Cloud Computing: A Perspective Study

Rochester Institute of Technology
RIT Scholar Works
Presentations and other scholarship
2008
Cloud Computing: A Perspective Study
Lizhe Wang
Rochester Institute of Technology
Gregor von Laszewski
Rochester Institute of Technology
Marcel Kunze
Karlsruhe Institute of Technology
Jie Tao
Kalrsruhe Institute of Technology
Follow this and additional works at: http://scholarworks.rit.edu/other
This Conference Proceeding is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Presentations and
other scholarship by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]
Recommended Citation
Wang, L., von Laszewski, G., Younge, A. et al. New Gener. Comput. (2010) 28: 137. https://doi.org/10.1007/s00354-008-0081-5
Cloud Computing: a Perspective Study
Lizhe WANG, Gregor VON LASZEWSKI
Service Oriented Cyberinfrastruture Lab, Rochester Inst. of Tech.
102 Lomb Memorial Drive, Rochester, NY 14623, U.S.
Lizhe.Wang, [email protected]
Marcel KUNZE, Jie TAO
Steinbuch Centre for Computing, Karlsruhe Institute of Technology
Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, Germany
Marcel.Kunze, [email protected]
Received 1 Dec 2008
Abstract The Cloud computing emerges as a new computing paradigm
which aims to provide reliable, customized and QoS guaranteed dynamic
computing environments for end-users. In this paper, we study the Cloud
computing paradigm from various aspects, such as definitions, distinct
features, and enabling technologies. This paper brings an introductional
review on the Cloud computing and provide the state-of-the-art of Cloud
computing technologies.
Keywords Cloud Computing, Grid Computing, Cyberinfrastructure,
Distributed Computing
§1 Introduction
The Cloud computing, which was coined in late of 2007, currently emerges
as a hot topic due to its abilities to offer flexible dynamic IT infrastructures, QoS
guaranteed computing environments and configurable software services. As reported in the Google trends shown in Figure 1, the Cloud computing (the blue
2 Marcel KUNZE, Jie TAO
Fig. 1 Cloud computing in Google trends
line), which is enabled by virtualization technology (the yellow line), has already
outpaced the Grid computing 8) (the red line).
Numerous projects within industry and academia have already started,
for example the RESERVOIR project 27) – an IBM and European Union joint
research initiative for Cloud computing, Amazon Elastic Compute Cloud 13),
IBM’s Blue Cloud 10), scientific Cloud projects such as Nimbus 24) and Stratus 31),
and OpenNEbula 26). HP, Intel Corporation and Yahoo! Inc. recently announced
the creation of a global, multi-data center, open source Cloud computing test
bed for industry, research and education 3).
There are still no widely accepted definitions for the Cloud computing albeit the Cloud computing practice has attracted much attention. Several
reasons lead into this situation:
• Cloud computing involves researchers and engineers from various backgrounds, e.g., Grid computing, software engineering and database. They
work on Cloud computing from different viewpoints.
• Technologies which enable the Cloud computing are still evolving and
progressing, for example, Web 2.0 and Service Oriented Computing.
• Existing computing Clouds still lack large scale deployment and usage,
which would finally justify the concept of Cloud computing.
In this paper we attempt to contribute the concept of Cloud computing: definition, functionality, enabling technology and typical applications. The
remaining parts of this paper are organized as follows. Section 2 discusses the
concept of Cloud computing, Section 3 presents the functionalities of the Cloud
computing, Section 4 reviews the distinct features of the Cloud computing, and
Cloud Computing: a Perspective Study 3
Section 5 enumerates the enabling technologies for building computing Clouds.
Section 6 concludes the whole paper.
§2 Definition of Cloud Computing
Cloud computing is becoming one of the next IT industry buzz words:
users move out their data and applications to the remote “Cloud” and then access
them in a simple and pervasive way. This is again a central processing use case.
Similar scenario occurred around 50 years ago: a time-sharing computing server
served multiple users. Until 20 years ago when personal computers came to us,
data and programs were mostly located in local resources. Certainly currently
the Cloud computing paradigm is not a recurrence of the history. 50 years ago
we had to adopt the time-sharing servers due to limited computing resources.
Nowadays the Cloud computing comes into fashion due to the need to build
complex IT infrastructures. Users have to manage various software installations,
configuration and updates. Computing resources and other hardware are prone
to be outdated very soon. Therefore outsourcing computing platforms is a smart
solution for users to handle complex IT infrastructures.
