AI-Powered Adaptive Resource Allocation and Scheduling in Cloud Computing: A Unified Optimization Framework

Abstract

Cloud computing has grown into something of a backbone for nearly everything online, but managing its resources efficiently is still a tricky business. Servers manipulate countless demands CPU, memory, bandwidth and keeping that balance right is what makes or breaks performance, scalability, and even energy use. Traditional scheduling like Round Robin or First-Come-First-Serve does the job, but only up to a point. They’re rigid, and the cloud isn’t. So lately, researchers have been turning to artificial intelligence algorithms that learn, adapt, and even anticipate what’s coming next. Among the more interesting approaches is the hybrid LSTM–GA model, which pairs Long Short-Term Memory networks (for predicting workloads) with Genetic Algorithms (for optimizing how resources are actually assigned). Looking across recent work from 2023 to 2025, a pattern emerges: the field is moving toward unified, self-tuning systems that can balance cost, performance, and sustainability without constant human tweaking.

Introduction

Cloud computing enables organizations to access shared computing resources through scalable, on-demand services such as IaaS, PaaS, and SaaS, eliminating the need for physical infrastructure. As cloud systems expand to support modern digital applications—including AI, IoT, and data-intensive services—efficient resource allocation and scheduling have become essential. Poor management leads to degraded performance, energy waste, SLA violations, and increased operational costs. Traditional scheduling methods (FCFS, RR, SJF) are simple but struggle with dynamic, large-scale workloads. AI and machine learning offer more adaptive, predictive, and autonomous solutions, making them critical in modern cloud environments.

Resource management relies on both allocation (distributing CPU, memory, and bandwidth) and scheduling (deciding task execution order). AI-based approaches improve efficiency, scalability, and QoS by enabling real-time prediction, workload forecasting, and intelligent decision-making. Machine learning—including supervised, unsupervised, and reinforcement learning—and deep learning techniques enhance cloud performance by identifying patterns and enabling adaptive behavior.

Heuristic algorithms offer fast but suboptimal results, while metaheuristic methods like Genetic Algorithms (GA) and Ant Colony Optimization (ACO) provide stronger optimization for complex scheduling problems. Hybrid approaches combine these methods to balance exploration and exploitation, producing higher-quality solutions for dynamic cloud environments.

Recent research highlights prediction-based scheduling, optimization-oriented algorithms, and hybrid intelligent systems. However, many existing models are either purely predictive or purely optimization-based, lacking an integrated approach that adapts across multiple cloud management layers.

To address this gap, the study proposes a hybrid LSTM–GA framework that uses LSTM networks for workload prediction and GA for optimized resource scheduling. The goals include enhancing scalability, improving energy efficiency, reducing operational costs, and strengthening SLA compliance. The framework offers end-to-end adaptability, balancing performance and energy consumption through multi-objective optimization. Positioned within the literature, this hybrid model represents a next-generation solution that unifies prediction and optimization to meet the growing complexity of cloud computing systems.

Conclusion

Cloud computing continues to evolve rapidly, demanding smarter, more adaptive resource management strategies. Traditional scheduling methods, while simple, struggle to meet the dynamic and complex demands of modern cloud environments. AI-driven techniques—especially those combining prediction and optimization—offer a promising path forward. This study reviewed a wide range of approaches, from heuristic and metaheuristic algorithms to machine learning and hybrid frameworks. Among these, the proposed LSTM–GA hybrid model stands out for its ability to integrate time-series workload prediction with global optimization. By leveraging LSTM’s forecasting capabilities and GA’s evolutionary search, the model enables intelligent, scalable, and energy-efficient resource allocation. Future research should focus on real-world validation, multi-cloud deployment, and integration with edge and IoT systems. Additional directions include enhancing security, improving trust through anomaly detection, and refining multi-objective optimization for better trade-off management between performance and energy consumption. This work lays the foundation for intelligent, scalable, and trustworthy cloud resource management using AI-based hybrid models.

References

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Copyright

Copyright © 2025 Mahesh Patel, Dr. Swati Namdev. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.