Big Data Analytics-Enhanced Cloud Computing: Challenges, Architectural Elements, and Future Directions

TL;DR

This paper explores how Big Data analytics can improve cloud computing by addressing scalability, workload prediction, and anomaly detection, highlighting challenges, architectures, and future research directions.

Contribution

It provides a comprehensive overview of integrating Big Data analytics into cloud computing, including architectures and open issues for enhancing cloud operations.

Findings

Big Data analytics helps in workload prediction and resource management.

Architectures for anomaly detection in cloud environments are discussed.

Open issues include scalability and real-time processing challenges.

Abstract

The emergence of cloud computing has made dynamic provisioning of elastic capacity to applications on-demand. Cloud data centers contain thousands of physical servers hosting orders of magnitude more virtual machines that can be allocated on demand to users in a pay-as-you-go model. However, not all systems are able to scale up by just adding more virtual machines. Therefore, it is essential, even for scalable systems, to project workloads in advance rather than using a purely reactive approach. Given the scale of modern cloud infrastructures generating real time monitoring information, along with all the information generated by operating systems and applications, this data poses the issues of volume, velocity, and variety that are addressed by Big Data approaches. In this paper, we investigate how utilization of Big Data analytics helps in enhancing the operation of cloud computing environments. We discuss diverse applications of Big Data analytics in clouds, open issues for enhancing cloud operations via Big Data analytics, and architecture for anomaly detection and prevention in clouds along with future research directions.