JOURNAL ARTICLE

Runtime support for reconfigurable real-time embedded systems

Abstract

As the embedding environment becomes more and more complex so does the embedded system itself. An aspect of the complexity, the demand for robust and fault tolerant embedded solutions is ever increasing. Consequently the embedded system design and development face new challenges including modeling, representation, execution and implementation issues. These issues cannot be addressed adequately without a matching underlying system architecture. Model integrated computing (MIC) architectures relying on multi-aspect modeling and variety of models of computation are proven to be promising alternatives for designing and generating manageable complex computing systems for various application domains. With respect to implementation one of the substantial questions is how the MIC based (self-) adapting design can be mapped into a (reconfigurable) distributed hardware platform. The paper presents a novel middleware supporting execution of adapting/reconfigurable real-time programs. The main features of MIC and its relation to adaptivity and computation model based programming are overviewed. A minimal set of functionalities, which enables to construct real-time program structures serving as an integration framework for various models of computation is presented. The design principles of the runtime environment are summarized.

Keywords:
Computer science Distributed computing Middleware (distributed applications) Embedding Computation Embedded system Model of computation Computer architecture Variety (cybernetics) Construct (python library) Relation (database) Programming language Artificial intelligence

Metrics

3
Cited By
0.61
FWCI (Field Weighted Citation Impact)
10
Refs
0.64
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Embedded Systems Design Techniques
Physical Sciences →  Computer Science →  Hardware and Architecture
Real-Time Systems Scheduling
Physical Sciences →  Computer Science →  Hardware and Architecture
Parallel Computing and Optimization Techniques
Physical Sciences →  Computer Science →  Hardware and Architecture

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