This document discusses performance analysis of cloud computing services. It begins by defining cloud computing and describing its key characteristics like on-demand access to computing resources and pay-per-use models. It then reviews several studies on using virtualization technologies and frameworks for evaluating cloud performance and workload generation. The document concludes that tools are needed for comprehensive performance analysis of large scientific clouds to evaluate metrics like response time, cost and scalability across different cloud vendors.

1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
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ISSN 0976 - 6375(Online), Volume 5, Issue 2, February (2014), pp. 60-70 © IAEME
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SURVEY ON THE PERFORMANCE ANALYSIS OF CLOUD COMPUTING
SERVICES
A.Lourdes Mary1,
1
Dr. R.Ravi2
Department of computer science and Engineering, Associate Professor,
SCAD College Engineering and Technology, Cheranmahadevi, India
2
Department of computer science and Engineering, Professor & Head,
Francis Xavier Engineering College, Tirunelveli, India
ABSTRACT
Cloud computing is a cutting edge technology for computing and people who are not a
techsavy knew this buzz word. Now it is emerging rapidly in the research arena. It is an aggregation
of Pay-per-use computing paradigm and a utility computing. Voluminous data storage and timeliness
of the needed resources makes this as a computing-computer data centers. Numerous cloud providers
are there with their own set of features and service level agreements. Users are increasing day-by-day
due to the thirst for processing and retrieving large amount of data. Therefore performance of its
services needs its attention. Performance may be computed based on the metrics like latency,
response time, throughput and data retrieval time. For scientific computing, it is the excellent choice
due to the availability and reliability of the data over a period of time. Several peta bytes of workload
can be processed in several nanoseconds with the appropriate CRMS policies.
Keywords: Cloud Computing, Utility Computing, Data Centers, Peta Bytes, CRMS.
I. INTRODUCTION
Cloud computing is not at all a new term and even a computer illiterate knew that data can be
retrieved and stored in the form of cloud. Cloud computing has its own definition. It refers to both
the applications and services over the Internet as well as the hardware and software in the data
centers that provide those services[1].The data center hardware and software is what we will call a
cloud. When a cloud is open in apay-per-use manner to the general public, We call it by name public
cloud; the service being sold is called utility computing. Private cloud is the term refers to internal
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2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 2, February (2014), pp. 60-70 © IAEME
data centres of a business or other corporate, not for general public. Cloud is the aggregation of
utility computing and SaaS excluding private clouds[2].
In the view of hardware, the three main aspects of cloud computing are:
1. The mirage of infinite computing resources available on demand, there is no need for cloud
users to plan for resource provisioning.
2. The elimination of an up-front commitment by cloud users, thereby allowing corporate to
start small and increase hardware resources only when there is an increase in their needs.
3. The ability to pay per use on a short term basis as needed and release them as needed,
there by rewarding conservation by letting machines and storage go when they are no
longer in use[2].
Critical obstacles to the growth of Cloud computing:- adoption, growth, policy and business.
Current examples of public utility computing includes Amazon Web service(AWS),Google App
Engine and Microsoft Azure. As a successful example, Amazon Elastic Cloud (EC2) sells 1.0Ghz
x86 ISA “slices” for 10 dollars per hour,and a new slice(Instance) can be added in 2 to 5 minutes.
Amazon S3 charges $0.12 to $0.15 per Gigabits, with additional bandwidth charges of $0.10 to
$0.15pergigabyte to move data into and out of AWS over the Internet. Amazon, eBay, Google,
Microsoft and others having scalable infrastructures such as MapReduce, Google File System, Big
Table and Dynamo.
To avail a cloud service like Amazon the customer need to have a credit card. Selling
hardware-level virtual machine cycles, allowing users to choose their own software stack without
disturbing others while sharing hardware thereby reducing costs further.
Cloud offers excellent and unique opportunity for parallel batch processing that analyse
terabytes of data and can take hours to finish.
II. SCIENTIFIC COMPUTING
Importance
Scientific computing needs are always increasing in the number of resources to produce the
results for larger problem sizes in a reasonable time frame. Yester years, while the large research
projects were able to afford super computers, others projects were forced to use resources with
minimum cost such as commodity cluster and grids. Scientific workflow is concerned with the
automation of scientific processes in which tasks are structured based on their control and data
dependencies. Thanks to the major paradigm shift, Cloud computing offers leasing of data centers on
demand. Cloud remains an available and reliable platform the pool of resources or commodity
clusters [3].Scientific workloads varies from the initial target workload one in the size and the other in
performance demand. It usually requires top performance and High performance computing
capabilities. Scientific computing is a high-utilization workload, with parallel production
infrastructures(PPI).
Among the three services models SaaS,PaaS,IaaS- IaaS is a raw infrastructure and associated
middleware. Scientific computing adopts IaaS. There are number of cloud vendors. Among this,
Amazon is the commercial IaaS provides an infrastructure size that can accommodate entire grids
and PPI workloads. In EC2, ECU is the unit used for measuring CPU power 1.0-1.2 GHz
2007operton or Xeon processor. At peak performance 1EUC equals to 4.4GFLOPS[4].
A.
