Hopfield Neural Network, Mean Field Annealing, Neural Scheduler Algorithm, Recurrent Neural Network, Scheduling Algorithms


A literature review that analyzes four research papers that promote great solutions for specific scheduling problems.


Real time scheduling problems are present in every aspect of software development. An optimized real time scheduling scheme would determine the performance of an operating system. There are many different approaches that real time scheduling researchers developed to tackle scheduling problems in many computer systems that have great important roles in keeping our modern society running smoothly. Neural-network real time scheduling is one of those approaches that can solve many computer scheduling problems. As computing technology advanced, more and more real time scheduling problems arise that need new solutions to keep up with the demand of faster computer systems. In this literature review, we analyze four research papers that promote some great solutions for some particular scheduling problems. The first one is “A Neurodynamic Approach for Real Time Scheduling via Maximizing Piecewise Linear utility” by Zhishan Gou and Sanjoy K. Baruah (2016). The second paper is “Scheduling Multiprocessor Job with Resource and Timing Constraints Using Neural Networks” by Y. Huang and R. Chen (1999). The third paper is “Solving Real Time Scheduling Problems Using Hopfield-Types Neural Networks” by M. Silva, C. Cardeira, and Z. Mammeri (1997). Finally, the last one is “Neural Network for Multiprocessor Real Time Scheduling” by C. Cardiera and Z. Mammeri (1994).

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College of Engineering and Computer Science





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