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| Content Provider | ACM Digital Library |
|---|---|
| Author | Hertel, H. F. Merikallio, R. A. |
| Abstract | As computing systems and their applications become progressively more complex, the prediction of the performance of a proposed computer system or the effects of changes to an existing system likewise becomes more difficult. Fortunately there has been steady growth in performance evaluation technology to meet these expanding systems analysis requirements. The most desirable predictive tools continue to be analytical mathematical models which usually provide a comprehensive theoretical understanding of system behavior and are economical to operate. However many complex systems resist definition in analytical terms. Performance prediction for these systems is usually attempted through the use of imitative discrete simulation models. Simulative systems modeling can, however, be costly in man time, computer time, and elapsed time. Unless techniques such as those described in this paper are used the cost may be high enough to preclude in-depth modeling for any but the very largest systems. Even with sufficient budget, the elapsed time for systems modeling could be too great for system proposal efforts, where analysis is most needed but time is critical due to proposal deadlines. |
| Starting Page | 197 |
| Ending Page | 209 |
| Page Count | 13 |
| File Format | |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 1974-06-04 |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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