Brief summary on cloud computing

advertisement
Brief summary on cloud computing
Brief summary on cloud computing
Abstract. This paper provides a brief summary on cloud computing.
Summary
Cloud computing is considered as one of most accomplished and ubiquitous paradigms in 21st century,
especially for the service related computing. It has been credited for the revolution, it has brought by
abstracting the complex and expansive computing infrastructure underneath. Though, in service-oriented
age, there are only three cloud paradigms that have attained significant popularity -- Infrastructure-as-aService (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). Elasticity in service usage
and consequently price (pay-per-use), low financial inputs upfront, reasonably reduced time to market
(TTM), responsibility shift in risk management, etc. are some of the potential convincing attributes of
cloud computing. Such features attribute it as pervasive, economical, and a quintessential paradigm that is
highly suitable for novel applications deployment. Such economic feasibility is very far from reality under
the ambit of any traditional infrastructure environment; as a result of which, more and more applications
are migrating into cloud environment. This results a sharp spike in the scale of the data traffic through,
which indicates that the voluminous scalable data, storage, and their processing models are important
components of the cloud environment. In sum, cloud computing is a financially economical but
technologically robust invention that is designed for complex and overly large scale computations; which
relinquishes any need for a dedicated space, and onsite management of expensive hardware infrastructure
and delicate software systems. In an informal way, cloud computing can be defined as the pay-per-use
service of the highly scalable computing resources, offered from an outer environment by a third party
vendor. A resource-rich cloud environment may house thousands of resources, but a client (or a user) is
just billed only for those resources that are requested or used by him. However, at any point of time, and
from any geographical location, where Internet accessibility is available, any resources, active under the
contractual agreement, can be accessed. A cloud service provider is solely responsible for the
administration of infrastructure, constituted by physical hardware and expansive software; a client is
absolutely free from such burdens. In this section, a general classification of some of the leading cloud
services is provided. Each class is briefly described. SaaS is one of the most popular, repository-rich and
widely used cloud model that is offering the services for more than a decade now. The service repository is
much diversified and cover a very wide range of simple to complex services such as Google Email, Google
Doc, etc. Under the contractual agreement of SaaS model, the vendor also called the service provider is
fully responsible to provide all the essential infrastructure consisting of the robust hardware resources and
expansive software systems. As, the name suggests, IaaS offers the infrastructure as service. The
infrastructure consists of various building blocks, which can be combined or layered to derive a
customized environment most appropriate to execute the designated applications. Some of the most
popular examples of IaaS cloud model include Amazon Web Services (AWS), Rackspace, etc. In the PaaS
cloud model, the service provider is responsible to provide the risk free and robust environment for
software product development. The environment consists of required software tools and is hosted on the
hardware infrastructure of the service provider.
Conclusion. This paper was a one pager on cloud computing.
References
Sharma, S. (2016). Expanded cloud plumes hiding Big Data ecosystem. Future Generation Computer
Systems, 59, 63-92.
Sharma, S., Chang, V., Tim, U. S., Wong, S, Gadia, S. (2016). Cloud-based Emerging Services Systems.
International Journal of Information Management, Elsevier.
Sharma, S. (2016). Concept of Association Rule of Data Mining Assists Mitigating the Increasing Obesity.
International Journal of Information Retrieval Research (IJIRR), IGI Global, 2016.
1
Brief summary on cloud computing
Sharma, S., Chang, V., Tim, U. S., Wong, S, Gadia, S. (2016). Growing Cloud Density & as-a-Service
Modality and OTH-Cloud Classification in IOT Era.
Sharma, S., Tim, U. S., Gadia, S., & Wong, J. (2016). Proliferating Cloud Density through Big Data
Ecosystem, Novel XCLOUDX Classification and Emergence of as-a-Service Era.
Sharma, S. (2015). Evolution of as-a-Service Era in Cloud. arXiv preprint arXiv:1507.00939.
Sharma, S., Tim, U. S., Payton, M., Cohly, H., Gadia, S., Wong, J., & Karakala, S. (2015). Contextual
motivation in physical activity by means of association rule mining. Egyptian Informatics Journal, 16(3),
243-251.
Sharma, S., Gadia, S., Tim, U. S. (2016). SubVizCon: Visual Subsetting, Conversion and Complex Queries
Exploitation in Spatio-Temporal Compound of Big Data.
Sharma, S., Tim, U. S., Gadia, S., Wong, J., Shandilya, R., & Peddoju, S. K. (2015). Classification and
comparison of NoSQL big data models. International Journal of Big Data Intelligence, 2(3), 201-221.
Sharma, S., Shandilya, R., Patnaik, S., & Mahapatra, A. (2015). Leading NoSQL models for handling Big
Data: a brief review. International Journal of Business Information Systems, Inderscience, 18(4).
Sharma, S., Tim, U. S., Wong, J., Gadia, S., & Sharma, S. (2014). A brief review on leading big data
models. Data Science Journal, 13(0), 138-157.
Sharma, S., Tim, U. S., Gadia, S., & Wong, J. (2014). Does SNAP eligibility have racial or ethnic gradients:
a geospatial social exploratory. International Journal of Information and Communication Technology,
6(2), 189-212.
Sharma, S. (2014). Racial disparity in SNAP? No: a geospatial study for Iowa. International Journal of
Indian Culture and Business Management, 9(3), 340-352.
SUGAM, S., SHASHI, G., & UDOYARA SUNDAY, T. (2013). Parametric database approach integration for
handling temporal data in GIS. Geo-spatial Information Science, 16(2), 91-99.
