Resource provisioning methodology for cloud environment with producer and consumer favorable: an approach of virtual cloud compiler
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2018-03-10 https://doi.org/10.14419/ijet.v7i2.4.13022 -
Cloud Computing, Heavy Computational Task. Payload Distribution, Virtual Compiler -
Abstract
Cloud computing is a model where traditional resources such as CPU cycles, storage, security etc. are delivered through web based. It is a technology which has ability to change large part of software development cycle, 3D rendering or any other computationally expensive tasks execution. Much amount of time is wasted on compiling and rendering such computationally expensive tasks due to low power machines, which directly proportional to efficiency of user who is working on that project. Extreme computational tasks such as weather forecast, DNA analyses, encryption breaking takes so much time in consumer grade computing devices that they are realistically not possible to perform. We have proposed a novel approach to perform payload distribution, for the users who wanted to run their computationally expensive tasks efficiently. We have used virtualization technique on data center resources to perform scheduling. Up to 32% cost has been reduced in an environment of 30 users when our technology used instead of traditional standalone desktop environment. This is achieved by replacing 30 standalone computers with a powerful server and thin clients like Raspberry pi as clients. Time wasted in computational task such as rendering and compiling is greatly reduced. We have not only improved the efficiency, but also make sure both cloud producer and consumer are favorable. With simulations and outcomes, we validate that our methodology for payload distribution performs well.
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References
[1] Mell, P., & Grance, T. “The NIST Definition of Cloud Computing†(Draft) Recommendations of the National Institute of Standards and Technology. Nist Special, 145(6), [7]. National Institute of Standards and Technology, Information Technology Laboratory. Retrieved from http://csrc.nist.gov/publications/drafts/800145/Draft-SP 800145_ cloud definition. Pdf (2011).
[2] Saurabh Kumar Garg, Christian Vecchiola, Rajkumar Buyya “Mandi: a market exchange for trading utility and cloud computing services†The Journal of Supercomputing June 2013, Volume 64, Issue 3, pp 1153-1174.
[3] M. N. O. Sadiku, S. M. Musa and O. D. Momoh, "Cloud Computing: Opportunities and Challenges," in IEEE Potentials, vol. 33, no. 1, pp. 34-36, Jan.-Feb. 2014.
doi: 10.1109/MPOT.2013.2279684
[4] Sunilkumar S. Manvi,, Gopal Krishna Shyam, "Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey Review Article "Journal of Network and Computer Applications, Volume 41, May 2014, Pages 424-440.
[5] Giuseppe Di Modica, Orazio Tomarchio, "Matchmaking semantic security policies in heterogeneous clouds", Future Generation Computer Systems, Volume 55, February 2016, Pages 176-185.
[6] Adam Chlipala. 2015. An optimizing compiler for a purely functional web-application language. In Proceedings of the 20th ACM SIGPLAN International Conference on Functional Programming (ICFP 2015). ACM, New York, NY, USA, 10-21.
[7] N. A. B. S. Chebolu and R. Wankar, "A novel scheme for Compiler Optimization Framework," 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, 2015, pp. 2374-2380.
[8] E. Hendriks. “BProc: The Beowulf distributed process space.†ACM Proceedings of ICS, 2002.
[9] Ke Wang, Xiaobing Zhou, Kan Qiao, Michael Lang, Benjamin McClelland, and Ioan Raicu. 2015. towards Scalable Distributed Workload Manager with Monitoring-Based Weakly Consistent Resource Stealing. In Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing (HPDC '15). ACM, New York, NY, USA, 219-222
[10] http://www.cloudbus.org/papers/SSI-CCWhitePaper.pdf
[11] http://www.openmosix.org/
[12] de Robles, Marie Yvette B.; Arnejo, Zenith O.; Pabico, Jaderick P, "On Web-grid Implementation Using Single System Image",Computer Science - Distributed, Parallel, and Cluster Computing, arXiv:1507.01067
[13] G. Vallee, S. L. Scott, C. Morin, J. Y. Berthou and H. Prisker, "SSI-OSCAR: a cluster distribution for high performance computing using a single system image," 19th International Symposium on High Performance Computing Systems and Applications (HPCS'05), 2005, pp. 319-325.
