Volume-7 , Issue-4 , Aug 2019, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Survey on Minimizing Payment Cost of Multiple Cloud Service Provider

  Daimi Aafiya

  Survey Paper | Isroset-Journal (IJSRCSE)

  Vol.7 , Issue.4 , pp.1-5, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v7i4.15

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  Abstract
  Many industries and research center using a cloud service provider (CSP) provider for storing data on that and CSP used many web applications such as web portal, online social network providing services to the clients all over the world. These types of datacenters provide the different unit prices and get/put latencies for resources reservation and allocations. Selection of different CSPs datacenters and cloud customers facing two challenges (I.) How to allocating data to the datacenters in worldwide to satisfy application Service Level Objectives (SLO) requirement which includes both data availability and retrieval latency. (II.) How to allocate reserve resources and data in the datacenters, which belongs to different CSP to minimizing payment cost.Find out the solution of these challenges firstly we used integer programming techniques for handles cost minimization problems. We propose three techniques for reducing the service latency and payment cost 1. Multicast Based Data Transferring, 2. Coefficient Based Data Reallocation and 3. Request Redirection Based Congestion.

  Key-Words / Index Term
  Cloud Service Provider, Service Level Objectives, Payment Cost Minimization, and Data Availability

  References
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  Citation
  Daimi Aafiya, "Survey on Minimizing Payment Cost of Multiple Cloud Service Provider," International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.4, pp.1-5, 2019

• Open Access   Article

  Research on Minimizing Payment Cost of Multiple Cloud Service Provider

  Daimi Aafiya

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.7 , Issue.4 , pp.6-13, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v7i4.613

  Abstract

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  Abstract
  Todays many businesses are shifting their workloads to cloud storage to save capital costs for building and maintaining hardware infrastructures and avoid the complexity of managing data centers. Cloud computing has become a popular commercial service. The CSP (Cloud Service Provider) provides data storage services in which includes Get and Put function, using this geographically distributed data centers around the world. Selection of different CSPs datacenters and cloud customers facing two challenges, first one is how to allocating data to the datacenters in worldwide to satisfy application Service level objectives (SLO) requirement which includes both data availability and retrieval latency and second is how to allocate reserve resources & data in the datacenters which belongs to different CSP to minimizing payment cost. Find out the solution of these challenges firstly used integer-programming techniques for handles cost minimization problems. Here used three techniques for reducing service latency and payment cost first multicast-based data transferring second coefficient based data reallocation and third is request redirection based congestion and using PPM-C (Prediction by Partial Matching-Cloud) data compression technique, this helps to reduce storage cost and data transfer computing time

  Key-Words / Index Term
  Service Level Objectives, Cloud Computing, Resources Reservation, CSP, Payment Cost Minimization, PPM-C compression, and Data Availability

