Journal of Machine and Computing


Precision Offloading in Edge Computing: Leveraging Predictive Model



Journal of Machine and Computing

Received On : 18 May 2024

Revised On : 30 July 2024

Accepted On : 06 August 2024

Published On : 05 October 2024

Volume 04, Issue 04

Pages : 1079-1091


Abstract


The intended effect of the investigation is to provide sophisticated prediction and decision-making models in order to optimize service delivery and improve the Quality of Experience (QoE) for users. This research tackles the problems that are associated with job offloading in edge computing settings. In order to reduce service latency and improve overall performance, the Bi-Directional Long Short-Term Memory (B-LSTM) model is used. This model provides the ability to forecast task creation and server load. In order to accommodate the particular qualities of different devices, the Selective Objective Offloading Decision (SOOD) approach is presented. This method makes use of the TOPSIS methodology to turn server assessment into a decision-making issue that involves several criteria. A considerable increase of 98.4% in user quality of experience is achieved by the SOOD paradigm. In addition, the Rapid Offloading Decision (ROD) model is presented in order to manage unexpected work patterns. This is accomplished by using the log information of surrounding devices, which results in instantaneous and dependable offloading choices. Through the usage of prediction algorithms and selective decision-making, this research gives a complete strategy to improving the efficiency of edge computing. The goal of this technique is to maximize the utilization of servers and the user experience.


Keywords


IoT, Deep Learning, B-LSTM, QoE, SOOD.


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The authors would like to thank to the reviewers for nice comments on the manuscript.


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Cite this article


Suganya T S, Saivijayalakshmi J, Karthik M, Keerthana T, Srikanth V and Vidhya U, “Precision Offloading in Edge Computing: Leveraging Predictive Model”, Journal of Machine and Computing, pp. 1079-1091, October 2024. doi:10.53759/7669/jmc202404100.


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© 2024 Suganya T S, Saivijayalakshmi J, Karthik M, Keerthana T, Srikanth V and Vidhya U. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.