[14] Philip Healy, Theo Lynn, Enda Barrett, and John P. Morrison. 2016. Single system image. J. Parallel Distrib. Comput. 90, C (April 2016), 35-51. DOI=http://dx.doi.org/10.1016/j.jpdc.2016.01.004
[15] Frederic Magoules, ‎Jie Pan, ‎Fei Teng, "Cloud Computing: Data-Intensive Computing and Scheduling", Chapman & Hall/CRC Numerical Analysis and Scientific Computing,2013
[16] Chaisiri, S.; Bu-Sung Lee; Niyato, D., "Optimization of Resource Provisioning Cost in Cloud Computing," Services Computing, IEEE Transactions on, vol.5, no.2, pp.164,177, April-June 2012doi: 10.1109/TSC.2011.7
[17] Amazon EC2 Reserved Instances, [ ONLINE]
[19] http://www.cisco.com/c/en/us/solutions/service-provider/visual-networking-index-vni/index.html#cloud-forecast http://aws.amazon.com/ec2/reserved-instances, 2013.
[21] Transitioning to the Private Cloud with Confidence Cisco Web Learning White papers.
[22] V. Vivek; Srinivasan R; Elijah Blessing Rajsingh Resource Provisioning Methodologies: An Approach of Producer and Consumer Favorable in Cloud Environment’, International Journal of Emerging Technology and Advanced Engineering, Volume 3, pp.8-13, Special Issue 4, October 2013 (ISSN 2250 – 2459)
[23] Philip Healy, Theo Lynn, Enda Barrett, and John P. Morrison. 2016. Single system image. J. Parallel Distrib. Comput. 90, C (April 2016), 35-51. DOI=http://dx.doi.org/10.1016/j.jpdc.2016.01.004
[24] Jiayin Li; Meikang Qiu; Jian-Wei Niu; Yu Chen; Zhong Ming, "Adaptive resource allocation for preemptable jobs in cloud systems," Intelligent Systems Design and Applications (ISDA), IEEE 2010 10th International Conference on, vol., no., pp.31,36, Nov. 29 2010-Dec. 1 2010.
[25] Kyong Hoon Kim; Buyya, R., "Policy-based Resource Allocation in Hierarchical Virtual Organizations for Global Grids," Computer Architecture and High Performance Computing, 2006. SBAC-PAD '06. IEEE 18TH International Symposium on, vol., no., pp.36, 46, Oct. 2006.
[26] Amit Nathani, Sanjay Chaudhary, Gaurav Somani, Policy based resource allocation in IaaS cloud, Future Generation Computer Systems, Volume 28, Issue 1, January 2012, Pages 94-103, ISSN 0167-739X.
[27] Baker, T.P., "A stack-based resource allocation policy for realtime processes," Real-Time Systems Symposium, 1990. IEEE Proceedings., 11th, vol., no., pp.191,200, 5-7 Dec 1990
[28] Apostol, E.; Leordeanu, C.; Cristea, V., "Policy Based Resource Allocation in Cloud Systems," P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2011 IEEE International Conference on, vol., no., pp.285,288, 26-28 Oct. 2011.
[29] Mochizuki, K.; Kuribayashi, S.-i., "Evaluation of Optimal Resource Allocation Method for Cloud Computing Environments with Limited Electric Power Capacity," Network-Based Information Systems (NBiS), 2011 IEEE 14th International Conference on, vol., no., pp.1,5, 7-9 Sept. 2011.
[30] Anton Beloglazov, Jemal Abawajy, and Rajkumar Buyya. 2012. Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Future Gener. Comput. Syst. 28, 5 (May 2012), 755-768. DOI=10.1016/j.future.2011.04.017.
[31] Warneke, D.; Odej Kao, "Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud," Parallel and Distributed Systems, IEEE Transactions on, vol.22, no.6, pp.985, 997, June 2011. doi: 10.1109/TPDS.2011.65
[32] Kim, Tai-hoon and Adeli, Hojjat and Cho, Hyun-seob and Gervasi, Osvaldo and Yau, StephenS. In addition, Kang, Byeong-Ho and Villalba, JavierGarcÃa, a Dynamic Resource Allocation Model for Virtual Machine Management on Cloud, Springer Berlin Heidelberg.