  References
  [1] Z. Wu, M. Butkiewicz, D. Perkins, E. Katz-Bassett, and H. V. Madhyastha : SPANSTORE: COST-EFFECTIVE GEO-REPLICATED STORAGE SPANNING MULTIPLE CLOUD SERVICES. in Proc. SOSP, pp. 292-308, [2013].
  [2] G. A. Alvarez et al. : MINERVA: AN AUTOMATED RESOURCE PROVISIONING TOOL FOR LARGE SCALE STORAGE SYSTEMS, ACM Transaction on Computer System, vol. 19, no. 4, pp. 483-518, [2001].
  [3] S. Agarwal et al.,: VOLLEY: AUTOMATED DATA PLACEMENT FOR GEO-DİSTRİBUTE CLOUD SERVİCES, in Proc. NSDI, p. 28, [2010].
  [4] Y. Yao, Haozhou Wang, and K. Liu : ON STORING AND RETRIVEING GEOSPATIOAL BIG-DATA IN CLOUD, SIGSPTIAL International workshop of use of GIS in the emergency management, ACM, [2016].
  [5] Christoph Hoch Reiner, Stefan Schulte and Michael Borkowski, : PREDICTING CLOUD RESOURCE UTILIZATION, 9th ICUCC-International Conference on the Utility and Cloud Computing; [2016].
  [6] S. H. Gary Chan and Zhangyu Chang : VIDEO MANAGEMENT AN RESOURCES ALLOCATION FOR A LARGE SCALE VoD CLOUD, Nature Communication, 24 January 2016 ACM Transaction on Multimedia Computing Communication Application, Vol. 12, Article 72, [2016].
  [7] Ibrar Yaqoob, Samee Ullah Khan, S. Mokhtar and A. Gani : THE RISE OF BIG DATA ON CLOUD COMPUTING REVIEW AND OPEN RESEARCH ISSUES, Elsevier-[2015].
  [8] Zhiming Shen, Qin Jia, Weijia Song : SUPERCLOUD OPPORTUNITIES AND CHALLENGES, SIGOPS Oper. Syst. Rev, [2015].
  [9] Boyang Wang, Jiqiang Liu, Ming Li, and Shuo Qiu : TOWRODS PRACTICAL PRIVACY PRESERVING FREQUENT ITEMSET MINING ON ENCRYPTED CLOUD DATA, IEEE Transactions on Cloud Computing, [2017].
  [10] Miguel Correia, Alysson Bessani, and Bruno Quaresma: DepSky DEPENDABLE AND SECURE STORAGE IN A CLOUD OF CLOUDS, ACM Transaction Storage, [2017].
  [11] Den Bossche, Jan Broeckhove, and Kurt Vanmechelen: OPTIMIZING IAAS RESERVED CONTRACT PROCURRENT USING LOAD PREDICTION, 7th International Conference on Cloud Computing (ICCC), IEEE, [2014].
  [12] Michael Borkowski, Christoph Hochreiner and Stefan Schulte: PREDICTING CLOUD RESOURCES UTILIZATION, 9th International Conference on Utility and Cloud, IEEE/ACM, [2016].
  [13] S.H. Gary Chan, Zhangyu Chang, : VIDEO MANGEMENT AND RESOURCES ALLOCATION FOR A LARGE SCALE VOD CLOUD;. Transactions on Multimedia Computing, Communications, and Applicant, [2013].
  [14] Harsha V. Madhyastha, and John C. McCullough: SCC: CLUSTER STORAGE PROVİSİONİNG INFORMED BY APPLİCATİON CHARACTERİSTİCS AND SLAS; FAST, p. 23, [2012].
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  [17] Ramakrishna Kotla, and Mike Dahlin: SAFESTORE: A DURABLE AND PRACTİCAL STORAGE SYSTEM, ATC, pp. 129–142, [2007].
  [18] A. Wieder, P. Bhatotia, A. Post, and R. Rodrigues, “ORCHESTRATİNG THE DEPLOYMENT OF COMPUTATİONS İN THE CLOUD WİTH CONDUCTOR,” in Proc. NSDI, pp. 367–381, [2012].
  [19] F. Wang, and M. Chen, “CALMS: CLOUD-ASSİSTED LİVE MEDİA STREAMİNG FOR GLOBALİZED DEMANDS WİTH TİME/REGİON DİVERSİTİES,” in Proc. INFOCOM, pp. 199–207, [2012].
  

  Citation
  Daimi Aafiya, "Research on Minimizing Payment Cost of Multiple Cloud Service Provider," International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.4, pp.6-13, 2019

• Open Access   Article

  A Multiples Method for Number System Conversions

  Jitin Kumar

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.7 , Issue.4 , pp.14-17, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v7i4.1417

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  Abstract
  Number systems are the technique, to represent numbers in the computer system architecture, every value that you save into computer memory has a defined number system. To understand computers, knowledge of number systems and knowledge of their inter conversion is essential. Computer architecture supports Binary number system, octal number system, Decimal number system and Hexadecimal number system. In this paper, the conversion of number systems is given as multiples. By using multiples method, we can easily convert one number system into another number system without taking too much time and in the multiples method you have not to consider integer and fractional as two parts, all will be converted in a single method. It is easy to understand as well as memorize.

