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"Multiprocessors."
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Optimizing multiprocessor performance in real-time systems using an innovative genetic algorithm approach
Due to its enormous influence on system functionality, researchers are presently looking into the issue of task scheduling on multiprocessors. Establishing the most advantageous schedules is often regarded as a difficult-to-compute issue. Genetic Algorithm is a recent tool employed by researchers to optimize scheduling tasks and boost performance, although this field of research is yet mostly unexplored. In this article, a novel approach for generating task schedules for real-time systems utilizing a Genetic Algorithm is proposed. The approach seeks to design task schedules for multiprocessor systems with optimal or suboptimal lengths, with the ultimate goal of achieving high performance. This research project focuses on non-preemptive independent tasks in a multiprocessor environment. All processors are assumed to be identical. We conducted a thorough analysis of the proposed approach and pitted it against three frequently utilized scheduling methodologies: the “Evolutionary Fuzzy Based Scheduling Algorithm”, the “Least Laxity First Algorithm”, and the “Earliest Deadline First Algorithm”. The Proposed Algorithm demonstrated superior efficiency and reliability compared to Earliest Deadline First, Least Laxity First, and Evolutionary Fuzzy-based Scheduling Algorithm. It consistently achieved zero missed deadlines and the lowest average response and turnaround times across all scenarios, maintaining optimal performance even under high load conditions.
Journal Article
A boundle of on-line algorithms for scheduling computational tasks
We deal with the problem of scheduling the set of computational tasks on parallel identical processors. Each task needs a predefined number of processors to perform. The problem is known in scheduling theory and has been considered up to now by a few authors. Starting from the formal original description of the problem, we provide a mathematical model and then propose, at first, the solution method in the deterministic case. In fact, the paper focuses chiefly on the nondeterministic variant of the problem. We have proposed several online algorithms for this case. These algorithms are evaluated through competitive analysis and experiments. The practical application of the problem can be found in embedded systems with increased dependability obtained through hardware and software redundancy.
Journal Article
Mixed-criticality scheduling on multiprocessors
The scheduling of mixed-criticality implicit-deadline sporadic task systems on identical multiprocessor platforms is considered. Two approaches, one for global and another for partitioned scheduling, are described. Theoretical analyses and simulation experiments are used to compare the global and partitioned scheduling approaches.
Journal Article
Neural network world :international journal on neural and mass-parallel computing and information systems
Mezinárodní časopis o problematice neuronových a paralelních výpočetních a informačních systémů
Journal
Diagnosability of Lexicographic Product of Wheels and Paths under the PMC Model
Diagnosability is a critical metric for evaluating the fault diagnosis capabilities of interconnection networks in multiprocessor systems. Accurate assessment of diagnosability requires system-level fault diagnosis models, which play a key role in the design of new interconnection networks. In this paper, we introduce a novel network, denoted as Pm ∘ Wn, which represents the lexicographic product of a wheel and a path. Under the PMC model, we prove that the diagnosability of Pm ∘ Wn is 3 + n and its h-edge tolerable diagnosability is 3 + n - h for 0 ≤ h < 3+n, m ≥ 4, and n ≥ 7. These results reveal that Pm ∘ Wn exhibits strong fault diagnosis capabilities. Furthermore, the lexicographic product offers a promising approach to designing interconnection network architectures for large-scale multiprocessor systems.
Journal Article
Developing a Platform Using Petri Nets and GPenSIM for Simulation of Multiprocessor Scheduling Algorithms
Efficient multiprocessor scheduling is pivotal in optimizing the performance of parallel computing systems. This paper leverages the power of Petri nets and the tool GPenSIM to model and simulate a variety of multiprocessor scheduling algorithms (the basic algorithms such as first come first serve, shortest job first, and round robin, and more sophisticated schedulers like multi-level feedback queue and Linux’s completely fair scheduler). This paper presents the evaluation of three crucial performance metrics in multiprocessor scheduling (such as turnaround time, response time, and throughput) under various scheduling algorithms. However, the primary focus of the paper is to develop a robust simulation platform consisting of Petri Modules to facilitate the dynamic representation of concurrent processes, enabling us to explore the real-time interactions and dependencies in a multiprocessor environment; more advanced and newer schedulers can be tested with the simulation platform presented in this paper.
Journal Article
Enhanced Harmonic Partitioned Scheduling of Periodic Real-Time Tasks Based on Slack Analysis
The adoption of multiprocessor platforms is growing commonplace in Internet of Things (IoT) applications to handle large volumes of sensor data while maintaining real-time performance at a reasonable cost and with low power consumption. Partitioned scheduling is a competitive approach to ensure the temporal constraints of real-time sensor data processing tasks on multiprocessor platforms. However, the problem of partitioning real-time sensor data processing tasks to individual processors is strongly NP-hard, making it crucial to develop efficient partitioning heuristics to achieve high real-time performance. This paper presents an enhanced harmonic partitioned multiprocessor scheduling method for periodic real-time sensor data processing tasks to improve system utilization over the state of the art. Specifically, we introduce a general harmonic index to effectively quantify the harmonicity of a periodic real-time task set. This index is derived by analyzing the variance between the worst-case slack time and the best-case slack time for the lowest-priority task in the task set. Leveraging this harmonic index, we propose two efficient partitioned scheduling methods to optimize the system utilization via strategically allocating the workload among processors by leveraging the task harmonic relationship. Experiments with randomly synthesized task sets demonstrate that our methods significantly surpass existing approaches in terms of schedulability.
Journal Article