At the current stage, the Cloud computing is still evolving and there
exists no widely accepted definition. Based on our experience, we propose an
early definition of Cloud computing as follows:
A computing Cloud is a set of network enabled services, providing scalable, QoS guaranteed, normally personalized, inexpensive computing infrastructures on demand, which could be accessed in a simple and pervasive way.
§3 Functional Aspects of Cloud Computing
Conceptually, users acquire computing platforms or IT infrastructures
from computing Clouds and then run their applications inside. Therefore, computing Clouds render users with services to access hardware, software and data
resources, thereafter an integrated computing platform as a service, in a transparent way:
Hardware as a Service (HaaS):
Hardware as a Service was coined possibly in 2006. As the result of
rapid advances in hardware virtualization, IT automation and usage
metering & pricing, users could buy IT hardware, or even an entire
4 Marcel KUNZE, Jie TAO
data center, as a pay-as-you-go subscription service. The HaaS is
flexible, scalable and manageable to meet your needs 2). Examples
could be found at Amazon EC2 13), IBM’s Blue Cloud project 10),
Nimbus 24), Eucalyptus 18) and Enomalism 17).
Software as a Service (SaaS):
Software or an application is hosted as a service and provided to customers across the Internet. This mode eliminates the need to install
and run the application on the customer’s local computers. SaaS
therefore alleviates the customer’s burden of software maintenance,
and reduces the expense of software purchases by on-demand pricing.
An early example of the SaaS is the Application Service Provider
(ASP) 15). The ASP approach provides subscriptions to software
that is hosted or delivered over the Internet. Microsoft’s “Software +
Service” 30) shows another example: a combination of local software
and Internet services interacting with one another. Google’s Chrome
browser 21) gives an interesting SaaS scenario: a new desktop could be
offered, through which applications can be delivered (either locally or
remotely) in addition to the traditional Web browsing experience.
Data as a Service (DaaS):
Data in various formats and from multiple sources could be accessed
via services by users on the network. Users could, for example, manipulate the remote data just like operate on a local disk or access the
data in a semantic way in the Internet.
Amazon Simple Storage Service (S3) 14) provides a simple Web services interface that can be used to store and retrieve, declared by
Amazon, any amount of data, at any time, from anywhere on the
Web. The DaaS could also be found at some popular IT services,
e.g., Google Docs 22) and Adobe Buzzword 12). ElasticDrive 16) is a
distributed remote storage application which allows users to mount a
remote storage resource such as Amazon S3 as a local storage device.
Based on the support of the HaaS, SaaS and DaaS, the Cloud computing
in addition can deliver the Infrastructure as a Service (IaaS) for users. Users
thus can on-demand subscribe to their favorite computing infrastructures with
requirements of hardware configuration, software installation and data access
demands. Figure 2 shows the relationship between the services. The Google
Cloud Computing: a Perspective Study 5
App Engine 20) is an interesting example of the IaaS. The Google App Engine
enables users to build Web applications with Google’s APIs and SDKs across
the same scalable systems, which power the Google applications.
SaaS HaaS DaaS
Cloud resoruce
Application
Scientific Cloud
IaaS
Fig. 2 Cloud functionalities
§4 Why is Cloud Computing Distinct?
The Cloud computing distinguishes itself from other computing paradigms,
like Grid computing, Global computing, Internet Computing in the following aspects:
User-centric interfaces.
Cloud services should be accessed with simple and pervasive methods.
In fact, the Cloud computing adopts the concept of Utility computing. In other words, users obtain and employ computing platforms in
computing Clouds as easily as they access a traditional public utility (such as electricity, water, natural gas, or telephone network). In
detail, the Cloud services enjoy the following features:
– The Cloud interfaces do not force users to change their working
habits and environments, e.g., programming language, compiler and operating system. This feature differs Cloud computing from Grid computing as Grid users have to learn new Grid
commands & APIs to access Grid resources & services.
– The Cloud client software which is required to be installed locally is lightweight. For example, the Nimbus Cloudkit client
6 Marcel KUNZE, Jie TAO
size is around 15MB.
– Cloud interfaces are location independent and can be accessed
by some well established interfaces like Web services framework
and Internet browser.
On-demand service provisioning.
Computing Clouds provide resources and services for users on demand.
Users can customize and personalize their computing environments
later on, for example, software installation, network configuration, as
users usually own administrative privileges.
QoS guaranteed offer.
The computing environments provided by computing Clouds can guarantee QoS for users, e.g., hardware performance like CPU speed, I/O
bandwidth and memory size.
