A Practical Schedulability Analysis for Generalized Sporadic Tasks in Distributed Real-Time Systems

Existing off-line schedulability analysis for real-time systems can only handle periodic or sporadic tasks with known minimum inter-arrival times. Modeling sporadic tasks with fixed minimum inter-arrival times is a poor approximation for systems in which tasks arrive in bursts, but have longer intervals between the bursts. In such cases, schedulability analysis based on the existing sporadic task model is pessimistic and seriously overestimates the task\u27s time demand. In this paper, we propose a generalized sporadic task model that characterizes arrival times more precisely than the traditional sporadic task model, and we develop a corresponding schedulability analysis that computes tighter bounds on worst-case response times. Experimental results show that when arrival time jitter increases, the new analysis more effectively guarantees schedulability of sporadic tasks

Reinforcement Learning with Frontier-Based Exploration via Autonomous Environment

Active Simultaneous Localisation and Mapping (SLAM) is a critical problem in autonomous robotics, enabling robots to navigate to new regions while building an accurate model of their surroundings. Visual SLAM is a popular technique that uses virtual elements to enhance the experience. However, existing frontier-based exploration strategies can lead to a non-optimal path in scenarios where there are multiple frontiers with similar distance. This issue can impact the efficiency and accuracy of Visual SLAM, which is crucial for a wide range of robotic applications, such as search and rescue, exploration, and mapping. To address this issue, this research combines both an existing Visual-Graph SLAM known as ExploreORB with reinforcement learning. The proposed algorithm allows the robot to learn and optimize exploration routes through a reward-based system to create an accurate map of the environment with proper frontier selection. Frontier-based exploration is used to detect unexplored areas, while reinforcement learning optimizes the robot's movement by assigning rewards for optimal frontier points. Graph SLAM is then used to integrate the robot's sensory data and build an accurate map of the environment. The proposed algorithm aims to improve the efficiency and accuracy of ExploreORB by optimizing the exploration process of frontiers to build a more accurate map. To evaluate the effectiveness of the proposed approach, experiments will be conducted in various virtual environments using Gazebo, a robot simulation software. Results of these experiments will be compared with existing methods to demonstrate the potential of the proposed approach as an optimal solution for SLAM in autonomous robotics.Comment: 23 pages, Journa

A comparative study of the realization of rate-based computing services in general purpose operating systems

Abstract: Scheduling architectures that support a rate abstraction are becoming increasingly popular for realizing real-time services in general-purpose operating systems. While many rate-based schemes have been proposed, there has been little discussion of the relative merits of each approach. We study the performance of a set of multimedia applications under three different rate-based scheduling schemes implemented in the FreeBSD operating system: a proportional share scheme (Earliest Eligible Virtual Deadline First scheduling), a polling, server-based scheme (the Constant Bandwidth Server), and a rate-based extension to the original Liu and Layland task model (Rate-Based Execution). Furthermore, we consider three specific scheduling problems: scheduling application level tasks, scheduling system calls, and scheduling the kernel-level processing of data input from devices such as network interfaces. Based on empirical evidence, we conclude that “one size does not fit all ” — that no one rate-based resource allocation scheme suffices for all scheduling problems along the data path from the device to an application. Rather, we achieve the best performance for our multimedia workload when we apply different ratebased scheduling policies at different layers of the operating system such as proportional share scheduling of system calls and application tasks, and rate-based Liu and Layland scheduling of device processing. * 1