Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(1023.33KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
High Availability based Migration Analysis to Cloud Computing for High Growth Businesses
Dilip K. Prasad
Pages - 35 - 52     |    Revised - 15-03-2012     |    Published - 16-04-2012
Volume - 4   Issue - 2    |    Publication Date - April 2012  Table of Contents
MORE INFORMATION
KEYWORDS
Cloud Computing, High Availability, Distributed Systems, Network Risk Matrix.
ABSTRACT
High availability requirement of the network is becoming essential for high growth disruptive technology companies. For businesses which require migration to networks supporting scalability and high availability, it is important to analyze the various factors and the cost effectiveness for choosing the optimal solution for them. The current work considers this important problem and presents an analysis of the important factors influencing the decision. The high availability of network is discussed using internal and external risk factors of the network. A production network risk matrix is proposed and a scheme to compute the overall risk is presented. A case study is presented in which four possible network configurations are analyzed and the most suitable solution is recognized. This study provides a paradigm and a useful framework for analyzing cloud computing services.
CITED BY (6)  
1 Sharkh, M. A., Shami, A., Ouda, A., & Kanso, A. (2015, November). Simulating High Availability Scenarios in Cloud Data Centers: A Closer Look. In 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom) (pp. 617-622). IEEE.
2 Edward, O. O. (2014). Cloud computing, its security issues and cost reduction factor: an introductory overview. Journal of Engineering Research and Design Vol, 2(2), 11-17.
3 Prasad, D. K. (2013). Geometric primitive feature extraction-concepts, algorithms, and applications. arXiv preprint arXiv:1305.3885.
4 Prasad, D. K. (2013). Object detection in real images. arXiv preprint arXiv:1302.5189.
5 Hutke, A., & Dakhane, D. M. High Availability for Data Storage in Cloud Computing.
6 Prasad, D. K. (2012). Survey of the problem of object detection in real images. International Journal of Image Processing (IJIP), 6(6), 441.
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 PdfSR
1 Amazon Inc. "Amazon Elastic Compute Cloud (Amazon EC2)," http://aws.amazon.com/ec2/, 2011, [Jan 15, 2012].
2 GoGrid. "GoGrid Cloud-Server Hosting," http://www.gogrid.com, 2011, [Jan 15, 2012].
3 A. Losup, O. Sonmez, S. Anoep et al., “The performance of bags-of-tasks in large-scale distributed systems,” in Proceedings of the 17th International Symposium on High Performance Distributed Computing 2008, HPDC'08, 2008, pp. 97-108.
4 I. Raicu, Z. Zhang, M. Wilde et al., “Toward loosely coupled programming on petascale systems,” in Proc. ACM Conf. Supercomputing (SC), 2008, pp. 22.
5 D. G. Feitelson, L. Rudolph, U. Schwiegelshohn et al., “Theory and practice in parallel job scheduling,” Job Scheduling Strategies for Parallel Processing, vol. 1291, pp. 1-34, 1997.
6 L. Youseff, R. Wolski, B. Gorda et al., "Paravirtualization for HPC Systems," Lecture Notes in Computer Science, pp. 474-486, 2006.
7 E. Deelman, G. Singh, M. Livny et al., “The cost of doing science on the cloud: The montage example,” in SC '08: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, 2008, pp. 1-12.
8 M. Palankar, A. Lamnitchi, M. Ripeanu et al., “Amazon S3 for science grids: A viable solution?,” International Symposium on High Performance Distributed Computing, HPDC 2008 - Proceedings of the 2008 International Workshop on Data-aware Distributed Computing 2008, DADC'08, pp. 55-64, 2008.
9 E. Walker, “Benchmarking amazon EC2 for high-performance scientific computing,” Login, vol. 33, no. 5, pp. 18-23, 2008.
10 L. Wang, J. Zhan, W. Shi et al., “In cloud, do mtc or htc service providers benefit from the economies of scale?,” in Proc. Second Workshop Many-Task Computing on Grids and Supercomputers (SC-MTAGS), 2009.
11 J. S. Vetter, S. R. Alam, T. H. D Jr et al., “Early evaluation of the cray XT3,” in Proc. 20th Int'l Conf. Parallel and Distributed Processing Symp. (IPDPS), 2006.
12 S. Saini, D. Talcott, D. C. Jespersen et al., “Scientific application-based performance comparison of SGI altix 4700, IBM POWER5+, and SGI ICE 8200 supercomputers,” in Proc. IEEE/ACM Conf. Supercomputing (SC), 2008, pp. 7.
13 T. H. Dunigan Jr, “Early Evaluation of the Cray X1,” in Proc. ACM/IEEE SC2003 Conf. (SC 03), 2003, pp. 18.
14 S. R. Alam, R. F. Barrett, M. Bast et al., “Early evaluation of IBM bluegene/P,” in Proc. ACM Conf. Supercomputing (SC), 2008, pp. 23.
15 F. Petrini, D. J. Kerbyson, and S. Pakin, “The case of the missing supercomputer performance: Achieving optimal performance on the 8,192 processors of ASCI Q,” in SC '03: Proceedings of the 2003 ACM/IEEE Conference on Supercomputing, 2003, pp. 55-55.
16 D. J. Kerbyson, A. Hoisie, and H. J. Wasserman, “A performance comparison between the Earth Simulator and other terascale systems on a characteristic ASCI workload,” Concurrency Computation Practice and Experience, vol. 17, no. 10, pp. 1219-1238, 2005.
