Home| Contact Us| New Journals| Browse Journals| Journal Prices| For Authors|

Print ISSN:
Online ISSN:


  About JDP
  DLINE Portal Home
Home
Aims & Scope
Editorial Board
Current Issue
Next Issue
Previous Issue
Sample Issue
Upcoming Conferences
Self-archiving policy
Alert Services
Be a Reviewer
Publisher
Paper Submission
Subscription
Contact us
 
  How To Order
  Order Online
Price Information
Request for Complimentary
Print Copy
 
  For Authors
  Guidelines for Contributors
Online Submission
Call for Papers
Author Rights
 
 
RELATED JOURNALS
Journal of Digital Information Management (JDIM)
Journal of Multimedia Processing and Technologies (JMPT)
International Journal of Web Application (IJWA)

 

 
Journal of Data Processing
 

Eagle Techniques in Cloud Computational Formulation
Ashish Tiwari
National Institute of Technology, Kurukshetra & India
Abstract: The Eagle expresses of Cloud computing plays a pivotal role in the development of Technology. The Technology momentum gaining Cloud System environment across the emerging Computational Intelligence as artificial intelligence in IOTs. Cloud Computing playing a pivotal role in providing the Optimized algorithms for the issues in cloud computing which are the global challenges. The problem aims to solve in such a way that it will provide an optimized solution. Among all these research areas the works are developing at a rapid speed. The rapid vision, models and challenges for efficient optimization of resource in cloud computing world. The key role of allocating these efficient resources and making the algorithms for its time and cost optimization keeping in consideration of its quality of services and characteristics. These both are effecting the performance of these techniques is a major drawback due to low accuracy and large computational complexity of the algorithms. Therefore the concept of fuzzy system is playing a pivotal role in designing such strategies in which it makes use of the concept Fuzzy sets, Fuzzy logic and reasoning, Fuzzy controllers, Rough sets, etc. As per the scenario the approach of the research is based on technology acceptance model (TAM) and the Rough Set Theory (RST). RST a great method for making a large difference in qualitative analysis situations. It’s a technique to found the knowledge discovery and handle the problems such as inductive reasoning, automatic classification, pattern recognition, learning algorithms, and data reduction. The rough set theory is the new method in cloud service selection so that the best services to provide for cloud users and efficient service improvement for cloud providers.
Keywords: High Performance Computing, Cloud Parameters, Cloud Brokers, Mathematical Model, Cloud Simulator Eagle Techniques in Cloud Computational Formulation
DOI:https://doi.org/10.6025/jdp/2020/10/1/11-29
Full_Text   PDF 3.27 MB   Download:   221  times
References:

