TY - GEN
T1 - Big data analytics-enhanced cloud computing : challenges, architectural elements, and future directions
AU - Buyya, Rajkumar
AU - Ramamohanarao, Kotagiri
AU - Leckie, Chris
AU - Calheiros, Rodrigo N.
AU - Dastjerdi, Amir Vahid
AU - Versteeg, Steve
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:41853
U2 - 10.1109/ICPADS.2015.18
DO - 10.1109/ICPADS.2015.18
M3 - Conference Paper
SN - 9780769557854
SP - 75
EP - 84
BT - Proceedings 2015 IEEE 21st International Conference on Parallel and Distributed Systems ICPADS 2015, 14-17 December 2015 Melbourne, Victoria, Australia
PB - IEEE
T2 - International Conference on Parallel and Distributed Systems
Y2 - 14 December 2015
ER -