Fast Load Balancing Approach for Growing Clusters by Bioinformatics

TL;DR

This paper introduces a bioinformatics-inspired fast load balancing method for real-time patient assignment to specialist clusters, effectively managing growing request volumes and ensuring balanced load during emergencies.

Contribution

It proposes a novel Gaussian mixture model-based algorithm for dynamic, real-time patient-to-cluster assignment, improving load balancing in healthcare systems.

Findings

Efficient handling of any size of patient requests.

Rapid patient placement to specialist clusters.

Effective load balancing during natural calamities.

Abstract

This paper presents Fast load balancing technique inspired by Bioinformatics is a special case to assign a particular patient with a specialist physician cluster at real time. The work is considered soft presentation of the Gaussian mixture model based on the extracted features supplied by patients. Based on the likelihood ratio test, the patient is assigned to a specialist physician cluster. The presented algorithms efficiently handle any size and any numbers of incoming patient requests and rapidly placed them to the specialist physician cluster. Hence it smoothly balances the traffic load of patients even at a hazard situation in the case of natural calamities. The simulation results are presented with variable size of specialist physician clusters that well address the issue for randomly growing patient size.