Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing
Performance
Analysis of Cloud Computing
Services
for Many-Tasks Scientific Computing
Abstract:
Cloud
computing is an emerging commercial infrastructure paradigm that promises to
eliminate the need for maintaining expensive computing facilities by companies
and institutes alike. Through the use of virtualization and resource time
sharing, clouds serve with a single set of physical resources a large user base
with different needs. Thus, clouds have the potential to provide to their owners
the benefits of an economy of scale and, at the same time, become an
alternative for scientists to clusters, grids, and parallel production
environments. However, the current commercial clouds have been built to support
web and small database workloads,
which
are very different from typical scientific computing workloads. Moreover, the
use of virtualization and resource time sharing may introduce significant
performance penalties for the demanding scientific computing workloads. In this
work, we analyze the performance of cloud computing services for scientific
computing workloads. We quantify the presence in real scientific computing Workloads
of Many-Task Computing (MTC) users, that is, of users who employ loosely
coupled applications comprising many tasks to achieve their scientific goals.
Then, we perform an empirical evaluation of the performance of four commercial
cloud computing services including Amazon EC2, which is currently the largest
commercial cloud. Last, we compare through trace-based simulation the
Performance
characteristics and cost models of clouds and other scientific computing
platforms, for general and MTC-based scientific computing workloads. Our
results indicate that the current clouds need an order of magnitude in
performance improvement to be useful to the scientific community, and show
which improvements should be considered first to address this discrepancy
between offer and demand.
Existing System:
Ø Size wise, top scientific computing facilities
comprise very large systems
Ø Performance wise, scientific workloads often
require High-Performance Computing (HPC) or High-Throughput Computing (HTC)
capabilities.
Ø on high-performance execution of loosely coupled
applications comprising many (possibly interrelated) tasks
Ø The job execution model of scientific computing
platforms is based on the exclusive, space-shared usage of resources.
Ø Compute performance of the tested clouds is low.
Proposed
System:
Ø we first investigate the presence of an MTC
component in existing scientific
Computing workloads
and find that this presence is significant both in number of jobs and in
resources consumed.
Ø We perform an empirical performance evaluation
of four public computing clouds, including Amazon EC2, one of the largest
commercial clouds currently in production.
Ø We compare the performance and cost of clouds
with those of scientific computing alternatives such as grids and parallel production
infrastructures.
Ø We found here Current cloud computing services
are insufficient for scientific computing at large
Ø We will extend this work with additional
analysis of the other services offered by clouds, and in particular storage and
network
Ø The usefulness of our empirical evaluation part
of this work may be reduced with the commercialization of new cloud services.
KEYWORDS:
Generic Technology Keywords: Database,
User Interface, Programming
Specific Technology Keywords: C#.Net,
ASP.Net, MS SqlServer-08
Project Keywords: Presentation, Business Object, Data Access Layer, Database
SDLC Keywords: Analysis, Design, Code, Testing, Implementation, Maintenance
SYSTEM
CONFIGURATION
HARDWARE
CONFIGURATION
|
S.NO
|
HARDWARE
|
CONFIGURATIONS
|
|
1
|
Operating System
|
Windows 7 & vista
|
|
2
|
RAM
|
2GB
|
|
3
|
Processor (with Speed)
|
Intel
Pentium IV (3.0 GHz) and Upwards
|
|
4
|
Hard Disk Size
|
40 GB and above
|
|
5
|
Monitor
|
15’ CRT
|
SOFTWARE
CONFIGURATION
|
S.NO
|
SOFTWARE
|
CONFIGURATIONS
|
|
1
|
Platform
|
Microsoft Visual Studio
|
|
2
|
Framework
|
.Net Framework 4.0
|
|
3
|
Language
|
C#.Net
|
|
4
|
Front End
|
Windows application
|
|
5
|
Back End
|
SQL Server 2008
|
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