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Edge-Internet of Things (Edge IoT) systems are increasingly integral to applications that require real time signal processing, particularly where low latency and energy efficiency are critical. This paper explores the design and performance evaluation of a heterogeneous microprocessor architecture aimed at optimizing energy consumption and real time performance. The heterogeneous architecture integrates multiple types of cores, such as Central Processing Units (CPUs), Digital Signal Processors (DSPs), and Graphics Processing Units (GPUs), to allocate tasks based on computational demand. The proposed design significantly reduces energy consumption, particularly during high-performance tasks, while maintaining real time processing guarantees. Simulation-based performance evaluation was conducted to assess the energy efficiency, latency, and overall system performance under varying workloads, including real time Digital Signal Processing (DSP) benchmarks. The results showed that the heterogeneous architecture outperformed traditional homogeneous processors, demonstrating up to a 19-fold improvement in energy efficiency. Furthermore, the system reduced latency by up to 45% in real time applications, making it particularly suitable for Edge IoT environments such as industrial automation and smart healthcare, where both performance and energy efficiency are critical. Despite some trade-offs in task scheduling complexity, the heterogeneous design was able to balance power consumption and computational performance effectively. The findings suggest that this architecture can serve as a foundation for future Edge IoT systems, providing significant advantages in terms of energy efficiency, real time processing, and scalability. Future work will focus on further optimization of the architecture and exploring its application across various IoT environments.

CPU scheduling is a core function in modern operating systems that significantly impacts system performance and efficiency. Among various scheduling algorithms, Priority Scheduling is widely used and exists in two main variants: non-preemptive and preemptive. The non-preemptive mode allows a process to run to completion, while the preemptive mode can interrupt a running process for a higher-priority one. Understanding the behavioral differences between these modes is crucial but often challenging through manual calculations. To address this, an interactive web-based application was developed to simulate and visualize both preemptive and non-preemptive Priority Scheduling algorithms. The research method involved designing the system logic based on the core principles of each scheduling variant, followed by implementation using standard web technologies: HTML, Tailwind CSS, and JavaScript. The application allows users to input custom process data or load predefined case studies, select the scheduling mode, and instantly receive a comprehensive analysis. The results include a dynamically generated Gantt chart, a detailed performance metrics table (including turnaround time and waiting time), and a step-by-step execution log. Through a comparative analysis of a specific case study, the application is proven to be an effective educational tool. It accurately simulates both modes and visually demonstrates the impact of preemption on execution order, resource utilization, and key performance metrics, thereby simplifying the learning process for students and educators.

This study aims to analyze process management in the Linux operating system, focusing on how processes are managed from creation, scheduling, to termination. Linux utilizes various scheduling and memory management mechanisms to ensure system efficiency and stability. One of the schedulers used is the Completely Fair Scheduler (CFS), which provides a fair distribution of CPU time based on the priority and needs of the processes. This research also discusses the use of techniques such as paging for memory management and how Linux handles inter-process communication through pipes and shared memory. Experiments were conducted to measure CPU usage, memory consumption, and execution time by various processes with different priorities. The results show that Linux effectively manages resources, provides fair time-sharing, and maintains security by isolating the address space between the kernel and user-space. This study is expected to contribute to the further development of process management in Linux and provide insights for developers and system administrators to optimize operating system performance.

In today's digital era, operating systems play a crucial role in various technological devices. This paper discusses the optimization and modification of Debian, one of the most stable Linux distributions, to enhance its performance as a real-time operating system (RTOS). The primary objectives of this research are to improve the responsiveness, reliability, and stability of Debian in handling real-time tasks. The study involves modifying the kernel, adjusting scheduling settings, optimizing memory management, and integrating specific real-time software. The results of the tests show that the modified Debian provides lower and more stable task execution latency compared to the standard Debian. Tests were conducted using the cyclictest software to measure system latency and Htop to monitor CPU and memory performance. Additionally, the use of the VLC application as a real-world workload demonstrated that the optimized operating system could handle task priorities more efficiently, allocate resources as needed for real-time demands, and maintain stability under heavy workloads. This research significantly contributes to the development of Debian-based RTOS, which can be applied in fields such as industrial automation, robotics, and IoT devices. The optimizations ensure that the operating system can meet the high-performance and real-time reliability demands of these critical applications.    

This research aims to analyze the performance of various CPU scheduling algorithms in increasing the responsiveness of the Android operating system. Using algorithms such as First Come First Served (FCFS), Shortest Job First (SJF), Shortest Remaining Time First (SRTF), and Round Robin, this study evaluates the advantages and disadvantages of each in terms of average waiting time, throughput, and power consumption. Research methods include literature study, algorithm selection and implementation, as well as data testing and analysis. The research results show that choosing the right scheduling algorithm can increase system efficiency and responsiveness, as well as provide recommendations for Android operating system developers and the application developer community. It is hoped that these findings will help in determining the optimal solution for improving the performance of the Android operating system.

Process scheduling algorithms play an important role in determining the performance of operating systems, particularly in managing CPU utilization and process execution efficiency. Ineffective scheduling mechanisms can lead to increased waiting time and reduced system performance. This study aims to model and simulate several process scheduling algorithms using MATLAB in order to evaluate their performance characteristics. The research method involves developing simulation models for selected scheduling algorithms, including First Come First Serve, Shortest Job First, and Round Robin. Performance evaluation is conducted based on key parameters such as average waiting time, turnaround time, and processor throughput. The simulation results demonstrate that each scheduling algorithm exhibits different performance behaviors depending on the workload characteristics. Shortest Job First produces the lowest average waiting time, while Round Robin offers better response time fairness for time-sharing systems. The findings indicate that simulation-based analysis using MATLAB provides an effective approach to understanding the behavior and performance trade-offs of process scheduling algorithms. This research contributes as a reference for selecting appropriate scheduling algorithms in operating system design and as a learning model for process scheduling analysis.