Machine Learning and Neural Networks for Real-Time Scheduling

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 and Timing Constraints Using Neural Networks” . “Solving Real Time Scheduling Problems with Hopfield-type Neural Networks” and “Neural Networks for Multiprocessor Real-Time Scheduling

Analysis Literatures of Machine Learning and Neural Networks for Real Time Scheduling

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)

Machine Learning and Neural Networks for Real-Time Scheduling

Using neural networks to find optimal solutions to real-time scheduling is a common technique, and there have been many different models put forth to accomplish this goal. This paper is an academic literature review of six different designs put forth that use neural networks for real-time scheduling. A comparison is done for these models which weighs the feasibility and time complexity for each one as well as identifying common themes and trends in this topic

Stochastic optimal adaptive controller and communication protocol design for networked control systems

Networked Control System (NCS) is a recent topic of research wherein the feedback control loops are closed through a real-time communication network. Many design challenges surface in such systems due to network imperfections such as random delays, packet losses, quantization effects and so on. Since existing control techniques are unsuitable for such systems, in this dissertation, a suite of novel stochastic optimal adaptive design methodologies is undertaken for both linear and nonlinear NCS in presence of uncertain system dynamics and unknown network imperfections such as network-induced delays and packet losses. The design is introduced in five papers. In Paper 1, a stochastic optimal adaptive control design is developed for unknown linear NCS with uncertain system dynamics and unknown network imperfections. A value function is adjusted forward-in-time and online, and a novel update law is proposed for tuning value function estimator parameters. Additionally, by using estimated value function, optimal adaptive control law is derived based on adaptive dynamic programming technique. Subsequently, this design methodology is extended to solve stochastic optimal strategies of linear NCS zero-sum games in Paper 2. Since most systems are inherently nonlinear, a novel stochastic optimal adaptive control scheme is then developed in Paper 3 for nonlinear NCS with unknown network imperfections. On the other hand, in Paper 4, the network protocol behavior (e.g. TCP and UDP) are considered and optimal adaptive control design is revisited using output feedback for linear NCS. Finally, Paper 5 explores a co-design framework where both the controller and network scheduling protocol designs are addressed jointly so that proposed scheme can be implemented into next generation Cyber Physical Systems --Abstract, page iv

Scheduling real time security aware tasks in fog networks

Fog computing brings the cloud closer to a user with the help of a micro data center (mdc), leading to lower response times for delay sensitive applications. RT-SANE (Real-Time Security Aware scheduling on the Network Edge) supports batch and interactive applications, taking account of their deadline and security constraints. RT-SANE chooses between an mdc (in proximity to a user) and a cloud data center (cdc) by taking account of network delay and security tags. Jobs submitted by a user are tagged as: private, semi-private and public, and mdcs and cdcs are classified as: trusted, semi-trusted and untrusted. RT-SANE executes private jobs on a user's local mdcs or pre-trusted cdcs, and semi-private and public jobs on remote mdcs and cdcs. A security and performance-aware distributed orchestration architecture and protocol is made use of in RT-SANE. For evaluation, workload traces from the CERIT-SC Cloud system are used. The effect of slow executing straggler jobs on the Fog framework are also considered, involving migration of such jobs. Experiments reveal that RT-SANE offers a higher success ratio (successfully completed jobs) to comparable algorithms, including consideration of security tags

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

A Neurodynamic Approach for Real-Time Scheduling via Maximizing Piecewise Linear Utility

In this paper, we study a set of real-time scheduling problems whose objectives can be expressed as piecewise linear utility functions. This model has very wide applications in scheduling-related problems, such as mixed criticality, response time minimization, and tardiness analysis. Approximation schemes and matrix vectorization techniques are applied to transform scheduling problems into linear constraint optimization with a piecewise linear and concave objective; thus, a neural network-based optimization method can be adopted to solve such scheduling problems efficiently. This neural network model has a parallel structure, and can also be implemented on circuits, on which the converging time can be significantly limited to meet real-Time requirements. Examples are provided to illustrate how to solve the optimization problem and to form a schedule. An approximation ratio bound of 0.5 is further provided. Experimental studies on a large number of randomly generated sets suggest that our algorithm is optimal when the set is nonoverloaded, and outperforms existing typical scheduling strategies when there is overload. Moreover, the number of steps for finding an approximate solution remains at the same level when the size of the problem (number of jobs within a set) increases

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms