Resource Management on Cloud Computing Using Machine Learning

Efficient resource management within cloud computing stands as a multifaceted challenge, involving the dynamic allocation and scaling of resources to suit the ever-shifting demands of applications and workloads. Conventional resource management systems hinge on rigid, static policies, often resulting in inefficiencies, leading either to resource overprovisioning or under-provisioning. Such scenarios tend to escalate costs for both cloud providers and customers, subsequently leading to service level agreement (SLA) breaches. Consequently, the pursuit for optimal solutions in cloud computing resource management has gained significant attention among researchers and developers. Specifically, machine learning techniques have emerged as a promising avenue due to their extensive applicability in this domain. This paper presents a comprehensive survey that explores the utilization of machine learning techniques for resource management systems within the realm of cloud computing. The first approach employs the versatile K-means clustering algorithm, introducing a resource management scheme that organizes virtual machines (VMs) into clusters based on their resource requirements. Each cluster is led by a representative VM, and the efficacy of this scheme is evaluated through cloud simulation. The results demonstrate a significant improvement in resource utilization and a reduction in energy consumption. Beyond these benefits, K-means clustering exhibits potential in identifying workload clusters, anomaly detection, and optimizing resource allocation. This approach showcases the adaptability and efficiency of K-means clustering in enhancing overall cloud resource management. The second approach introduces a resource management system (RMS) in the cloud environment, leveraging a decision tree (DT) algorithm. The DT model is trained on historical resource usage data, establishing relationships between workload characteristics and resource requirements. This enables the prediction of future resource needs for informed allocation decisions. Through simulation, the RMS outperforms a static resource allocation policy, achieving higher resource utilization and cost efficiency.Acknowledging the dynamic nature of cloud workloads, the decision tree-based approach highlights the promise of machine learning techniques in enhancing resource management in the cloud. The third approach introduces a Random Forest (RF) model, known for its ensemble learning capabilities. RF extends beyond individual decision trees, offering improved predictive accuracy and robustness. By harnessing the collective power of multiple decision trees, RF proves effective in handling complex relationships within cloud workloads, providing a more nuanced understanding of resource demands. Neural Networks (NN) is a sophisticated tool for autonomous cloud resource management. NN models, as showcased by Zhang et al. (2022), exhibit adaptability and learning capabilities, enabling autonomous allocation of resources based on historical and real-time data. The integration of Neural Network into cloud-resource management systems marks a move towards more intelligent and self-regulating frameworks, addressing the evolving demands of cloud computing.