A Manifestation of Cloud Computing Environments for Application Clusters, High-Performance Clusters and Allocation strategies for Virtual Machines

Authors

  • Kranthi Kumar. K  Information Technology, SNIST, Ghatkesar, Telangana, India
  • R. Rindha Reddy  Information Technology, SNIST, Ghatkesar, Telangana, India
  • Kurumaddali Sushmitha  Information Technology, SNIST, Ghatkesar, Telangana, India

DOI:

https://doi.org//10.32628/CSEIT1953145

Keywords:

Virtualization, Cloud Computing, cloud, Resource Allocation, VM arrangement, Cluster, HIPECO cluster, Cloud Resource.

Abstract

Cloud Computing (CC) is the advancement of the Grid Computing (GC) worldview in the direction of administration arranged structures. The phrasing connected to this sort of handling, while portraying shared resources, alludes to the idea of Service of X. Such assets are accessible on interest and at an altogether low cost contrasted with self-conveyance of individual segments. CC is found everywhere in current situations, from vast scale associations to a just little scale business, everybody is equipping themselves cloud. Due to its effortlessness, observing and support over remote association, expansive territory inclusion. Cloud can be any sort Software as an administration, stage as an administration, foundation as an administration dependent on its use. High Performance Computing (HIPECO) implies the accumulation of computational capacity to build the capacity of handling substantial issues in science, designing, and business. HIPECO on the cloud permits performing on interest HIPECO errands by superior clusters in a cloud atmosphere. Currently, CC arrangements (e.g., Microsoft Azure, Amazon EC2) enable the users to make use of only the fundamental storage and computational utilities. They prevent the allowance of custom adjustments of the topology designs or parameters of the system. The associations structures of the nodes in HIPECO clusters ought to give a quick bury node correspondence. It is vital that adaptability is safeguarded also. In a foundation, as an administration, virtualization viably maps virtual machines to the physical machines. In spite of the fact that it is difficult, undertaking for hypervisor to choose fitting host to serve up and coming virtual machine is a must requirement. In this paper, our main aim is to examine different techniques/types of cluster topology mapping and their necessities in numerous Cloud situations to accomplish higher dependability along with adaptability of utilization which is executed inside Cloud resources (CR), HIPECO resource allocation (RA) on the cloud clusters and Cluster based designation procedure.

