A Dynamic Storage Allocation Technique Based on Memory Residence Time.
Saved in:
| Title: | A Dynamic Storage Allocation Technique Based on Memory Residence Time. |
|---|---|
| Authors: | Beck, Leland L.1, Jones, Anita |
| Source: | Communications of the ACM. Oct82, Vol. 25 Issue 10, p714-724. 11p. 1 Chart, 13 Graphs. |
| Subjects: | Dynamic storage allocation (Computer science), Computer programming, Memory maps (Computer science), Computer memory management, Computer systems, Production scheduling |
| Abstract: | A new dynamic storage allocation technique, the release-match method, is described. The memory utilization efficiency of this method is compared with that of the first-fit and best-fit methods, using a series of simulations. Results indicate that release-match gives significantly better memory utilization than first-fit or best-fit over a wide range of conditions. In addition, this method can be implemented with only minor increases in time and space overhead as compared with best-fit. The release-match method depends on prior knowledge of the memory residence time for each request. A modification of the release-match method which does not require such knowledge (the age-match method) is described; the performance of this method is investigated using a variety of request distributions. Age-match approaches the performance of release-match when the mean request size is a significant fraction of the total memory space. Its usefulness decreases as the coefficient of variation of the request lifetimes increases. The possibility of using release-match with estimated residence times is considered; some preliminary results are given, and a number of future directions for research are proposed. [ABSTRACT FROM AUTHOR] |
| Copyright of Communications of the ACM is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
