The imprecise computation model is one of the flexible computation models used to construct real-time systems. It is especially useful when the worst case execution times are difficult to estimate or the execution times vary widely. Although there are several ways to implement this model, they have not attained much attentions of real-world application programmers to date due to their unrealistic assumptions and high dependency on the execution environment. In this paper, we present an integrated approach for implementing the imprecise computation model. In particular, our research covers three aspects. First, we present a new imprecise computation model which consists of a mandatory part, an optional part, and another mandatory part called wind-up part. This wind-up part allows application programmers to explicitly incorporate into their programs the exact operations needed for safe degradation of performance when there is a shortage in resources. Second, we describe a scheduling algorithm called Mandatory-First with Wind-up Part (M-FWP) which is based on the Earliest Deadline First strategy. This algorithm, unlike scheduling algorithms developed for the classical imprecise computation model, is capable of scheduling a mandatory portion after an optional portion. Third, we present a dynamic priority server method for an efficient implementation of the M-FWP algorithm. We also show that the number of the proposed server at most needed per node is one. In order to estimate the performance of the proposed approach, we have implemented a real-time operating system called RT-Frontier. The experimental analyses have proven its ability to implement tasks based on the imprecise computation model without requiring any knowledge on the execution time of the optional part. Moreover, it also showed performance gain over the traditional checkpointing technique.
|ジャーナル||IEICE Transactions on Information and Systems|
|出版ステータス||Published - 2003 10|
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Artificial Intelligence