WORK LOAD SHARING USING MOBILE EDGE COMPUTING
DOI:
https://doi.org/10.61841/z0aqmy35Keywords:
Mobile edge computing, Work load sharing cloud environment,, Backscattering Algorithm,, ,Prirority based task Scheduling AlgorithmAbstract
Recently, intense use of mobile devices is increased and day by day new technology in communication is increasing so we have to accommodate more and more devices on the cloud server also we have to provide advanced algorithm for efficient exchange of resources between cloud and client server. Mobile devices users are saturated with already proposed traditional methods such as grid-computing. Our aim in this project is to reduce the load on mobile devices and reduces the work sharing in cloud environment. This paper supports the use of more optimum algorithm for mobile Edge-Clouds. In using mobile edge computing technology, we have a cellular operator that allows efficient deployment services for specific customers or classes of customers. This technology also reduces the signal load of the core network, and can host applications and provide services in a cheaper way. Data Sharing will increase traffic on mobile edge cloud to reduce it is our major aim of the project. Our idea is to secure and reliable data sharing in mobile edge cloud environment.To achieve this we are going to implement an Algorithm of BackScatter and Priority based task Sharing Algorithm.
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1. Wang, Q., Guo, S., Wang, Y., & Yang, Y. (2019). Incentive Mechanism for Edge Cloud Profit Maximization in Mobile Edge Computing. ICC 2019 - 2019 IEEE International Conference on Communications (ICC).
2. R.K. Panta, R. Jana, F. Cheng, Y.R. Chen, and V.A.Vaishampayan. Phoenix: Storage using an autonomous mobile infrastructure. Parallel and Distributed Systems, IEEE Transactions on, 24(9):1863– 1873, 2013.
3. J. Ren, Y. Zhang, K. Zhang, and X. Shen. Exploiting mobile crowdsourcing for pervasive cloud challenges and solutions. Communications Magazine, IEEE,53(3):98–105, 2015.
4. K. Habak, M. Ammar, K. Harras, and E. Zegura. Femtoclouds: Leveraging mobile devices to provide cloud service at the edge. In Proceedings of the 8th IEEE International Conference on Cloud Computing, 2015.
5. Dbouk, T., Mourad, A., Otrok, H., Tout, H., & Talhi, C. (2019). A Novel Ad-Hoc Mobile Edge Cloud Offering Security Services through Intelligent Resource-Aware Offloading. IEEE Transactions on Network and Service Management
6. Fernando, N., Loke, S. W., & Rahayu, W. (2019).Computing with Nearby Mobile Devices: a WorkSharing Algorithm for Mobile Edge-Clouds. IEEE Transactions on Cloud Computing.Wang, Q., Guo, S., Wang, Y., & Yang, Y. (2019).
7. Incentive Mechanism for Edge Cloud Profit Maximization in Mobile Edge Computing. ICC 2019 - 2019 IEEE International Conference on Communications (ICC).
8. THE LOCATIONS OF IP SPOOFERS FROM PATHWAY BACKSCATTER IN PASSIVE IP TRACEBACK 1S. Mahesh Babu,K. Sreenivas 21(CSE, GIST/ JNTU ANANTHAPURAMU, INDIA)
9. Passive IP Traceback: Disclosing the Locations of Man in the Middle from Path Backscatter Aman Shekhar [1], Krishan Yadav [2], Krishna Yele[3] Utpal Chirag [4], Ms. Santhi K. Guru [5] Research Scholar [1], [2], [3] & [4], Assistant Professor [5] Department of Computer EngineeringD Y Patil College of Engineering, Akurdi PuneIndia
10. Review on Priority Based Task Scheduling In Cloud Computing Dr. Sudhir D. Sawarkar, [2] Pratiksha D. Warule [1] Principal Datta Meghe college of Engg, Airoli [2] ME Computer Engg, Student,Datta Meghe collegeof Engg, Airoli -March 2018
11. A. Fahim, A. Mtibaa, and K. A. Harras. Making the case for of the 19th Int’l Conference on Mobile Computing & Networking, pages 203– 205, NY, USA, 2013..
12. D. G. Murray, E. Yoneki, J. Crowcroft, and S. Hand. The case for crowd computing. In Proc. of the 2nd SIGCOMM workshop on Networking, systems, and applications on mobile handhelds, pages 39– 44, 2010.
13. J. Oomen and L. Aroyo. Crowdsourcing in the cultural heritage domain: Opportunities and challenges. In Proceedings of the 5th International Conference on Communities and Technologies, C&T ’11, pages 138–149, NY, USA, 2011. ACM
14. S. Pandey, W. Voorsluys, S. Niu, A. Khandoker, and R. Buyya. An autonomic cloud environment for hosting ecg28(1):147 – 154, 2012.
15. R.K. Panta, R. Jana, F. Cheng, Y.R. Chen, and V.A. Vaishampayan. Phoenix: Storage using an autonomous mobile infrastructure. Parallel and Distributed Systems, IEEE Transactions on, 24(9):1863–1873, 2013.
16. K. Parshotam. Crowd computing: a literature review and definition. In Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference, pages 121– 130
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