Title: Osmosis machine learning load balancing of healthcare tasks in cutting edge technologies with smart grid

Authors: Basetty Mallikarjuna

Addresses: School of Computing Science and Engineering, Galtotias University, Greater Noida, Uttar Pradesh, 203-201, India

Abstract: Smart grid communication requires an embedded approach on IoT-based cloud, fog computing and big data. In order to provide e-health and m-health services, the allocation of tasks on resources in healthcare services is crucial. The primary need for users in the healthcare industry is the solution to the bottleneck of service level agreement (SLA) and accomplishes the quality of service (QoS) parameters. The add-on objective is to achieve effective resource utilization and satisfaction of the end-user application for effective communication and load balancing of tasks on cutting edge technologies. The machine learning approach in osmosis load balancing of tasks at the end of the fog service provider (FSP) level reduces the network utilisation time, latency, usage of energy, etc. The results proves that fog nodes are efficient than the cloud nodes, and also the experimental results proved that the proposed model is efficient than the various other existing approaches.

Keywords: smart grid; IoT-based cloud; big data; e-health; electronic health; m-health; ML approach on osmosis; fog service provider; FSP; latency; usage of energy.

DOI: 10.1504/IJSGGC.2022.128001

International Journal of Smart Grid and Green Communications, 2022 Vol.2 No.2, pp.150 - 169

Received: 22 Jun 2020
Accepted: 06 Aug 2020
Published online: 04 Jan 2023 *

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