Performance Evaluation and Analysis of Meta-Heuristic Techniques in Cloud Computing

Cloud computing has emerged as a transformative technology, offering on-demand access to scalable computing resources. However, ensuring optimal performance while managing costs remains a challenging task. This research focuses on evaluating and analyzing the performance of various meta-heuristic techniques in cloud computing environments, with a specific emphasis on execution time and cost as critical performance metrics. The study begins by introducing the concept of meta-heuristic techniques, which are intelligent optimization algorithms that have gained prominence in solving complex, dynamic, and computationally expensive problems. These techniques, such as Base Genetic Algorithms, Teaching Learning base optimization, Elephant herding optimization, and Harmony search optimizer have the potential to optimize resource allocation in cloud computing, improving average execution time of tasks, and resource cost efficiency. The analysis of the results demonstrates the effectiveness of meta-heuristic techniques in reducing execution time and managing costs. The findings also highlight the trade-offs between different meta-heuristic approaches, helping cloud administrators make informed decisions when selecting the most suitable technique for specific use cases. In conclusion, this research contributes to the understanding of how meta-heuristic techniques can enhance the performance of cloud computing systems by optimizing resource allocation. By utilizing execution time and cost as key performance metrics, it provides valuable insights for cloud service providers and organizations seeking to improve the efficiency and cost-effectiveness of their cloud-based services. Ultimately, this work aims to facilitate the development of more efficient and cost-conscious cloud computing solutions in an increasingly dynamic and competitive digital landscape. Results shows the performance indicators with four key optimizer in which Elephant herding optimizer outperforms the base GA, HS, and TLO optimizer respectively.