The computing Cloud renders QoS in general by processing Service
Level Agrement (SLA) with users – a negotiation on the levels of
availability, serviceability, performance, operation, or other attributes
of the service like billing and even penalties in the case of violation of
the SLA.
Autonomous System.
The computing Cloud is an autonomous system and it is managed
transparently to users. Hardware, software and data inside clouds
can be automatically reconfigured, orchestrated and consolidated to
present a single platform image, finally rendered to users.
Scalability and flexibility.
The scalability and flexibility are the most important features that
drive the emergence of the Cloud computing. Cloud services and
computing platforms offered by computing Clouds could be scaled
across various concerns, such as geographical locations, hardware performance, software configurations. The computing platforms should
be flexible to adapt to various requirements of a potentially large number of users.
§5 Enabling Technologies behind Cloud Computing
A number of enabling technologies contribute to Cloud computing, sev
Cloud Computing: a Perspective Study 7
eral state-of-the-art techniques are identified here:
Virtualization technology.
Virtualization technologies partition hardware and thus provide flexible and scalable computing platforms. Virtual machine techniques,
such as VMware 34) and Xen 1), offer virtualized IT-infrastructures on
demand. Virtual network advances, such as VPN 7), support users
with a customized network environment to access Cloud resources.
Virtualization techniques are the bases of the Cloud computing since
they render flexible and scalable hardware services.
Orchestration of service flow and workflow.
Computing Clouds offer a complete set of service templates on demand, which could be composed by services inside the computing
Cloud. Computing Clouds therefore should be able to automatically
orchestrate services from different sources and of different types to
form a service flow or a workflow transparently and dynamically for
users.
Web service and Service Oreinted Architecture (SOA).
Computing Cloud services are normally exposed as Web services,
which follow the industry standards such as WSDL 33), SOAP 28) and
UDDI 25). The services organization and orchestration inside Clouds
could be managed in a Service Oriented Architecture (SOA). A set of
Cloud services furthermore could be used in a SOA application environment, thus making them available on various distributed platforms
and could be further accessed across the Internet.
Web 2.0.
Web 2.0 is an emerging technology describing the innovative trends
of using World Wide Web technology and Web design that aims to
enhance creativity, information sharing, collaboration and functionality of the Web 6). The essential idea behind Web 2.0 is to improve
the interconnectivity and interactivity of Web applications. The new
paradigm to develop and access Web applications enables users access
the Web more easily and efficiently. Cloud computing services in nature are Web applications which render desirable computing services
on demand. It is thus a natural technical evolution that the Cloud
computing adopts the Web 2.0 technique.
8 Marcel KUNZE, Jie TAO
World-wide distributed storage system.
A Cloud storage model should foresee:
– A network storage system, which is backed by distributed storage providers (e.g., data centers), offers storage capacity for
users to lease. The data storage could be migrated, merged,
and managed transparently to end users for whatever data formats. Examples are Google File System 9) and Amazon S3 14).
A Mashup 11) is a Web application that combines data from
more than one source into a single integrated storage tool. The
SmugMug 29) is an example of Mashup, which is a digital photo
sharing Web site, allowing the upload of an unlimited number of
photos for all account types, providing a published API which
allows programmers to create new functionality, and supporting XML-based RSS and Atom feeds.
– A distributed data system which provides data sources accessed
in a semantic way. Users could locate data sources in a large
distributed environment by the logical name instead of physical
locations. Virtual Data System (VDS) 32) is good reference.
Programming model.
Users drive into the computing Cloud with data and applications.
Some Cloud programming models should be proposed for users to
adapt to the Cloud infrastructure. For the simplicity and easy access
of Cloud services, the Cloud programming model, however, should not
be too complex or too innovative for end users.
The MapReduce 4, 5) is a programming model and an associated implementation for processing and generating large data sets across the
Google worldwide infrastructures. The MapReduce model firstly involves applying a “map” operation to some data records – a set of
key/value pairs, and then processes a “reduce” operation to all the
values that shared the same key. The Map-Reduce-Merge 35) method
evolves the MapReduce paradigm by adding a “merge” operation.
Hadoop 23) is a framework for running applications on large clusters built of commodity hardware. It implements the MapReduce
paradigm and provides a distributed file system – the Hadoop Distributed File System. The MapReduce and the Hadoop are adopted
by recently created international Cloud computing project of Yahoo!,
Cloud Computing: a Perspective Study 9
Intel and HP 3, 19).
§6 Conclusion
This paper reviews the recent advances of Cloud computing and presents
our views on Cloud computing: definition, key features and enabling technologies. The perspective study aims to contribute the evolution of the Cloud computing paradigm.
