Heterogeneity-aware data placement in Hybrid Clouds
Published in International Conference on Cloud Computing, 2019
Recommended citation: Marquez J.D., Gonzalez J.D., Mondragon O.H. (2019) Heterogeneity-Aware Data Placement in Hybrid Clouds. In: Da Silva D., Wang Q., Zhang LJ. (eds) Cloud Computing – CLOUD 2019. CLOUD 2019. Lecture Notes in Computer Science, vol 11513. Springer https://link.springer.com/chapter/10.1007/978-3-030-23502-4_13
In next-generation cloud computing clusters, performance of data-intensive applications will be limited, among other factors, by disks data transfer rates. In order to mitigate performance impacts, cloud systems offering hierarchical storage architectures are becoming commonplace. The Hadoop File System (HDFS) offers a collection of storage policies that exploit different storage types such as RAM_DISK, SSD, HDD, and ARCHIVE. However, developing algorithms to leverage heterogeneous storage through an efficient data placement has been challenging. This work presents an intelligent algorithm based on genetic programming which allow to find the optimal mapping of input datasets to storage types on a Hadoop file system.
Recommended citation: Marquez J.D., Gonzalez J.D., Mondragon O.H. (2019) Heterogeneity-Aware Data Placement in Hybrid Clouds. In: Da Silva D., Wang Q., Zhang LJ. (eds) Cloud Computing – CLOUD 2019. CLOUD 2019. Lecture Notes in Computer Science, vol 11513. Springer