GARTL: a real-time programming language based on multi-version computation

Marlin, Chris D.; Zhao, Wei; Doherty, Gavin; Bohonis, A.

doi:10.1109/iccl.1990.63766

Cited by 3 publications

(2 citation statements)

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“…Other research that addresses real-time scheduling concerns by providing multiple solution methods includes the work on imprecise computation [7], the GARTL real-time programming language [8], and the Flex language [9]. In this paper we investigate the implications of design-to-time on the real-time scheduling and controlling of task execution for tasks with soft and hard deadlines 2 .…”

Section: Is a Graph Showingmentioning

confidence: 99%

“…Before problem solving actually begins, control knowledge sources post the system goal, which triggers the posting of a top-level strategy for meeting that goal. In this example a goal-directed top-level strategy is posted 8 . Additional control knowledge sources elaborate this strategy into default heuristics for controlling the execution of control knowledge sources and an initial control goal of finding any new vehicles that appear.…”

Section: An Example Problem With Adequate Resourcesmentioning

confidence: 99%

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Design-to-time real-time scheduling

Garvey

Lesser

1993

IEEE Trans. Syst., Man, Cybern.

126

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Design-to-time is an approach to problem-solving in resource-constrained domains where: multiple solution methods are available for tasks, those solution methods make tradeoffs in solution quality versus time, and satisficing solutions are acceptable. Design-to-time involves designing a solution to a problem that uses all available resources to maximize the solution quality within the available time. This paper defines the design-to-time approach in detail, contrasting it to the anytime algorithm approach, and presents a heuristic algorithm for designto-time real-time scheduling. Our blackboard architecture that implements the design-to-time approach is discussed and an example problem and solution from the Distributed Vehicle Monitoring Testbed (DVMT) is described in detail. Experimental results, generated using a simulation, show the effects of various parameters on scheduler performance. Finally we discuss future research goals and plans.

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Section: Is a Graph Showingmentioning

confidence: 99%

Section: An Example Problem With Adequate Resourcesmentioning

confidence: 99%