File caching in data intensive scientific applications on data-grids

  • Authors:

  • Ekow Otoo;Doron Rotem;Alexandru Romosan;Sridhar Seshadri

  • Affiliations:

  • Lawrence Berkeley National Laboratory, University of California, Berkeley, California;Lawrence Berkeley National Laboratory, University of California, Berkeley, California;Lawrence Berkeley National Laboratory, University of California, Berkeley, California;Leonard N. Stern School of Business, New York University, New York

  • Venue:
  • DMG 2005 Proceedings of the First VLDB conference on Data Management in Grids
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present some theoretical and experimental results of an important caching problem which arises frequently in data intensive scientific applications that are run in data-grids. Such applications often need to process several files simultaneously, i.e., the application runs only if all its needed files are present in some disk cache accessible to the compute resource of the application. The set of files requested by an application, all of which must be in cache for the application to run, is called a file-bundle. This requirement introduces the need for cache replacement algorithms that are based on file-bundles rather then individual files. We show that traditional caching algorithms such as Least Recently Used (LRU) and GreedyDual-Size (GDS) are not optimal in this case since they are not sensitive to file-bundles and may hold in the cache non-relevant combinations of files. We propose and analyze a new cache replacement algorithm specifically adapted to deal with file-bundles. Results of experimental studies of the new algorithm, using a disk cache simulation model under a wide range of conditions such as file request distributions, relative cache size, file size distribution, and incoming job queue size, show significant improvement over traditional caching algorithms such as GDS.