Title :
Efficient distributed disk caching in data grid management
Author :
Jiang, Song ; Zhang, Xiaodong
Author_Institution :
Dept. of Comput. Sci., Coll. of William & Mary, Williamsburg, VA, USA
Abstract :
Effectively utilizing disk caches is critical for delivering and sharing data in data-grids considering the large sizes of requested files and excessively prolonged file transmission time. An essential component in the disk cache management is its replacement policy that determines which file(s) are least valuable and should be evicted to create space for incoming files. Though a large number of replacement algorithms for data objects of different sizes have been proposed recently in the domain of Web-caching and disk caching in data grids, they inherit the shortcomings of the LRU and LFU replacements in characterization access patterns. In order to address this limit, we propose a technique to measure relative file access locality strength - how soon a file is to be re-accessed before being evicted compared with other files. When we estimate the in-cache reaccess probability, we take the disk space consumed by accessed files as well as disk cache size into consideration. Using relative locality strength estimation, we are able to accurately rank the value of each file for being cached, and select the file(s) with least values for replacement. Our simulation results show that our proposed policy is the most effective one among existing policies in interpreting access patterns, and considering achieves performance improvement measured by hit ratios and byte hit ratios.
Keywords :
cache storage; computational complexity; grid computing; performance evaluation; shared memory systems; Web-caching; access patterns; data grid management; data objects; disk cache management; distributed disk caching; file access locality strength; file transmission time; in-cache reaccess probability; relative locality strength estimation; replacement algorithms; requested files; sharing data; Cache memories; Complexity theory; Conference management; Distributed computing; Grid computing; Shared memory systems;
Conference_Titel :
Cluster Computing, 2003. Proceedings. 2003 IEEE International Conference on
Print_ISBN :
0-7695-2066-9
DOI :
10.1109/CLUSTR.2003.1253346