article · Open Collections · 2009
Maximizing buffer and disk utilizations for news on-demand
Cited by 42Open access
Abstract
In this thesis, we study the problem of how to maximize the throughput of a multimedia system, given a fixed amount of buffer space and disk bandwidth, both pre-determined at design-time. Our approach is to maximize the utilization of disk and buffers. We propose two methods. First, we analyze a scheme that allows multiple streams to share buffers. Our analysis and simulation results indicate that buffer sharing could lead to as much as a 50% reduction in total buffer requirements. Second, we develop three prefetching strategies: straight forward prefetching (SP), and two types of intelligent prefetchings (IP1 and 1P2). All these strategies try to read a certain amount of data into memory before a query is actually activated. We demonstrate that SP is not effective at all, but both IP1 and 1P2 can maximize the effective use of buffers and disk, leading to as much as a 40% improvement in system throughput. We also extend the two intelligent prefetching schemes to a multiple disk environment.
Cite this paper
Ng, R. T. (2009). Maximizing buffer and disk utilizations for news on-demand. Open Collections, 451–462. https://doi.org/10.14288/1.0051253
Ng, Raymond T. “Maximizing buffer and disk utilizations for news on-demand.” Open Collections, 2009, pp. 451–62, https://doi.org/10.14288/1.0051253.
Ng, Raymond T. 2009. “Maximizing buffer and disk utilizations for news on-demand.” Open Collections: 451–62. https://doi.org/10.14288/1.0051253.
Ng, R.T. (2009) ‘Maximizing buffer and disk utilizations for news on-demand’, Open Collections, pp. 451–462. Available at: https://doi.org/10.14288/1.0051253.
@article{ng2009maximizing,
title = {{Maximizing buffer and disk utilizations for news on-demand}},
author = {Ng, Raymond T.},
journal = {Open Collections},
pages = {451--462},
year = {2009},
doi = {10.14288/1.0051253}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.