Critical Assessment of Phases of Partitioning and Offloading Tasks in Edge Computing
Kulvir Singh1, Yogesh Kumar Sharma2
1Kulvir Singh, Research Scholar Department of Computer science and Engineering; Shri JJT University Jhunjhunu Rajasthan, India.
2Dr. Yogesh Kumar Sharma, Associate Professor (HOD/Research coordinator), Department of Computer science and Engineering; Shri JJT University Jhunjhunu Rajasthan, India.

Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7599-75604 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5345118419/2019©BEIESP | DOI: 10.35940/ijrte.D5345.118419

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Abstract: Edge computing defeats high correspondence defer weakness of customary distributed computing and furnishes figuring administrations with high unwavering quality and high transfer speed for cell phones. At present, edge processing has turned into the front line and hotspot of versatile edge distributed computing (MEC) look into. In any case, with the expanding prerequisites and administrations of versatile clients, offloading procedure of straightforward edge registering is never again relevant to MEC design. This paper advances another astute calculation offloading based MEC design in blend with man-made brainpower (AI) innovation. As indicated by the information size in calculation undertaking of portable clients and the exhibition highlights of edge figuring hubs, a calculation offloading and task relocation calculation dependent on errand expectation is proposed. The calculation task expectation dependent on LSTM calculation, calculation offloading procedure for cell phone dependent on errand forecast, and undertaking relocation for edge cloud planning plan are utilized to help with upgrading the edge processing offloading model. Keywords—
Keywords: Artificial Intelligence, Computation Offloading, Edge Computing, Task Migration.
Scope of the Article: Distributed Computing.