Views provided by UsageCounts
This archive includes the research data associated to the paper: Giuliano Casale. Accelerating Performance Inference over Closed Systems by Asymptotic Methods. Proc. ACM Meas. Anal. Comput. Syst., 1(1), 2017. The paper is accepted for presentation at ACM SIGMETRICS 2017. The research data requires MATLAB 2015a or later. Four datasets are included, each corresponding to a section of the paper: - sec5.3.1: Small and medium models without infinite server nodes (Section 5.3.1) - sec5.3.2: Large models without infinite server nodes (Section 5.3.2) - sec5.3.3: Models with infinite server nodes (Section 5.3.3) - sec5.4: Optimization programs (Section 5.4) A description of each dataset is included in the README.TXT file inside each folder.
Normalizing constant, queueing network, closed model
Normalizing constant, queueing network, closed model
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 3 |

Views provided by UsageCounts