Measuring the influence of real-time operating systems on performance and determinism

Mächtel, M.; Rzehak, Helmut

doi:10.1016/0967-0661(96)00157-8

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…A distinct rapid time scale is taken into account for the ignored high frequency phenomena in solitary perturbations. A change in the dynamical order of a system with differential conditions is treated as a parameter perturbation to achieve this effect [8].…”

Section: Nonlinear System and Design Analysismentioning

confidence: 99%

“…It is difficult to examine and configure these behaviours due to their inherent interconnectedness, variety, and mix. Cyber physical system analysis and design should be possible with the use of simulation or design tools that can simulate complicated or hybrid behaviours with cyber components, as well as scenarios with disturbances and other external phenomena like time delays [8]. Many branches of engineering, including mechatronics, embedded systems, and control systems, have recently contributed to cyber physical system (CPS) research.…”

Section: Cyber Physical Systemmentioning

confidence: 99%

“…In terms of the nature of communication on the internet, this assault is neither cracked nor hacked. The only goal of denial-of-service attacks is to disrupt the victim's services [8].…”

Section: Denial Of Service Attackmentioning

confidence: 99%

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Modeling Nonlinear Physical Systems in a Real-Time Operating System Environment for Control and Cyber Threat Mitigation

Prarthana A. Deshkar,

2024

jes

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Nonlinearity control and interconnection between physical systems are essential for many contemporary systems. Cyber Physical systems came about because of the growing number of security concerns with network control systems. Nonlinear physical systems are the primary subject of this study's investigation into control and cyber threat mitigation. Nonlinear physical systems are the focus of this study, which also delves into their control, modelling, and real-time implementation. Here, physical systems are defined as two kinds of benchmark nonlinear robotic systems: the Segway transporter and the cruise control system. Many see the Segway as an unstable and nonlinear physical system. By factoring in kinetic and potential energy, Lagrangian dynamics has allowed for the derivation of equations of motion. Next, a state-space model of the system is obtained by linearizing the nonlinear differential equations using Taylor series expansion of functions. This model is then transformed into two transfer functions, one for the tilt angle and one for the yaw angle. The Model Predictive Controller (MPC), GA Tuned PID, and PID are the building blocks of MATLAB's effective simulation study of nonlinear control.

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Section: Nonlinear System and Design Analysismentioning

confidence: 99%

Section: Cyber Physical Systemmentioning

confidence: 99%

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Modeling Nonlinear Physical Systems in a Real-Time Operating System Environment for Control and Cyber Threat Mitigation

Prarthana A. Deshkar,

2024

jes

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“…In our experiments, we first investigated the performance of our scheme over fluctuating workload patterns. The objective in studying different patterns is to assess the sensitivity of feedback DVS to different types of execution time fluctuations, which have been observed in interrupt-driven systems (Mächtel and Rzehak, 1996). Since it is not practical to examine every possible type of fluctuation, we constructed three synthesized execution time patterns based on our observation of some typical real-time applications, as shown in Figure 11.…”

Section: Methodsmentioning

confidence: 99%

Feedback EDF Scheduling of Real-Time Tasks Exploiting Dynamic Voltage Scaling

Zhu

Mueller

2005

Real-Time Syst

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Abstract.Many embedded systems are constrained by limits on power consumption, which are reflected in the design and implementation for conserving their energy utilization. Dynamic voltage scaling (DVS) has become a promising method for embedded systems to exploit multiple voltage and frequency levels and to prolong their battery life. However, pure DVS techniques do not perform well for systems with dynamic workloads where the job execution times vary significantly. In this paper, we present a novel approach combining feedback control with DVS schemes targeting hard real-time systems with dynamic workloads. Our method relies strictly on operating system support by integrating a DVS scheduler and a feedback controller within the earliest-deadline-first (EDF) scheduling algorithm. Each task is divided into two portions. The objective within the first portion is to exploit frequency scaling for the average execution time. Static and dynamic slack is accumulated for each task with slackpassing and preemption handling schemes. The objective within the second portion is to meet the hard real-time deadline requirements up to the worst-case execution time following a lastchance approach. Feedback control techniques make the system capable of selecting the right frequency and voltage settings for the first portion, as well as guaranteeing hard real-time requirements for the overall task. A feedback control model is given to describe our feedback DVS scheduler, which is used to analyze the system's stability. Simulation experiments demonstrate the ability of our algorithm to save up to 29% more energy than previous work for task sets with different dynamic workload characteristics.

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“…In our experiments, we first investigated the performance of our scheme over fluctuating workload patterns. The objective in studying different patterns is to assess the sensitivity of feedback DVS to different types of fluctuations, which have been observed in interrupt-driven systems [15]. As shown in Figure 8, we constructed three synthesized execution time patterns to simulate realistic workloads.…”

Section: Methodsmentioning

confidence: 99%