.png){kind=icon}
.png){kind=icon}
Return
Performing Initiative Data Prefetching in Distributed File Systems for Cloud Computing
.png){kind=icon}
PRE
AI
DOI：10.1109/TCC.2015.2417560.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents an initiative data prefetching scheme on the storage servers in distributed file systems for cloud computing. In this prefetching technique, the client machines are not substantially involved in the process of data prefetching, but the storage servers can directly prefetch the data after analyzing the history of disk I/O access events, and then send the prefetched data to the relevant client machines proactively. To put this technique to work, the information about client nodes is piggybacked onto the real client I/O requests, and then forwarded to the relevant storage server. Next, two prediction algorithms have been proposed to forecast future block access operations for directing what data should be fetched on storage servers in advance. Finally, the prefetched data can be pushed to the relevant client machine from the storage server. Through a series of evaluation experiments with a collection of application benchmarks, we have demonstrated that our presented initiative prefetching technique can benefit distributed file systems for cloud environments to achieve better I/O performance. In particular, configuration- limited client machines in the cloud are not responsible for predicting I/O access operations, which can definitely contribute to preferable system performance on them.
Keywords:
Mobile cloud computing
distributed file systems
time series
server-side prediction
initiative data prefetching
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
5
Papers:
1.8K
Citations:
4.3K
Organization
View more organizations