Issue No. 07 - July (2014 vol. 26)
Gang Chen , Computer Science College, Zhejiang University, Hangzhou, Zhejiang, P.R. China
H. V. Jagadish , Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI
Dawei Jiang , School of Computing, National University of Singapore, Singapore
David Maier , Department of Computer Science, Portland State University, Portland, OR
Beng Chin Ooi , School of Computing, National University of Singapore, Singapore
Kian-Lee Tan , School of Computing, National University of Singapore, Singapore
Wang-Chiew Tan , Computer Science Department, University of California, Santa Cruz, Santa Cruse, CA
Companies are increasingly moving their data processing to the cloud, for reasons of cost, scalability, and convenience, among others. However, hosting multiple applications and storage systems on the same cloud introduces resource sharing and heterogeneous data processing challenges due to the variety of resource usage patterns employed, the variety of data types stored, and the variety of query interfaces presented by those systems. Furthermore, real clouds are never perfectly symmetric—there often are differences between individual processors in their capabilities and connectivity. In this paper, we introduce a federation framework to manage such heterogeneous clouds. We then use this framework to discuss several challenges and their potential solutions.
Servers, Data processing, Resource management, Computer architecture, Companies, Cloud computing
G. Chen et al., "Federation in Cloud Data Management: Challenges and Opportunities," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 7, pp. , 2014.