Optimizing Scalable Online Services: A Dynamic Resource Allocation Approach Utilizing Artificial Intelligence

Abstract

The proliferation of cloud-based services has necessitated the development of sophisticated resource management strategies to ensure scalability and reliability. This paper introduces an artificial intelligence (AI)-driven approach to dynamic resource allocation (DRA) for online services, aiming to address the inefficiencies of traditional static resource provisioning methods. The primary objective is to develop a predictive model to anticipate resource demand fluctuations and allocate cloud resources in real-time, enhancing service performance and user satisfaction.
This research aims to investigate the effectiveness of AI-driven dynamic resource allocation (DRA) for scalable online services. We aimed to address the question of how AI can be leveraged to optimize resource utilization and maintain service availability in cloud computing environments. Our methods involved simulating different resource allocation strategies, including AI-driven, static, and rule-based approaches, and comparing their performance using synthetic data.
Our results demonstrate that the AI-driven DRA method outperforms static and rule-based methods regarding resource utilization and service availability. The AI-driven approach dynamically adjusts resource allocations based on predicted user traffic, leading to more efficient resource usage. In contrast, static and rule-based methods allocate resources at fixed levels, which may result in underutilization or resource contention during peak periods.
Key findings indicate that the AI-driven DRA system optimizes resource utilization, contributes to cost savings for cloud service providers, and improves the overall user experience. The adaptability and accuracy of the AI models in predicting and managing resource needs suggest that AI-driven DRA could be a game-changer for managing cloud resources in online platforms.

Keywords: Dynamic Resource Allocation, Scalable Online Services, Artificial Intelligence, Cloud Computing, Predictive Analytics.

JEL Classification: C8, C80