[1] Abawajy, J. (2009, December). Determining service trustworthiness in Intercloud computing environments. In: Pervasive Systems, Algorithms, and Networks (ISPAN), 2009 10th International Symposium on (p. 784-788). IEEE.
[2] Abawajy, J. H., Dandamudi, S. P. (2003, December). Parallel job scheduling on multicluster computing systems. In: null (p. 11). IEEE.
[3] Almuttairi, R. M., Wankar, R., Negi, A., Rao, C. R., Agarwal, A., Buyya, R. (2013). A two phased service oriented Broker for replica selection in data grids. Future Generation Computer Systems, 29 (4) 953-972.
[4] Ashish, M. K. S. V. K. (2014). Security and Concurrency Control in Distributed Database System. International Journal of Scientific Research and Management, 2 (12).
[5] Awad, A. I., El-Hefnawy, N. A., Abdel_kader, H. M. (2015). Enhanced particle swarm optimization for task scheduling in cloud computing environments. Procedia Computer Science, 65, 920-929.
[6] Beloglazov, A., Abawajy, J., Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28 (5) 755-768.
[7] Bera, S., Misra, S., Rodrigues, J. J. (2015). Cloud computing applications for smart grid: A survey. IEEE Transactions on Parallel & Distributed Systems, (5) 1477-1494.
[8] Bonabeau, E., Marco, D. D. R. D. F., Dorigo, M., Théraulaz, G., Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems (No. 1). Oxford University Press.
[9] Buyya, R. (2010, October). Cloud computing: The next revolution in information technology. In: Parallel Distributed and Grid Computing (PDGC), 2010 1st International Conference on (p. 2-3). IEEE.
[10] Buyya, R., Beloglazov, A., Abawajy, J. (2010). Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. arXiv preprint arXiv:1006.0308.
[11] Buyya, R., Broberg, J., Goscinski, A. M. (Eds.). (2010). Cloud computing: Principles and paradigms (Vol. 87). John Wiley & Sons.
[12] Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25 (6) 599-616.
[13] Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., Buyya, R. (2011). CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience, 41 (1) 23-50.
[14] Chandrasekar, R., Misra, S. (2006, December). Introducing an ACO based paradigm for detecting wildfires using wireless sensor networks. In Ad Hoc and Ubiquitous Computing, 2006. ISAUHC’06. International Symposium on (p. 112-117). IEEE.
[15] Chen, F., Guo, K., Lin, J., La Porta, T. (2012, March). Intra-cloud lightning: Building CDNs in the cloud. In: INFOCOM, 2012 Proceedings IEEE (p. 433-441). IEEE.
[16] Deepa, O., Senthilkumar, A. (2016). Swarm intelligence from natural to artificial systems: Ant colony optimization. Networks (GRAPH-HOC), 8 (1).
[17] Mann, F. (2009). Cloud Computing: The Next Revolution in IT, Photogrammetric Week ’09.
[18] Foster, I., Kesselman, C. (Eds.). (2003). The Grid 2: Blueprint for a new computing infrastructure. Elsevier.
[19] Geeta, C. M., Raghavendra, S., Buyya, R., Venugopal, K. R., Iyengar, S. S., Patnaik, L. M. (2018). Data Auditing and Security in Cloud Computing: Issues, Challenges and Future Directions. International Journal of Computer (IJC), 28(1) 8-57.
[20] Herawan, T., Deris, M. M., Abawajy, J. H. (2010). A rough set approach for selecting clustering attribute. Knowledge-Based Systems, 23(3) 220-231.
[21] Juarez, F., Ejarque, J., Badia, R. M., Rocha, S. N. G., Esquivel-Flores, O. A. (2018). Energy-Aware Scheduler for HPC Parallel Task Base Applications in Cloud Computing. International Journal of Combinatorial Optimization Problems and Informatics, 9(1) 54-61.
[22] Khosravi, A., Andrew, L. L., Buyya, R. (2017). Dynamic vm placement method for minimizing energy and carbon cost in geographically distributed cloud data centers. IEEE Transactions on Sustainable Computing, 2(2) 183-196.
[23] Klems, M., Nimis, J., Tai, S. (2008, December). Do clouds compute? a framework for estimating the value of cloud computing.In: Workshop on E-Business (p. 110-123). Springer, Berlin, Heidelberg.
[24] Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A. (1999). Rough sets: A tutorial. Rough fuzzy hybridization: A new trend in decision-making, 3-98.
[25] Konar, A. (2005). An introduction to computational intelligence. Computational Intelligence: Principles, Techniques and Applications, 1-35.
[26] Krishna, P. V., Misra, S., Joshi, D., Obaidat, M. S. (2013, May). Learning automata based sentiment analysis for recommender system on cloud. In Computer, Information and Telecommunication Systems (CITS), 2013 International Conference on (p. 1-5). IEEE.
