Neural computing: theory and practice
Neural computing: theory and practice
Parallel simulated annealing algorithms
Journal of Parallel and Distributed Computing
The Problem of Schedule Construction in the Joint Design of Hardware and Software
Programming and Computing Software
DYANA: An Environment for Embedded System Design and Analysis
SS '99 Proceedings of the Thirty-Second Annual Simulation Symposium
On the acceleration of simulated annealing
On the acceleration of simulated annealing
Simulated annealing heuristic for flow shop scheduling problems with unrelated parallel machines
Computers and Operations Research
Simulated Annealing for Grid Scheduling Problem
JVA '06 Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing
Placement by Simulated Annealing on a Multiprocessor
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Algorithm for synthesis of real-time systems under reliability constraints
Journal of Computer and Systems Sciences International
Problems of instrumental support for the development of distributed embedded real-time systems
Programming and Computing Software
Model of distributed computing system operation with time
Programming and Computing Software
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A major requirement imposed on the operation of a real-time computing (RTC) system is that the deadlines for the operation of application programs must be met. The violation of an operational deadline leads to a failure of an RTC system. In this context, the problem arises of ensuring the required accuracy of estimating the execution time of application programs. An approach is developed for the design of iterative scheduling algorithms in which the execution times of application programs are estimated using simulation models with a different degree of detail, which ensures the required accuracy of estimating the execution time of programs. The approach can be used to design iterative algorithms of the following classes: genetic, evolutionary, simulated annealing, random-search, and locally optimal algorithms.