Interactive schedulability analysis

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Bibliographic Details
Title: Interactive schedulability analysis
Authors: Unmesh D. Bordoloi, Samarjit Chakraborty
Contributors: The Pennsylvania State University CiteSeerX Archives
Source: http://www.arl.wustl.edu/~gorinsky/cited/GMT_Interactive_Bordoloi_Chakraborty_2007.pdf.
Publisher Information: IEEE Computer Society
Publication Year: 2006
Collection: CiteSeerX
Subject Terms: Computers in Other Systems—Real time General Terms, Algorithms, Design, Performance, Verification Additional Key Words and Phrases, Schedulability analysis, recurring real-time task model, interactive design, performance debugging, nonfunctional constraints ACM Reference Format
Description: A typical design process for real-time embedded systems involves choosing the values of certain system parameters and performing a schedulability analysis to determine whether all deadline constraints can be satisfied. If such an analysis returns a negative answer, then some of the parameters are modified and the analysis is invoked once again. This iteration is repeated until a schedulable design is obtained. However, the schedulability analysis problem for most task models is intractable (usually co-NP hard) and, hence, such an iterative design process is often very expensive. To get around this problem, we introduce the concept of “interactive ” schedulability analysis. It is based on the observation that if only a small number of system parameters are changed, then it is not necessary to rerun the full schedulability analysis algorithm, thereby making the iterative design process considerably faster. We refer to this analysis as being “interactive ” because it is supposed to be run in an interactive mode. This concept is fairly general and can be applied to a wide variety of task models. In this paper, we have chosen the recurring real-time task model, because it can be used to represent realistic applications from the embedded systems domain (containing conditional branches and fine-grained deadline constraints). Our experimental results show that using our scheme can lead to more than 20 × speedup for each invocation of the schedulability analysis algorithm, compared to the case where the full algorithm is run.
Document Type: text
File Description: application/pdf
Language: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.416.2279; http://www.arl.wustl.edu/~gorinsky/cited/GMT_Interactive_Bordoloi_Chakraborty_2007.pdf
Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.416.2279
http://www.arl.wustl.edu/~gorinsky/cited/GMT_Interactive_Bordoloi_Chakraborty_2007.pdf
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Accession Number: edsbas.C4303C33
Database: BASE
Description
Abstract:A typical design process for real-time embedded systems involves choosing the values of certain system parameters and performing a schedulability analysis to determine whether all deadline constraints can be satisfied. If such an analysis returns a negative answer, then some of the parameters are modified and the analysis is invoked once again. This iteration is repeated until a schedulable design is obtained. However, the schedulability analysis problem for most task models is intractable (usually co-NP hard) and, hence, such an iterative design process is often very expensive. To get around this problem, we introduce the concept of “interactive ” schedulability analysis. It is based on the observation that if only a small number of system parameters are changed, then it is not necessary to rerun the full schedulability analysis algorithm, thereby making the iterative design process considerably faster. We refer to this analysis as being “interactive ” because it is supposed to be run in an interactive mode. This concept is fairly general and can be applied to a wide variety of task models. In this paper, we have chosen the recurring real-time task model, because it can be used to represent realistic applications from the embedded systems domain (containing conditional branches and fine-grained deadline constraints). Our experimental results show that using our scheme can lead to more than 20 × speedup for each invocation of the schedulability analysis algorithm, compared to the case where the full algorithm is run.