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3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 2, February (2014), pp. 60-70 © IAEME
To Create an infrastructure from EC2 resources, the user needs the creation of one or more
instances for which it specifies the instance type and the VM image; the user can specify the VM
image already registered. Once the VM image is deployed on the running machine where the
resource is available, the instance is booted. At the end of the boot process the resource status
becomes installed. The resource installed can be used as a regular computing node via sh connection.
A maximum of 20 instances can be used by the users. Amazon EC2 do not provide job
execution .RMS can act as a middleware between the user and Amazon EC2 to reduce resource
complexity.
III. LITERATURE SURVEY
Wei Huang, Jiuxing Liu et al., (2006) proposes a virtual machine technologies[5] with a
revision and provides advantages such as ease of management, system security, performance
isolation ,checkpoint and live migration. Challenges addressed are: CPU virtualization, memory
virtualization and I/O virtualization. VM Image Management cost can be reduced by minimizing VM
images, Fast and scalable Image distribution and by VM Image caching.HPC applications can
achieve the performance as those running in a native, non-virtualized environments.
Jianfeng.Z, Lei Wang et al.(2007) designed and implemented a innovative cloud computing
software called phoenixcloud[6] to consolidate parallel batch jobs and web service, which
cooperatively share the cluster resource. cooperative resource provision and management policies are
used to share the cluster. To assess the performance of this cloud software, number of jobs completed
jobs and the reciprocal of the turnaround time per job was taken.
M .R. Palankar, A. Lamnitchi, M. Ripeanu and S . Garfinkel (2008) proposes a tool for data
storage in cloud. REST protocol for failures detection.S3[7] is suitable for reading object of 16MB
and larger. storage cost can be reduced by archiving “cold” data on low cost and maintaining only
the data most likely to be used on. It offers low latency, infinite data durability and high-availability.
Transfer costs can be reduced by using local caches. Unlimited storage capacity, open protocols and
simple API for easy integration.
Rajkumar buyya (2008) proposes Grids computing[8] as a global cyber-infrastructure for the
future of e-Science applications, by integrating large-scale, distributed and heterogeneous resources.
a)utilizing resources that are located in a particular domain to increase throughput or reduce
execution costs, (b) execution spanning multiple administrative domains to obtain specific
processing capabilities, and (c) integration of multiple teams involved in management of different
parts of the experiment workflow – thus promoting inter-organizational collaborations.
Neizih Y, Alexandru Iosup et.al.(2009) proposes a new framework called as C-Meter[9] for
generating and submitting test workloads to computing clouds. IaaS providers are responsible for
maintaining the underlying infrastructure while at the same time minimizing the administration and
maintenance costs for the users. GrenchMark is the tool used before for Generating and submitting
synthetic of real workloads to Grid. It cannot be used for experiments with computing clouds. CMeter has developed as an extension of GrenchMark. The performance analysis of clod computing
requires the framework has to be able to generate and submit both real and synthetic workloads. It
should gather the statistic details of resource acquisition and release overhead. The framework
should have the capability to compare with other computing environments .In addition to these ,the
frameworks should provide basic resource management functionalities. Scheduling algorithms
implemented are round robin and a simple heuristic algorithm that selects the resources with
minimum predicted response time. Performance metrics take n for the evaluation is instance
throughput and network latency. Other issues related to response time, waiting time in the queue and
bounded slowdown with the threshold.
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4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 2, February (2014), pp. 60-70 © IAEME
Ewa Deelman, Gurmeet singh et al.,(2008) proposes Montage tool[10] when a science project
purchases a cluster, the cluster may be cost effective but it is fully dedicated to the needs of the
project. Teragrid[11] is a national level effort to provide a large-scale computation platform for
science.The reservations and uregent computing are available.Not all the resources are available at a
time. It is a data-intensive application .How can an application use cloud in a way that balance
between cost and performance? It is desirable to run the Montage application in a resource rich
environment where the assurance of the storage resources. storing the large data set in cloud is
needed for science. In Amazon[12] web services ,all the resources are there for immediate occupancy
and there is no advanced reservations.
.
IV. PERFORMANCE EVALUATION
Cloud computing offers a new business model for supporting data intensive applications and
provides a new horizon for scientific applications to have on-demand access to potentially large
amounts of storage and computing services.
Performance evaluation is necessary in cloud because so many number of cloud vendors
offering the services for computing and storage. Provision plans, workflow execution modes, start up
cost of the application which is a combination of launching and configuring a virtual machine and its
teardown. The reliability and availability of the storage and compute resources are also an important
point. Scalability is also a big issue which is need to be explored.
A workload trace[3] consists of a number of jobs. For each job the trace records consists of the
description about the job includes the size of the job, job’s submission, start and end time.
Performance metrics includes resource acquisition time and experiment cost. Performance
and cost metrics include the traditional metrics like[13]:wait time[WT],response time(ReT) and
bounded slowdown(BSD).
Performance analysis tools include Grid Workload Archive and DGSIM in cloud
environments.
Provisioning of the resources are also the major task. Appropriate scheduling algorithms have
to be found to get the work done quickly and correctly in cloud.
V. CONCLUSION
This paper shows a comprehensive performance analysis of large computing cloud in
particular science clouds. Till now the existing clouds is insufficient for computing and storage even
though IaaS model provides the needed resources temporarily and immediately.
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