Using AI for Dynamic Resource Allocation and Performance Optimization in Software Systems
Keywords:
Dynamic Resource Allocation, Artificial Intelligence, Performance Optimization, Reinforcement Learning, Neural NetworksAbstract
This research work aims at evaluating the possibilities of using artificial intelligence in dynamic resource management and optimization of software system performance. In today’s complex world of application usage, normal methods of resource management are unable to cater to these dynamic needs and fulfill its usage potential. In this work, an assessment of three mainstream AI techniques – reinforcement learning, neural network, and genetic algorithm – is performed based on performance indicators such as resource utilization and consumption, average response time, throughput, costs, prediction capability, stability, and time taken to converge. The results show that the neural networks have the best resource acquisition performance as well as response rates, while the reinforcement learning has the best cost management and flexibility rates. As it has been pointed out, genetic algorithms are quite useful in finding optimization solutions, however real-time responsiveness is lack. Thus, the results provide significant understating of how to choose the proper AI technique depending on the specific application needs which in turn will be useful for organizations willing to improve their resource management using AI-based solutions.
References
S. Tatineni and N. V. Chakilam, "Integrating Artificial Intelligence with DevOps for Intelligent Infrastructure Management: Optimizing Resource Allocation and Performance in Cloud-Native Applications," Journal of Bioinformatics and Artificial Intelligence, vol. 4, no. 1, pp. 109–142, 2024. Available from: https://biotechjournal.org/index.php/jbai/article/view/68
O. C. Agomuo, O. W. B. Jnr, and J. H. Muzamal, "Energy-Aware AI-based Optimal Cloud Infra Allocation for Provisioning of Resources," in 2024 IEEE/ACIS 27th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Jul. 2024, pp. 269–274. Available From: https://doi.org/10.1109/SNPD61259.2024.10673918
N. Mohammad, "Dynamic Resource Allocation Techniques for Optimizing Cost and Performance in Multi-Cloud Environments," International Journal of Cloud Computing (IJCC), vol. 1, no. 1, pp. 1–12, 2023. Available From: https://iaeme-library.com/index.php/IJCC/article/view/IJCC_01_01_001
A. R. Kunduru, "Artificial intelligence usage in cloud application performance improvement," Central Asian Journal of Mathematical Theory and Computer Sciences, vol. 4, no. 8, pp. 42–47, 2023. Available From: https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/491
S. Tatineni and K. Allam, "AI-Driven Continuous Feedback Mechanisms in DevOps for Proactive Performance Optimization and User Experience Enhancement in Software Development," Journal of AI in Healthcare and Medicine, vol. 4, no. 1, pp. 114–151, 2024. Available From: https://healthsciencepub.com/index.php/jaihm/article/view/75
G. K. Walia, M. Kumar, and S. S. Gill, "AI-empowered fog/edge resource management for IoT applications: A comprehensive review, research challenges and future perspectives," IEEE Communications Surveys & Tutorials, 2023. Available From: https://doi.org/10.1109/COMST.2023.3338015
Z. Aghapour, S. Sharifian, and H. Taheri, "Task offloading and resource allocation algorithm based on deep reinforcement learning for distributed AI execution tasks in IoT edge computing environments," Computer Networks, vol. 223, p. 109577, 2023. Available From: https://doi.org/10.1016/j.comnet.2023.109577
A. Gharehgoli, A. Nouruzi, N. Mokari, P. Azmi, M. R. Javan, and E. A. Jorswieck, "AI-based resource allocation in end-to-end network slicing under demand and CSI uncertainties," IEEE Transactions on Network and Service Management, vol. 20, no. 3, pp. 3630–3651, 2023. Available From: https://doi.org/10.1109/TNSM.2023.3243837
J. Lim, "Versatile Cloud Resource Scheduling Based on Artificial Intelligence in Cloud-Enabled Fog Computing Environments," Human-Centric Computing and Information Sciences, vol. 13, p. 54, 2023. Available From: https://doi.org/10.22967/HCIS.2023.13.054
J. Logeshwaran, T. Kiruthiga, and J. Lloret, "A Novel Architecture of Intelligent Decision Model for Efficient Resource Allocation in 5G Broadband Communication Networks," ICTACT Journal on Soft Computing, vol. 13, no. 3, 2023. Available From: https://doi.org/10.21917/ijsc.2023.0420
M. Shuaib, S. Bhatia, S. Alam, R. K. Masih, N. Alqahtani, S. Basheer, and M. S. Alam, "An optimized, dynamic, and efficient load-balancing framework for resource management in the internet of things (IoT) environment," Electronics, vol. 12, no. 5, p. 1104, 2023. Available From: https://doi.org/10.3390/electronics12051104
B. Desai and K. Patil, "Reinforcement learning-based load balancing with large language models and edge intelligence for dynamic cloud environments," Journal of Innovative Technologies, vol. 6, no. 1, pp. 1–13, 2023. Available From: https://academicpinnacle.com/index.php/JIT/article/view/106
J. Singh, P. Singh, M. Hedabou, and N. Kumar, "An efficient machine learning-based resource allocation scheme for SDN-enabled fog computing environment," IEEE Transactions on Vehicular Technology, vol. 72, no. 6, pp. 8004–8017, 2023 Available From: https://doi.org/10.1109/TVT.2023.3242585
