.png){kind=icon}
.png){kind=icon}
Return
Efficient and robust probabilistic guarantees for real-time tasks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jss.2011.12.042.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents a new method for providing probabilistic real-time guarantees to tasks scheduled through resource reservations. Previous work on probabilistic analysis of reservation-based schedulers is extended by improving the efficiency and robustness of the probability computation. Robustness is improved by accounting for a possibly incomplete knowledge of the distribution of the computation times (which is typical in realistic applications). The proposed approach computes a conservative bound for the probability of missing deadlines, based on the knowledge of the probability distributions of the execution times and of the inter-arrival times of the tasks. In this paper, such a bound is computed in realistic situations, comparing it with simulative results and with the exact computation of deadline miss probabilities (without pessimistic bounds). Finally, the impact of the incomplete knowledge of the execution times distribution is evaluated. (C) 2011 Elsevier Inc. All rights reserved.
Keywords:
Real-time computing
Stochastic analysis
Soft real-time
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.1
Papers:
5.4K
Citations:
8.4K
Organization
No organization information available