.png){kind=icon}
.png){kind=icon}
Return
A block-level caching optimization method for mobile transparent computing
.png){kind=icon}
PRE
AI
DOI：10.1007/s12083-017-0554-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In mobile transparent computing, a large number of concurrent data requests from heterogeneous clients via the network need to be processed in a timely fashion, and servers have to repeatedly fetch (search and read) the data from storage, which may cause numerous I/O costs. Generally, disk access speed is more limited than memory; therefore, massive I/O operations at servers may become a bottleneck for the system, and transport delay of the total network caused by the limitation of wireless bandwidth and stability may lead to poorer user experience. Hence, caching method plays a significant role in performance improvement of transparent computing systems. In this paper, we propose a block-level caching optimization method for the server and client by analyzing the system bottleneck in mobile transparent computing. We first analyze the storage format of the data file and the three-layer structure in the server according to the characteristics of requesting data from the client to the server and propose a block-level cache based on the access time and access frequency for the server. Second, considering the restriction of bandwidth and stability of the wireless network, we analyze network boot processes from the client's startup and propose a client block-level cache optimization combined with local storage access technology. Finally, experimental results demonstrate that the server block-level cache optimization can effectively reduce the amount of server disk I/O, improving the concurrent ability of the server. In addition, the client block-level cache can significantly increase startup speed of the client, reduce network traffic and improve user experience.
Keywords:
I/O request
Block-level cache
Caching algorithm
Mobile transparent computing
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.6
Papers:
2.2K
Citations:
2.9K