  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.