[27] Krishna, P. V., Misra, S., Saritha, V., Raju, D. N., Obaidat, M. S. (2017, May). An efficient learning automata based task offloading in mobile cloud computing environments. In: Communications (ICC), 2017 IEEE International Conference on (p. 1-6). IEEE.
[28] Liu, Y., Esseghir, M., Boulahia, L. M. (2014, December). Cloud service selection based on rough set theory. In: Network of the Future (NOF), 2014 International Conference and Workshop on the (p. 1-6). IEEE.
[29] Liu, Y., Esseghir, M., Boulahia, L. M. (2016). Evaluation of parameters importance in cloud service selection using rough sets. Applied Mathematics, 7 (06) 527.
[30] Mahrishi, M., Shrotriya, A., Sharma, D. K. (2012). Globally Recorded binary encoded Domain Compression algorithm in Column Oriented Databases. Global Journal of Computer Science and Technology.
[31] Mansouri, Y., Toosi, A. N., Buyya, R. (2017). Cost optimization for dynamic replication and migration of data in cloud data centers. IEEE Transactions on Cloud Computing.
[32] Mell, P., Grance, T. (2011). The NIST definition of cloud computing.
[33] Mosleh, M. A., Radhamani, G., Hazber, M. A., Hasan, S. H. (2016). Adaptive Cost-Based Task Scheduling in Cloud Environment. Scientific Programming.
[34] Nan, X., He, Y., Guan, L. (2011, October). Optimal resource allocation for multimedia cloud based on queuing model. In: Multimedia Signal Processing (MMSP), 2011 IEEE 13th international workshop on (p. 1-6). IEEE.
[35] Nan, X., He, Y., Guan, L. (2012, May). Optimal resource allocation for multimedia cloud in priority service scheme. In: Circuits and systems (ISCAS), 2012 IEEE international symposium on (p. 1111-1114). IEEE.
[36] Naga, K. P. P., Kodialam, M., Varvello, M. (2014). U.S. Patent Application No. 13/597,614.
[37] Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11(5) 341-356.
[38] Pawlak, Z. (1998). Rough set theory and its applications to data analysis. Cybernetics & Systems, 29(7) 661-688.
[39] Pawlak, Z. (2002). Rough set theory and its applications. Journal of Telecommunications and Information Technology, 7- 10.
[40] Peng, X., Ren, J., She, L., Zhang, D., Li, J., Zhang, Y. (2018). BOAT: A Block-Streaming App Execution Scheme for Lightweight IoT Devices. IEEE Internet of Things Journal, 5(3) 1816-1829.
[41] Phyo, Z. L., Thida, A. (2011, March). Best resource node selection using rough sets theory. In: Computer Research and Development (ICCRD), 2011 3rd International Conference on (Vol. 2, p. 461-464). IEEE.
[42] Puttaswamy, K. P., Nandagopal, T., Kodialam, M. (2012, April). Frugal storage for cloud file systems. In: Proceedings of the 7th ACM european conference on Computer Systems (p. 71-84). ACM.
[43] Rimal, B. P., Choi, E., Lumb, I. (2009, August). A taxonomy and survey of cloud computing systems. In: INC, IMS and IDC, 2009. NCM’09. Fifth International Joint Conference on (p. 44-51). IEEE.
[44] Rissino, S., Lambert-Torres, G. (2009). Rough set theory—fundamental concepts, principals, data extraction, and applications. In: Data mining and knowledge discovery in real life applications. InTech.
[45] Riza, L. S., Janusz, A., Bergmeir, C., Cornelis, C., Herrera, F., Sle, D., Benítez, J. M. (2014). Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “roughsets”. Information Sciences, 287, 68-89.
[46] Samanta, A., Misra, S. (2018). Energy-efficient and distributed network management cost minimization in opportunistic wireless body area networks. IEEE Transactions on Mobile Computing, 17(2) 376-389.
[47] Sarkar, S., Chatterjee, S., Misra, S. (2015). Assessment of the Suitability of Fog Computing in the Context of Internet of Things. IEEE Transactions on Cloud Computing.
[48] Sarkar, S., Chatterjee, S., Misra, S., Kudupudi, R. (2017). Privacy-Aware Blind Cloud Framework for Advanced Healthcare. IEEE Communications Letters, 21(11) 2492-2495.
[49] Sharma, N., Rana, S., Sharma, R. M. (2010, April). Provisioning of Quality of Service in MANETs performance analysis & comparison (AODV and DSR). In Computer Engineering and Technology (ICCET), 2010 2nd International Conference on (Vol. 7, p. V7-243). IEEE.
[50] Sharma, R. M. (2010). Performance Comparison of AODV, DSR and AntHocNet Protocols. Department of Computer Engineering, NIT Kurukshetra.
[51] Shojafar, M., Canali, C., Lancellotti, R., Abawajy, J. (2016). Adaptive computing-plus-communication optimization framework for multimedia processing in cloud systems. IEEE Transactions on Cloud Computing.
