Osmosis machine learning load balancing of healthcare tasks in cutting edge technologies with smart grid. (7th December 2022)

• Record Type: Journal Article
• Authors: Mallikarjuna, Basetty
• Is Part Of: International journal of smart grid and green communications. Volume 2:Number 2(2022)
• Page Start: 150
• Page End: 169
• Publication Date: 2022-12-07
• Subjects: 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; Smart power grids -- Computer networks -- Periodicals; Smart power grids -- Periodicals; Wireless communication systems -- Security measures -- Periodicals; Wireless communication systems -- Energy consumption -- Periodicals; 333.7911028546
• Languages: English
• ISSNs: 2052-2002

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.