MB. • Reduces time to perform live-migration. • Can do better. VM Image Design 17
  • 18. Summary - 1 • Cloud computing is an emerging topic in Distributed Systems. • Need to conserve energy wherever possible! • Green Cloud Framework: • Power-aware scheduling of VMs. • Advanced VM & infrastructure management. • Specialized VM Image. • Small energy savings result in a large impact. • Combining a number of different methods together can have a larger impact then when implemented separately. 18
  • 19. • Combine concepts of both Power-aware and Thermal-aware scheduling to minimize both energy and temperature. • Integrated server, rack, and cooling strategies. • Further improve VM Image minimization. • Designing the next generation of Cloud computing systems to be more efficient. 19 Summary - 2