SHARMA, S., TIM, U. S., & GADIA, S. (2012). AutoConViz: automating the conversion and visualization
of spatio-temporal query results in GIS. Geo-spatial Information Science, 15(2), 85-93.
Sharma, S., Wong, J., Tim, U. S., & Gadia, S. (2013). Bidirectional migration between variability and
commonality in product line engineering of smart homes. International Journal of System Assurance
Engineering and Management, 4(1), 1-12.
Sharma, S., Goyal, S. B., Shandliya, R., & Samadhiya, D. (2012). Towards XML Interoperability. In
Advances in Computer Science, Engineering & Applications (pp. 1035-1043). Springer Berlin Heidelberg.
Shandilya, R., Sharma, S., & Qamar, S. (2012). A Domain Specific Indexing Technique for Hidden Web
Documents. Communications in Information Science and Management Engineering.
Sharma, S., Tim, U. S., Gadia, S., & Smith, P. (2011). Geo-spatial Pattern Determination for SNAP
Eligibility in Iowa Using GIS. In Advances in Computing and Communications (pp. 191-200). Springer
Berlin Heidelberg.
Sharma, S., Yang, H. I., Wong, J., & Chang, C. K. (2011). Wrenching: transient migration from commonality
to variability in product line engineering of smart homes. In Toward Useful Services for Elderly and People
with Disabilities (pp. 230-235). Springer Berlin Heidelberg.
Meghanathan, N., Sharma, S., & Skelton, G. W. (2010). On Energy Efficient Dissemination in Wireless
sensor Networks using Mobile Sinks. Journal of Theoretical and Applied Information Technology, 19(2), 7991.
Sharma, S., Gadia, S., Kumar, N., Narayanan, V., & Zhao, X. (2010). Climate Analysis in IOWA Using
XML and Spatio-Temporal Dataset-NC94. Int. J. Database Manage. Syst, 2(3), 82-93.
Sharma, S., & Gadia, S. (2010). Perl Status Reporter (SRr) on Spatiotemporal Data Mining. International
Journal Computer Science and Engineering Survey, AIRCC-IJCSES (August 2010).
2
Brief summary on cloud computing
Sharma, S., & Gadia, S. K. (2010). On analyzing the degree of coldness in Iowa, a north central region,
United States: An XML exploitation in spatial databases. In Recent Trends in Network Security and
Applications (pp. 613-624). Springer Berlin Heidelberg.
Sharma, S., Gadia, S., & Goyal, S. B. (2010, September). On the Calculation of Coldness in Iowa, a North
Central Region, United States: A Summary on XML Based Scheme. In Information and Communication
Technologies (pp. 400-405). Springer Berlin Heidelberg.
Sharma, S., & Gadia, S. K. (2010, April). An XML-based range variation approach to render the coldness in
Iowa, a North Central region, United States. In Information Management and Engineering (ICIME), 2010
The 2nd IEEE International Conference on (pp. 567-571). IEEE.
Sharma, S., Goyal, S. B., & Qamar, S. (2009, December). Four-Layer Architecture Model for Energy
Conservation in Wireless Sensor Networks. In Embedded and Multimedia Computing, 2009. EM-Com
2009. 4th International Conference on (pp. 1-3). IEEE.
Sharma, S., & Cohly, H. (2009, December). On enhancing the energy conservation by ATIM window in
wireless sensor networks. In Internet Multimedia Services Architecture and Applications (IMSAA), 2009
IEEE International Conference on (pp. 1-4). IEEE.
Sharma, S., & Rani, M. (2009, October). Three-layer architecture model (TLAM) for energy conservation
in wireless sensor networks. In Ultra Modern Telecommunications & Workshops, 2009. ICUMT'09.
International Conference on (pp. 1-5). IEEE.
Sharma, S., Rani, M., & Goyal, S. B. (2009, October). Energy efficient data dissemination with ATIM
window and dynamic sink in wireless sensor networks. In Advances in Recent Technologies in
Communication and Computing, 2009. ARTCom'09. International Conference on (pp. 559-564). IEEE.
Sharma, S., Cohly, H., & Pei, T. (2010). On Generation of Firewall Log Status Reporter (SRr) Using Perl.
arXiv preprint arXiv:1004.0604.
Sharma, S., Pei, T., & Cohly, H. (2010). On Utilization and Importance of Perl Status Reporter (SRr) in
Text Mining. arXiv preprint arXiv:1001.3277.
Meghanathan, N., Sugam, S., & Skelton, G. W. (2008). Use of Mobile Sinks to Disseminate Data in WSNs.
International Journal of Information Processing, 2(2).
SHARMA, S., PEI, T., & COHLY, H. (2008). ToAccess PubMed Database to Extract Articles Using
Perl_Su. IJCSES, 2(2), 92.
Challa, V. S., Indracanti, J., White, L. D., Sharma, S., Baham, J. M., Rabarison, M. K., & Anjaneyulu, Y.
(2007, September). Study of Meso-Scale Coastal Circulations in Mississippi Gulf coast with Mesonet
Observations and Modeling. In Seventh Conference on Coastal Atmospheric and Oceanic Prediction and
Processes.
Singh, S. P., Bhanot, K., & Sharma, S. (2016). Critical Analysis of Clustering Algorithms for Wireless
Sensor Networks. In Proceedings of Fifth International Conference on Soft Computing for Problem
Solving (pp. 783-793). Springer Singapore.
Sharma, S. (2015). An Extended Classification and Comparison of NoSQL Big Data Models. arXiv
preprint arXiv:1509.08035.
3
Download