Issue No. 02 - April-June (2015 vol. 3)
Rajiv Ranjan , Digital Productivity Flagship CSIRO, Australia
Lizhe Wang , School of Computer Science, China University of Geosciences, China
Albert Y. Zomaya , School of Information Technologies, The University of Sydney, Australia
Dimitrios Georgakopoulos , RMIT University, Australia
Xian-He Sun , Department of Computer Science, Illinois Institute of Technology, IL, USA
Guojun Wang , School of Information Science and Engineering, Central South University, Changsha, Hunan Province, China
Cloud computing assembles large networks of virtualised ICT services such as hardware resources (such as CPU, storage, and network), software resources (such as databases, application servers, and web servers) and applications. Big Data applications have become a common phenomenon in domain of science, engineering, and commerce. Large-scale, heterogeneous, and uncertain Big Data applications are becoming increasingly common, yet current cloud resource provisioning methods do not scale well and nor do they perform well under highly unpredictable conditions (data volume, data variety, data arrival rate, etc.). Much research effort have been paid in the fundamental understanding, technologies, and concepts related to autonomic provisioning of cloud resources for Big Data applications, to make cloud-hosted Big Data applications operate more efficiently, with reduced financial and environmental costs, reduced under-utilisation of resources, and better performance at times of unpredictable workload. Targeting the aforementioned research challenges, this special issue compiles recent advances in Autonomic Provisioning of Big Data Applications on Clouds. The special issue articles are briefly summarized.
Big data, Cloud computing
R. Ranjan, L. Wang, A. Y. Zomaya, D. Georgakopoulos, X. Sun and G. Wang, "Recent advances in autonomic provisioning of big data applications on clouds," in IEEE Transactions on Cloud Computing, vol. 3, no. 2, pp. 101-104, 2015.