AI-Augmented Cloud Resource Allocation for Big Data Analytics
DOI:
https://doi.org/10.15662/IJEETR.2023.0505002Keywords:
AI-augmented resource allocation, cloud computing, big data analytics, machine learning, reinforcement learning, workload prediction, energy efficiency, Quality of ServiceAbstract
The exponential growth of big data analytics applications has intensified the demand for scalable, efficient, and intelligent cloud resource allocation strategies. Traditional resource management methods often fall short in dynamically adapting to the complex and fluctuating workloads characteristic of big data environments. This paper investigates the integration of Artificial Intelligence (AI) techniques to augment cloud resource allocation, enhancing performance, cost efficiency, and scalability for big data analytics. We present a hybrid AI framework that leverages machine learning models and reinforcement learning algorithms to predict workload patterns and optimize resource provisioning in real-time. The research methodology involves simulation using cloud computing platforms and synthetic workload traces to evaluate the proposed AI-augmented allocation system against conventional heuristics. Key performance metrics assessed include resource utilization, task completion time, energy consumption, and operational cost. Results demonstrate that AI-augmented resource allocation significantly improves prediction accuracy for workload demands, enabling proactive scaling and reducing resource wastage. Reinforcement learning policies adapt to changing workloads by continuously refining allocation decisions, resulting in up to 30% improvement in resource utilization and 20% reduction in energy consumption. The system also enhances Quality of Service (QoS) by minimizing task execution delays and avoiding resource contention. Challenges such as model training overhead, data privacy concerns, and integration complexity are discussed. The study concludes that AI-augmented cloud resource allocation provides a promising approach to meet the dynamic demands of big data analytics, improving both system efficiency and sustainability. Future work will explore federated learning techniques to enhance privacy and scalability, as well as realtime deployment on commercial cloud platforms. This research offers valuable insights for cloud service providers and data scientists aiming to optimize resource management in big data ecosystems.
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