Jia Zou, Jing Xiao, et al.
ICDM 2010
Storage and memory systems for modern data analytics are heavily layered, managing shared persistent data, cached data, and non-shared execution data in separate systems such as a distributed file system like HDFS, an in-memory file system like Alluxio, and a computation framework like Spark. Such layering introduces significant performance and management costs. In this paper, we propose a single system called Pangea that can manage all data—both intermediate and long-lived data, and their buffer/caching, page replacement, data placement optimization, and failure recovery—all in one monolithic distributed storage system, without any layering. We present a detailed performance evaluation of Pangea and show that its performance compares favorably with several widely used layered systems such as Spark.
Jia Zou, Jing Xiao, et al.
ICDM 2010
Jim Challenger, Paul Dantzig, et al.
ACM/IEEE SC 1998
Arun Iyengar, Daniela Rosu
Scientific Programming
Qi Zhang, Ling Liu, et al.
ICWS 2016