17 A. Iosup, C. Dumitrescu, D. Epema et al., “How are real Grids used? The analysis of four Grid traces and its implications,” in Proceedings - IEEE/ACM International Workshop on Grid Computing, 2006, pp. 262-269.
18 R. Biswas, M. J. Djomehri, R. Hood et al., “An application-based performance characterization of the columbia supercluster,” in Proc. IEEE Conf. Supercomputing (SC), 2005, pp. 26.
19 A. Iosup, and D. Epema, “GRENCHMARK: A framework for analyzing, testing, and comparing grids,” in Sixth IEEE International Symposium on Cluster Computing and the Grid, 2006. CCGRID 06, 2006, pp. 313-320.
20 S. Williams, J. Shalf, L. Oliker et al., “The potential of the cell processor for scientific computing,” in Proceedings of the 3rd Conference on Computing Frontiers 2006, CF '06, 2006, pp. 9-20.
21 D. Nurmi, R. Wolski, C. Grzegorczyk et al., The Eucalyptus Open-source Cloud-computing System, vol. 2011, 2008.
22 B. Quétier, V. Neri, and F. Cappello, “Scalability comparison of four host virtualization tools,” Journal of Grid Computing, vol. 5, no. 1, pp. 83-98, 2007.
23 P. Barham, B. Dragovic, K. Fraser et al., “Xen and the art of virtualization,” Operating Systems Review (ACM), vol. 37, no. 5, pp. 164-177, 2003.
24 B. Clark, T. Deshane, E. Dow et al., “Xen and the art of repeated research,” in USENIX Annual Technical Conference, FREENIX Track, 2004, pp. 135-144.
25 A. Menon, J. R. Santos, Y. Turner et al., “Diagnosing performance overheads in the xen virtual machine environment,” in Proceedings of the First ACM/USENIX International Conference on Virual Execution Environments, VEE 05, 2005, pp. 13-23.
26 N. Sotomayor, K. Keahey, and I. Foster, “Overhead matters: A model for virtual resource management,” in Proc. IEEE First Int'l Workshop Virtualization Technology in Distributed Technology (VTDC), 2006, pp. 4-11.
27 A. B. Nagarajan, F. Mueller, C. Engelmann et al., “Proactive fault tolerance for HPC with Xen virtualization,” in Proceedings of the International Conference on Supercomputing, 2007, pp. 23-32.
28 L. Youseff, K. Seymour, H. You et al., “The impact of paravirtualized memory hierarchy on linear algebra computational kernels and software,” in Proceedings of the 17th International Symposium on High Performance Distributed Computing 2008, HPDC'08, 2008, pp. 141- 152.
29 W. Yu, and J. S. Vetter, “Xen-based HPC: A parallel I/O perspective,” in Proceedings CCGRID 2008 - 8th IEEE International Symposium on Cluster Computing and the Grid, 2008, pp. 154-161.
30 L. Cherkasova, and R. Gardner, “Measuring CPU Overhead for I/O Processing in the Xen Virtual Machine Monitor,” in Proceedings of the USENIX Annual Technical Conference, 2005, pp. 387-390.
31 U. F. Minhas, J. Yadav, A. Aboulnaga et al., “Database systems on virtual machines: How much do you lose?,” in Proceedings - International Conference on Data Engineering, 2008, pp. 35-41.
32 W. Huang, J. Liu, B. Abali et al., “A case for high performance computing with virtual machines,” in Proceedings of the International Conference on Supercomputing, 2006, pp. 125-134.
33 L. Gilbert, J. Tseng, R. Newman et al., “Performance implications of virtualization and hyper-threading on high energy physics applications in a grid environment,” in Proc. IEEE 19th Int'l Parallel and Distributed Processing Symp. (IPDPS), 2005.
34 J. Zhan, L. Wang, B. Tu et al., “Phoenix cloud: Consolidating different computing loads on shared cluster system for large organization,” in Proc. First Workshop Cloud Computing and Its Application (CCA '08) Posters, 2008, pp. 7-11.
35 M.-E. Bgin, B. Jones, J. Casey et al., An EGEE comparative study: Grids and Clouds - Evolution or revolution?, 2008.
36 C. Evangelinos, and C. Hill, “Cloud Computing for Parallel Scientific HPC Applications: Feasibility of running Coupled Atmosphere-Ocean Climate Models on Amazon EC2,” Ratio, vol. 2, pp. 2-34, 2008.
37 L. Youseff, M. Butrico, and D. Da Silva, “Toward a unified ontology of cloud computing,” Grid Computing Environments Workshop, pp. 1-10, 2008.
38 M. Armbrust, Above the clouds: A berkeley view of cloud computing, 2009.
39 R. Prodan, and S. Ostermann, “A survey and taxonomy of infrastructure as a service and web hosting cloud providers,” in Proc. Int'l Conf. Grid Computing, 2009, pp. 1-10.
40 Oracle Inc. "Oracle Cloud Computing," http://www.oracle.com/us/technologies/cloud/index.html, 2011, [Dec 11, 2011].
41 HostMySite. "Hosting Cloud Hosting," http://www.hostmysite.com/cloud/, 2011, [Jan 15, 2012].
42 S. O. Kuyoro, F. Ibikunle, and O. Awodele, “Cloud Computing Security Issues and Challenges,” International Journal of Computer Networks (IJCN), vol. 3, no. 5, pp. 247-255, 2011.
43 D. K. Prasad, “Adaptive traffic signal control system with cloud computing based online learning,” in Eighth International Conference on Information, Communications, and Signal Processing (ICICS 2011), Singapore, 2011.
Mr. Dilip K. Prasad
- India
dilipprasad@gmail.com