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A Review On Mixed Criticality Methods, Alex Jenkel
A Review On Mixed Criticality Methods, Alex Jenkel
Recent Advances in Real-Time Systems
Within the study of mixed criticality scheduling, there are many different aspects that must be considered—resources, processor speeds, number of processors, etc.—that make scheduling theories difficult to produce. Two papers address specific aspects of mixed criticality scheduling, and this paper compares the two different methods and also builds upon them.
Machine Learning And Neural Networks For Real-Time Scheduling, Daniel Hureira, Christian Vartanian
Machine Learning And Neural Networks For Real-Time Scheduling, Daniel Hureira, Christian Vartanian
Recent Advances in Real-Time Systems
This paper aims to serve as an efficient survey of the processes, problems, and methodologies surrounding the use of Neural Networks, specifically Hopfield-Type, in order to solve Hard-Real-Time Scheduling problems. Our primary goal is to demystify the field of Neural Networks research and properly describe the methods in which Real-Time scheduling problems may be approached when using neural networks. Furthermore, to give an introduction of sorts on this niche topic in a niche field. This survey is derived from four main papers, namely: “A Neurodynamic Approach for Real-Time Scheduling via Maximizing Piecewise Linear Utility” and “Scheduling Multiprocessor Job with Resource …