[33] K. Ye, X. Jiang, D. Huang, J. Chen, and B. Wang, “Live Migration of Multiple Virtual Machines with Resource Reservation in Cloud Computing Environmentsâ€, in Proc. IEEE CLOUD, 2011, pp.267-274.
[34] Christopher Clark, Keir Fraser, Steven Hand, Jacob Gorm Hansen, Eric Jul, Christian Limpach, Ian Pratt, and Andrew Warfield. 2005. Live migration of virtual machines. In Proceedings of the second conference on Symposium on Networked Systems Design & Implementation - Volume 2 (NSDI'05), Vol. 2. USENIX Association, Berkeley, CA, USA, 273-286.
[35] William Voorsluys, James Broberg, Srikumar Venugopal, and Rajkumar Buyya. 2009. Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation. In Proceedings of the 1st International Conference on Cloud Computing (CloudCom '09), Martin Gilje Jaatun, Gansen Zhao, and Chunming Rong (Eds.). Springer-Verlag, Berlin, Heidelberg, 254-265.
[36] Xiaoqiao Meng, Canturk Isci, Jeffrey Kephart, Li Zhang, Eric Bouillet, and Dimitrios Pendarakis. 2010. Efficient resource provisioning in compute clouds via VM multiplexing. In Proceedings of the seventh international conference on Autonomic computing (ICAC '10). ACM, New York, NY, USA, 11-20. DOI=10.1145/1809049.1809052.
[37] Zhen Xiao; Weijia Song; Qi Chen, "Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment," Parallel and Distributed Systems, IEEE Transactions on , vol.24, no.6, pp.1107,1117, June 2013 doi: 10.1109/TPDS.2012.283
[38] Xingwei Wang; Jiajia Sun; Min Huang; Chuan Wu; Xueyi Wang, "A Resource Auction Based Allocation Mechanism in the Cloud Computing Environment," Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International, vol., no., pp.2111,2115, 21-25 May 2012 doi: 10.1109/IPDPSW.2012.260
[39] Advances in Computing, Communication, and Control Communications in Computer and Information Science Unnikrishnan, Srija Surve, Sunil Bhoir, Deepak R 10.1007/978-3-642-36321-4_2 T Market-Driven Continuous Double Auction Method for Service Allocation in Cloud Computing Springer Berlin Heidelberg 2013-01-01 cloud computing continuous double auction intelligent agent market-driven agents resource allocation Farajian, Nima Zamanifar, Kamran 14-24.
[40] Kuo-Chan Huang; Bo-Jyun Shen; Tsung-Ju Lee; Hsi-Ya Chang; Yuan-Hsin Tung; Pin-Zei Shih, "Resource allocation and dynamic provisioning for Service-Oriented applications in cloud environment," Cloud Computing Technology and Science (CloudCom), 2012 IEEE 4th International Conference on, vol., no., pp.839,844, 3-6 Dec. 2012. doi: 10.1109/CloudCom.2012.6427592
[41] Kui Ren; Cong Wang; Qian Wang, "Security Challenges for the Public Cloud," Internet Computing, IEEE, vol.16, no.1, pp.69, 73, Jan.-Feb. 2012. doi: 10.1109/MIC.2012.14
[42] Dimitrios Zissis, Dimitrios Lekkas, Addressing cloud computing security issues, Future Generation Computer Systems, Volume 28, Issue 3, March 2012, Pages 583-592, ISSN 0167-739X, http://dx.doi.org/10.1016/j.future .2010.12.006.
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How to Cite
Vivek, V., Srinivasan, R., & Elijah Blessing, R. (2018). Resource provisioning methodology for cloud environment with producer and consumer favorable: an approach of virtual cloud compiler. International Journal of Engineering & Technology, 7(2.4), 123-130. https://doi.org/10.14419/ijet.v7i2.4.13022Received date: 2018-05-18
Accepted date: 2018-05-18
Published date: 2018-03-10