Efficient Resource Management

meatcologneInternet and Web Development

Nov 3, 2013 (3 years and 8 months ago)

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Efficient Resource Management
for Cloud Computing
Environments

2:
Rochester Institute of Technology

102 Lomb Memorial Drive

Rochester, New York 14623

1:
Pervasive Technology Institute

Indiana University

2719 E. 10
th

Street

Bloomington, Indiana 47408

Andrew J. Younge
1
, Gregor von Laszewski
1
, Lizhe Wang
1
, Sonia
Lopez
-
Alarcon
2
, Warren Carithers
2



Outline


Introduction


Motivation


Related Work


Green Cloud Framework


VM Scheduling & Management


Minimal Virtual Machine Images


Conclusion & Future Work


2

What is Cloud Computing?



Computing may someday
be organized as a public
utility just as the telephone
system is a public utility...
The computer utility could
become the basis of a new
and important industry.



John McCarthy, 1961



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.




Ian Foster, 2008


3

Virtualization


Virtual Machine (VM) is
a software artifact that
executes other software
as if it was running on a
physical resource
directly.


Typically uses a
Hypervisor or VMM
which abstracts the
hardware from an
Operating System


4

Cloud Computing


Features of Clouds


Scalable


Enhanced Quality of Service (QoS)


Specialized and Customized


Cost Effective


Simplified User Interface

5

Data Center Power Consumption


Currently it is estimated that servers consume
0.5% of the world

s total electricity usage.


Closer to 1.2% when data center systems are factored
into the equation.


Server energy demand doubles every 4
-
6 years.


This results in large amounts of CO
2

produced by
burning fossil fuels.


What if we could reduce the energy used with
minimal performance impact?


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, no more

blank checks.



Many facilities are are at
their peak operating
envelope, and cannot
expand without a new
power source.


Environmental


70% of the U.S. energy
sources are fossil fuels.


2.8 billion tons of CO
2

emitted each year from
U.S. power plants.


Sustainable energy
sources are not ready.


Need to reduce energy
dependence until a more
sustainable energy source
is deployed.

7

Green Computing


Performance/Watt is not following Moore

s
law.


Advanced scheduling schemas to reduce
energy consumption.


Power aware


Thermal aware


Data center designs to reduce Power Usage
Effectiveness.


Cooling systems


Rack design

8

Research Opportunities



There are a number of areas to explore in
order 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.


9

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

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

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

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

485 Watts vs. 552 Watts

13

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

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

15

Minimizing VM Instances


Virtual machines are desktop
-
based.


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

16

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

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

18

Conclusion


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.

19

Future Work


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.

20

Appendix


21

Cloud Computing


Distributed Systems
encompasses a wide
variety of technologies


Grid computing spans
most areas and is
becoming more mature.


Clouds are an emerging
technology, providing
many of the same
features as Grids without
many of the potential
pitfalls.

From

Cloud Computing and Grid Computing 360
-
Degree
Compared


22

Data Center Design

23


Need new data center
designs strategies to reduce
cooling requirements.


Pod
-
based clusters:


Modular


Semi
-
portable


Closed
-
loop systems


Quebec

s CLUMEQ Silo
supercomputer.


Minimal VM Image


Easier to slim down a fully
functional distro than to create
one from scratch.


Selected Ubuntu Linux.


Jaunty 9.04.


Minimal install profile compared
to other major distros.


Excellent package management
software (aptitude).


Great support.




Ubuntu Linux

Minimal
Ubuntu

Vs.

VM Image
Design

24

VM Scheduling


Implemented scheduler
on OpenNebula system


Replaced Round Robin
scheduling system with
Based on Algorithm


Startup and Shutdown
VM Management Easily
added

From

Opennebula: The open source virtual machine manager for cluster computing


25

Performance Impact of VMs

26

DVFS VM Scheduling

27