  Key-Words / Index Term
  Number System, binary, octal, decimal, hexadecimal, inter conversions, base, radix

  References
  [1]. A. Anand Kumar, “Switching Theory and Logic Design”, Publisher: PHI learning private Limited; 3rd edition (2015).
  [2]. Shahid Latif, Junaid Qayyum, Muhammad Lal, Faheem Khan, “Complete Description of Well-Known Number Systems using Single Table” International Journal of Electrical & Computer Sciences (IJECS-IJENS), Vol: 11, No:03; (June, 2011).
  [3]. Ramesh S. Gaonkar, “Microprocessor Architecture, Programming, and Applications with the 8085”, Publisher: Prentice Hall; 5th edition (February, 2002).
  [4]. Behrouz A. Forouzan, “Data Communication and Networking”, Publisher: McGraw-Hill Education; 5th edition (February 2012).
  [5]. Yaohan Chu, “Digital Computer Design Fundamentals”, Publisher: McGraw Hill Text; 1st edition (June 1962).
  

  Citation
  Jitin Kumar, "A Multiples Method for Number System Conversions," International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.4, pp.14-17, 2019

• Open Access   Article

  DDDA: Development of a Distributed De-Duplication Approach using Big Data Analysis in Hybrid Cloud Environment

  Aarfa Khan, Snehlata Kothari

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.7 , Issue.4 , pp.18-21, Aug-2019

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v7i4.1821

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  Abstract
  A renewed interest in cloud computing adoption has occurred in academic and industry settings because emerging technologies have strong links to cloud computing and Big Data technology. Big Data technology is driving cloud-computing adoption in large business organizations. For cloud computing adoption to increase, cloud computing must transition from low-level technology to high-level business solutions. Security, privacy and elimination of repetitive copies of data is of primary concern for many applications of Big Data (BD). Data of the consumers must be protected else private information can be leaked. Cloud should let the owners or a trusted third party to check for the integrity of their data storage without demanding a local copy of the data. For this reason, this paper covered: Issues in big data management (BDM), secure data processing (DP) and access control (AC’s) of data in cloud by data owner, data integrity verification in cloud. On performance basis, proposed approach is tested and simulated on different raw data and their processing compared with few of existing algorithm based on security, accessibility and integrity parameters. Results obtained are satisfactory to achieve all in single approach.

  Key-Words / Index Term
  DDDA, Big Data, Security, Cloud Computing, BDM etc

  References
  [1] Towards the Design of a System and a Workflow Model for Medical Big Data Processing in the Hybrid Cloud :2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)
  [2] Geospatial Big Data Processing in Hybrid Cloud Environments: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
  [3] Data security in cloud: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS)
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  [6] Rao, R. V., & Selvamani, K. (2015). Data security challenges and its solutions in cloud computing. Procedia Computer Science, 48, 204–209.
  [7] Saad, S. A., Adam, A., & Abdelateef, A. H. (2016). Binary logistic regression to estimate household income efficiency (South Darfur rural areas-Sudan). International Journal of Advanced Statistics and Probability, 4(1), 31–35. doi: 10.14419/ijasp.v4i1.5657
  [8] Schroedl, J. (2016). Hyperconverged infrastructure meets Big-Data-as-a-service.Bluedata. Retrieved from https://www.bluedata.com/blog/2016/05/hyperconverged-infrastructure-meets-big-data/
  [9] Shahrivari, S. (2014). Beyond batch processing: towards real-time and streaming Big Data. Computers, 3(4), 117-129.Siegel, E. (2013a). Predictive analytics. Hoboken, NJ: John Wiley & Sons.
  [10] Siegel, E. (2013b, July/August). Predictive analytics: Harnessing the power of big data. Retrieved from http://analytics-magazine.org/predictive-analytics-2/Singh, J. (2017). Study on challenges, opportunities and predictions in cloud computing. International Journal of Modern Education and Computer Science, 9(3), 17.
  

  Citation
  Aarfa Khan, Snehlata Kothari, "DDDA: Development of a Distributed De-Duplication Approach using Big Data Analysis in Hybrid Cloud Environment," International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.4, pp.18-21, 2019