References

  1. Sahba, A., & Prevost, J. J. (2016). Hypercube based clusters in CLOUD COMPUTING. 2016 World Automation Congress (WAC). doi:10.1109/wac.2016.75829741111111
  2. Y. Jadeja, K. Modi,(2012) “CLOUD COMPUTING - Concepts, Architecture and Challenges”, International Conference on Computing, Electronics and Electrical Technologies [ICCEET], 2012.
  3. Kosinska, J., Kosinski, J., & Zielinski, K. (2010). The Concept of Application Clustering in CLOUD COMPUTING Environments: The Need for Extending the Capabilities of Virtual Networks. 2010 Fifth International Multi-Conference on Computing in the Global Information Technology. doi:10.1109/iccgi.2010.34
  4. Peter Mell and Tim Grance. (2009)The NIST definition of CLOUD COMPUTING. Jul 2009.
  5. Intalio|Cloud Home Page: http://www.intalio.com. Accessed Jun 2010.
  6. Amazon EC2 Home Page: http://aws.amazon.com/ec2. Accessed May 2010.
  7. Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy H. Katz, Andrew Konwinski, Gunho Lee, David A. Patterson, Ariel Rabkin, Ion Stoica, and MateiZaharia. (2009)Above the clouds: A berkeley view of CLOUD COMPUTING. Technical Report UCB/EECS- 2009-28, EECS Department, University of California, Berkeley, February 2009
  8. Douglas Parkhill.(1996) The Challenge of the Computer Utility. AddisonWesley Educational Publishers Inc., US, 1966
  9. Liang-Jie Zhang and Qun Zhou.(2009)CCOA: CLOUD COMPUTING Architecture. IBM T.J. Watson Research Center. Proc. of IEEE International Conference on Web service,July 2009.
  10. Sun Cluster Home Page: http://www.sun.com/software/solaris/cluster Accessed Jun2010.
  11. Xuekun Kou.(2009)GlassFish Administration. Administer and configure the GlassFish v2 application server. 2009 PacktPublishing.
  12. Joanna Kosińska, Jacek Kosiński, and Krzysztof Zieliński.(2009)Virtual grid resource management system with virtualization technology. Proc. of The Conference of the High-Performance Computers’ Users, March2009.
  13. JXTAHomePage: http://jxta.dev.java.net.AccessedApr2010.
  14. JGroupsHomePage: http://www.jgroups.org.AccessedApr2010.
  15. Sun Microsystems. Sun Cluster Data Service for Sun Java System Application Server Guide for Solaris OS. Sun Developer Network. Technical Article. December2006.
  16. Richard Ellinga and Tim Read.(2001) Designing Enterprise Solutions with Sun Cluster 3.0. The official Sun Microsystems Resource Series. 2001.
  17. SunMicrosystems.SunClusterOverviewforSolarisOS.April2004.
  18. Sun Microsystems. Sun Cluster Concept Guide for Solaris OS. Dec2006.
  19. Sajay K R, &Babu, S. S. (2016). A study of CLOUD COMPUTING environments for High-Performance applications. 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE). doi:10.1109/sapience.2016.7684127
  20. Costaa,P.,Cruzb, (2015) A.:Migration to Windows Azure- analysis and comparison.In:CENTERIS 2012-Conference on ENTERprise Information Systems/HCIST 2012 – InternationalConferenceonHealthandSocialCare Information Systems and Technologies.Procedia Technol. 5, 93–102 (2012)
  21. http://www.wzl.rwth-aachen.de/en/index.html
  22. Google Apps.http://www.google.com/apps/intl/en/business/cloud.html
  23. Penguin Computing on Demand.http://www.penguincomputing.com/services/HiPeCo-cloud/POD
  24. R-Cloud.http://www.r-HiPeCo.com/
  25. Rajani, V., Shrimali, B., & Gohil, B. (2016). A VM allocation strategy for cluster of open host in cloud environment. 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT).doi:10.1109/icaccct.2016.7831704 Rajani, V., Shrimali, B., & Gohil, B. (2016). A VM allocation strategy for cluster of open host in cloud environment. 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT). doi:10.1109/icaccct.2016.7831704.
  26. Dillon, Tharam, Chen Wu, and Elizabeth Chang.(2010)"CLOUD COMPUTING: issues and challenges." Advanced Information Networking and Applications (AINA), 2010 24th IEEE International Conference on. Ieee, 2010.
  27. Perez-Botero, Diego, Jakub Szefer, and Ruby B. Lee. "Characterizing hypervisor vulnerabilities in CC servers." Proceedings of the 2013 international workshop on Security in CLOUD COMPUTING. ACM, 2013.
  28. Peng, Junjie, et al. "Comparison of several CLOUD COMPUTING platforms.(2009)" Information Science and Engineering (ISISE), 2009 Second International Symposium on. IEEE, 2009.
  29. Shrimali, Bela, and Hiren Patel.(2015) "Performance Based Energy Efficient Techniques For VM Allocation In Cloud Environment." Proceedings of the Third International Symposium on Women in Computing and Informatics. ACM, 2015.
  30. Xie, Ruitao, et al. "Energy saving virtual machine allocation in CLOUD COMPUTING.(2013)" Distributed Computing Systems Workshops (ICDCSW), 2013 IEEE 33rd International Conference on. IEEE, 2013.
  31. Shirinbab, Sogand, and Lars Lundberg.(2015)"Performance implications of over-allocation of virtual CPUs." Networks, Computers and Communications (ISNCC), 2015 International Symposium on. IEEE, 2015.
  32. Nagpure, Mahesh B., Prashant Dahiwale, and PunamMarbate.(2015) "An efficient dynamic resource allocation strategy for VM environment in cloud." Pervasive Computing (ICPC), 2015 International Conference on. IEEE, 2015.
  33. Ghribi, Chaima, MakhloufHadji, and DjamalZeghlache.(2013)"Energy efficient vm scheduling for cloud data centers: Exact allocation and migration algorithms." Cluster, Cloud and Grid Computing (CLOUD COMPUTINGGrid), 2013 13th IEEE/ACM International Symposium on. IEEE, 2013.
  34. Rocha, L. A., and F. L. Verdi.(2015) "A Network-Aware Optimization for VM Placement." Advanced Information Networking and Applications (AINA), 2015 IEEE 29th International Conference on. IEEE, 2015.
  35. Zhuang, Zhenyun, and Chun Guo. (2013) "OCPA: An Algorithm for Fast and Effective Virtual Machine Placement and Assignment in Large Scale Cloud Environments." CLOUD COMPUTING and Big Data (CloudCom-Asia), 2013 International Conference on. IEEE, 2013.
  36. McConnell, Aaron, et al.(2012) "A SLA-compliant Cloud resource allocation framework for N-tier applications." Cloud Networking (CLOUDNET), 2012 IEEE 1st International Conference on. IEEE, 2012.
  37. Iqbal, Waheed, Matthew N. Dailey, and David Carrera.(2010)"Sladriven dynamic resource management for multi-tier web applications in a cloud." Cluster, Cloud and Grid Computing (CLOUD COMPUTINGGrid), 2010 10th IEEE/ACM International Conference on. IEEE, 2010.
  38. Xiao, Zhen, Weijia Song, and Qi Chen.(2013) "Dynamic resource allocation using virtual machines for CLOUD COMPUTING environment." Parallel and Distributed Systems, IEEE Transactions on 24.6 (2013): 1107-1117

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Published

2019-06-30

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Section

Research Articles

How to Cite

[1]
Kranthi Kumar. K, R. Rindha Reddy, Kurumaddali Sushmitha, " A Manifestation of Cloud Computing Environments for Application Clusters, High-Performance Clusters and Allocation strategies for Virtual Machines, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 3, pp.596-612, May-June-2019. Available at doi : https://doi.org/10.32628/CSEIT1953145