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Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Ahyoung Lee1, Jui Mhatre1, Rupak Kumar Das2, Min Hong3,*
1 Department of Computer Science, Kennesaw State University, Marietta, GA, USA
2 Department of Computer Science, University of Minnesota Duluth, Duluth, MN, USA
3 Department of Computer Software Engineering, Soonchunhyang University, Asan, Korea
* Corresponding Author: Min Hong. Email:

Computers, Materials & Continua 2023, 74(1), 435-452. https://doi.org/10.32604/cmc.2023.029340

Received 02 March 2022; Accepted 26 May 2022; Issue published 22 September 2022

Abstract

Healthcare is a fundamental part of every individual’s life. The healthcare industry is developing very rapidly with the help of advanced technologies. Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises, as well as by patients from their mobile devices through communication interfaces. These systems promote reliable and remote interactions between patients and healthcare professionals. However, there are several limitations to these innovative cloud computing-based systems, namely network availability, latency, battery life and resource availability. We propose a hybrid mobile cloud computing (HMCC) architecture to address these challenges. Furthermore, we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture. We compare them, to identify the strengths and weaknesses of each algorithm; and provide their comparative results, to show latency and energy consumption performance. Challenging issues for cloud-based healthcare systems are discussed in detail.

Keywords

Mobile cloud computing; hybrid mobile cloud computing; load balancing; healthcare solution

Cite This Article

A. Lee, J. Mhatre, R. K. Das and M. Hong, "Hybrid mobile cloud computing architecture with load balancing for healthcare systems," Computers, Materials & Continua, vol. 74, no.1, pp. 435–452, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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