[52] Singh, S., Sharma, R. M. (2015). Some aspects of coverage awareness in wireless sensor networks. Procedia Computer Science, 70, 160-165.
[53] Singh, S., Sharma, R. M. (2018). Heuristic Based Coverage Aware Load Balanced Clustering in WSNs and Enablement of IoT. International Journal of Information Technology and Web Engineering (IJITWE), 13(2) 1-10.
[54] Singh, S., Chana, I., Buyya, R. (2017). STAR: SLA-aware autonomic management of cloud resources. IEEE Transactions on Cloud Computing.
[55] Singh, S., Sharma, R. M., Kumar, P. (2016). WSNs and PDNs: Similarities, challenges and application of computational intelligence. International Journal of Control Theory and Applications, 9(41) 489-497.
[56] Sundareswaran, S., Squicciarini, A., Lin, D. (2012, June). A brokerage-based approach for cloud service selection. In: Cloud computing (cloud), 2012 ieee 5th international conference on (p. 558-565). IEEE.
[57] Tiwari, A., Sharma, R. M. (2016, August). Potent Cloud Services Utilization with Efficient Revised Rough Set Optimization Service Parameters. In: Proceedings of the International Conference on Advances in Information Communication Technology & Computing (p. 90). ACM.
[58] Tiwari, A., Mahrishi, M., Fatehpuria, S. A Broking Structure Originated on Service accommodative Using MROSP Algorithm. 
[59] Tiwari, A., Nagaraju, A., Mahrishi, M. (2013, February). An optimized scheduling algorithm for cloud broker using adaptive cost model. In: Advance Computing Conference (IACC), 2013 IEEE 3rd International (p. 28-33). IEEE.
[60] Tiwari, A., Sah, M. K., Gupta, S. (2015). Efficient Service Utilization in Cloud Computing Exploitation Victimization as Revised Rough Set Optimization Service Parameters. Procedia Computer Science, 70, 610-617.
[61] Tiwari, A., Sah, M. K., Malhotra, A. (2015, September). Effective service Utilization in Cloud Computing exploitation victimisation rough pure mathematics as revised ROSP. In Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), 2015 4th International Conference on (pp. 1-6). IEEE.
[62] Tiwari, A., Sharma, V., Mahrishi, M. (2014). Service Adaptive Broking Mechanism Using MROSP Algorithm. In: Advanced Computing, Networking and Informatics-Volume 2 (p. 383-391). Springer, Cham.
[63] Tiwari, A., Tiwari, A. K., Saini, H. C., Sharma, A. K., Yadav, A. K. (2013). A Cloud Computing using Rough set Theory for Cloud Service Parameters through Ontology in Cloud Simulator. In: ACITY-2013 Conference at Chennai, in CS and IT proceedings. 
[64] Tiwari, A., Tiwari, A. K., Saini, H. C., Sharma, A. K., Yadav, A. K. (2013). A Cloud Computing using Rough set Theory for Cloud Service Parameters through Ontology in Cloud Simulator. In: ACITY-2013 Conference at Chennai, in CS and IT proceedings.
[65] Vallverdú, J., Talanov, M., Khasianov, A. (2017). Swarm Intelligence via the Internet of Things and the Phenomenological Turn. Philosophies, 2(3) 19.
[66] Vaquero, L. M., Rodero-Merino, L., Caceres, J., Lindner, M. (2008). A break in the clouds: towards a cloud definition. ACM SIGCOMM Computer Communication Review, 39(1) 50-55.
[67] Vecchiola, C., Pandey, S., Buyya, R. (2009, December). High-performance cloud computing: A view of scientific applications. In Pervasive Systems, Algorithms, and Networks (ISPAN), 2009 10th International Symposium on (pp. 4-16). IEEE.
[68] Voorsluys, W., Broberg, J., Buyya, R. (2011). Introduction to cloud computing. Cloud computing: Principles and paradigms, 1-41.
[69] Wei, Y., Sukumar, K., Vecchiola, C., Karunamoorthy, D., Buyya, R. (2011). Aneka cloud application platform and its integration with windows Azure. arXiv preprint arXiv:1103.2590.
[70] Weiss, A. (2007). Computing in the clouds. Networker, 11(4) 16-25.
[71] Xu, M., Cui, L., Wang, H., Bi, Y. (2009, August). A multiple QoS constrained scheduling strategy of multiple workflows for cloud computing. In: Parallel and Distributed Processing with Applications, 2009 IEEE International Symposium on (p. 629-634). IEEE.
[72] Zhang, L., Wu, C., Li, Z., Guo, C., Chen, M., Lau, F. C. (2013). Moving big data to the cloud: An online cost-minimizing approach. IEEE Journal on Selected Areas in Communications, 31(12) 2710-2721.
[73] Zhou, Z., Abawajy, J., Chowdhury, M., Hu, Z., Li, K., Cheng, H., Li, F. (2017). Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms. Future Generation Computer Systems.


Home | Aim & Scope | Editorial Board | Author Guidelines | Publisher | Subscription | Previous Issue | Contact Us |Upcoming Conferences|Sample Issues|Library Recommendation Form|

 

Copyright © 2011 dline.info