10 Marcel KUNZE, Jie TAO
References
1) P. Barham, B. Dragovic, K. Fraser, S. Hand, T. L. Harris, A. Ho, R. Neugebauer,
I. Pratt, and A. Warfield. Xen and the art of virtualization. In Proceedings
of the 19th ACM Symposium on Operating Systems Principles, pages 164–177,
New York, U. S. A., Oct. 2003.
2) Here comes HaaS [URL].
http://www.roughtype.com/archives/2006/03/here comes haas.php/,
access on June 2008.
3) Global Cloud computing test bed [URL].
http://www.hp.com/hpinfo/newsroom/press/2008/080729xa.html/, access on
July 2008.
4) J. Dean. Mapreduce and other building blocks for large-scale distributed systems at google. In Proceedings of the USENIX Annual Technical Conference,
2007.
5) J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large
clusters. Commun. ACM, 51(1):107–113, 2008.
6) Web 2.0 definition [URL].
http://en.wikipedia.org/wiki/web 2/, access on June 2008.
7) B. Gleeson etc. A framework for IP based virtual private networks. RFC2764,
The Internet Engineering Task Force, Feb. 2000.
8) I. Foster and C. Kesselman. The Grid: blueprint for a new computing infrastructure. Morgan Kaufmann, 1998.
9) S. Ghemawat, H. Gobioff, and S. Leung. The Google file system. In Proceedings
of the 19th ACM Symposium on Operating Systems Principles, pages 29–43,
2003.
10) IBM Blue Cloud project [URL].
http://www-03.ibm.com/press/us/en/pressrelease/22613.wss/,
access on June 2008.
11) Mashup project[URL].
http://en.wikipedia.org/wiki/mashup web application hybrid/, access on June
2008.
12) Adobe Buzzword [URL].
http://www.adobe.com/acom/buzzword/, access on Sep. 2008.
13) Amazon Elastic Compute Cloud [URL].
http://aws.amazon.com/ec2/, access on Nov. 2007.
14) Amazon Simple Storage Service [URL].
http://aws.amazon.com/s3/, access on Sep. 2008.
15) Application Service Provider [URL].
http://msdn.microsoft.com/en-us/architecture/aa699384.aspx/,
access on Sep. 2008.
16) Elasticdrive Project [URL]. http://www.elasticdrive.com/, access on Sep. 2008.
17) Enomalism Project [URL]. http://www.enomalism.com/, access on Sep. 2008.
18) Eucalyptus Project [URL]. http://eucalyptus.cs.ucsb.edu/, access on Sep. 2008.
Cloud Computing: a Perspective Study 11
19) Global Cloud Computing Research Test Bed Wiki [URL].
http://cloudtestbed.org/,
access on Oct. 2008.
20) Google App Engine [URL].
http://code.google.com/appengine/, access on Sep. 2008.
21) Google Chrome [URL]. http://www.google.com/chrome/, access on Sep. 2008.
22) Google Docs [URL]. http://docs.google.com/, access on Sep. 2008.
23) Hadoop [URL]. http://hadoop.apache.org/, access on Sep. 2008.
24) Nimbus Project [URL].
http://workspace.globus.org/clouds/nimbus.html/, access on June 2008.
25) OASIS UDDI Specification [URL].
http://www.oasis-open.org/committees/uddi-spec/doc/tcspecs.htm,
access on June 2008.

AssignmentTutorOnline

27) Reservoir Project [URL]. http://www-03.ibm.com/press/us/en/pressrelease/23448.wss/,
access on June 2008.
28) Simple Object Access Protocol (SOAP) [URL].
http://www.w3.org/tr/soap/, access on Nov. 2007.
29) SmugMug [URL]. http://www.smugmug.com/, access on June 2008.
30) Software + Services [URL].
http://www.microsoft.com/softwareplusservices/, access on June 2008.
31) Status Project [URL]. http://www.acis.ufl.edu/vws/, access on June 2008.
32) Virtual Data System [URL]. http://vds.uchicago.edu/, access on Nov. 2007.
33) Web Service Description Language (WSDL) [URL].
http://www.w3.org/tr/wsdl/, access on Nov. 2007.
34) VMware virtualization technology [URL].
http://www.vmware.com, access on Nov. 2007.
35) H. Yang, A. Dasdan, R. Hsiao, and D. S. Parker Jr. Map-reduce-merge: simplified relational data processing on large clusters. In Proceedings of the ACM
SIGMOD International Conference on Management of Data, pages 1029–1040,
2007.