Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions

TL;DR

This paper reviews machine learning techniques for resource management in cloud computing's IaaS, highlighting current challenges, approaches, and future research directions to improve scalability, efficiency, and cost-effectiveness.

Contribution

It provides a comprehensive review of ML-based resource management in cloud IaaS, identifying challenges, analyzing existing solutions, and proposing future research directions.

Findings

ML improves workload estimation and task scheduling.

Current approaches face limitations in dynamic environments.

Future directions include advanced ML models and adaptive algorithms.

Abstract

Cloud computing has rapidly emerged as model for delivering Internet-based utility computing services. In cloud computing, Infrastructure as a Service (IaaS) is one of the most important and rapidly growing fields. Cloud providers provide users/machines resources such as virtual machines, raw (block) storage, firewalls, load balancers, and network devices in this service model. One of the most important aspects of cloud computing for IaaS is resource management. Scalability, quality of service, optimum utility, reduced overheads, increased throughput, reduced latency, specialised environment, cost effectiveness, and a streamlined interface are some of the advantages of resource management for IaaS in cloud computing. Traditionally, resource management has been done through static policies, which impose certain limitations in various dynamic scenarios, prompting cloud service providers to adopt data-driven, machine-learning-based approaches. Machine learning is being used to handle a variety of resource management tasks, including workload estimation, task scheduling, VM consolidation, resource optimization, and energy optimization, among others. This paper provides a detailed review of challenges in ML-based resource management in current research, as well as current approaches to resolve these challenges, as well as their advantages and limitations. Finally, we propose potential future research directions based on identified challenges and limitations in current research.