2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 12 , Issue 1 , PP: 15-27, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Takura Wekwete 1 *
Doi: https://doi.org/10.54216/AJBOR.120102
The study proposes a GTO-FTASS (Gorilla Troop Optimizer-Based Fault Tolerant Aware Scheduling Scheme) for improving the reliability and performance in the cloud computing context. Cloud systems are more likely to fail due to the architecture of these layers and dependence on both the hardware and software, therefore require more sophisticated fault-tolerant solutions. The preliminary to this work is the design of an adaptive GTO-FTASS with a fitness function based on two constraints: Expected Time of Completion (ETC) and Failure probability that were derived from the gorilla value system. The approach provides resource utilization and task planning with the provision of fault recovery hence reducing exposure to time loss and operational vulnerability. MGS outperforms several state-of-the-art models, such as MTCT, MAXMIN, ACO, NSGA-II, and DCLCA in terms of makes pan, failure ratio and failure slowdown. Finally, the applicability of experimental validation with various situations and fluctuating intensities demonstrates the scalability of the model and its stability under pressure, decreased failure rates and increased effectiveness of performed tasks. Through the approaches to latency, resource, and error correction, GTO-FTASS is an investment that stewards have to make to cut costs and achieve high performance on clouds. The framework also provides competitive benefit and robustness for cloud enterprising in fluctuating and crucial strategic applications.
Fault Tolerance , Gorilla Troop Optimizer (GTO) , Fault Tolerant Aware Scheduling Scheme , Virtual Machines (VM) , Service-Level Agreements , Return on Investment and Resource Optimization
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