Date of Award

12-11-2025

Degree Type

Thesis

Degree Name

Master of Science (M.S.)

Department

Computer Science

First Advisor

Tamara Rogers

Abstract

Cloud computing enables flexible, scalable access to virtualised infrastructure but remains challenged by maintaining performance in fault-prone environments. Faults such as virtual machine failures can degrade task allocation and system reliability. However, traditional load balancing algorithms and bio-inspired strategies, including the Honeybee Behaviour Load Balancing algorithm, assume fault-free conditions. This thesis introduces a Fault-Tolerant Honeybee Behaviour Load Balancing algorithm that enhances the original Honeybee Behaviour Load Balancing algorithm by integrating fault detection. The proposed algorithm monitors the health of VMs and dynamically reallocates workloads when faults are detected which improves stability and reliability. The proposed fault tolerant algorithm was implemented and evaluated using CloudSim Plus under varying workloads and cloud configurations of 20, 50, and 75 virtual machines. Comparative experiments against the standard algorithm demonstrate that the Fault Tolerant Honeybee Behaviour Load Balancing algorithm achieves reduced makespan and improved average response time in small to medium fault-prone environments. However, results indicate performance trade-offs under heavy workloads due to monitoring overhead. This research contributes to the advancement of fault-tolerant bio-inspired load balancing techniques, providing insight into the balance between fault resilience and computational efficiency. The findings establish a foundation for future exploration of adaptive and scalable fault-tolerant mechanisms in large-scale cloud infrastructures.

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