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Scheduling of tasks in multiprocessor system using hybrid genetic algorithms
This paper presents an investigation into the optimal scheduling of realtime
tasks of a multiprocessor system using hybrid genetic algorithms (GAs). A comparative
study of heuristic approaches such as `Earliest Deadline First (EDF)¿ and
`Shortest Computation Time First (SCTF)¿ and genetic algorithm is explored and
demonstrated. The results of the simulation study using MATLAB is presented and
discussed. Finally, conclusions are drawn from the results obtained that genetic algorithm
can be used for scheduling of real-time tasks to meet deadlines, in turn to obtain
high processor utilization
Scheduling in multiprocessor system using genetic algorithms
Multiprocessors have emerged as a powerful computing means for running real-time applications, especially where a uniprocessor system would not be sufficient enough to execute all the tasks. The high performance and reliability of multiprocessors have made them a powerful computing resource. Such computing environment requires an efficient algorithm to determine when and on which processor a given task should execute. This paper investigates dynamic scheduling of real-time tasks in a multiprocessor system to obtain a feasible solution using genetic algorithms combined with well-known heuristics, such as 'Earliest Deadline First' and 'Shortest Computation Time First'. A comparative study of the results obtained from simulations shows that genetic algorithm can be used to schedule tasks to meet deadlines, in turn to obtain high processor utilization.Peer ReviewedPostprint (published version
Exploring Task Mappings on Heterogeneous MPSoCs using a Bias-Elitist Genetic Algorithm
Exploration of task mappings plays a crucial role in achieving high
performance in heterogeneous multi-processor system-on-chip (MPSoC) platforms.
The problem of optimally mapping a set of tasks onto a set of given
heterogeneous processors for maximal throughput has been known, in general, to
be NP-complete. The problem is further exacerbated when multiple applications
(i.e., bigger task sets) and the communication between tasks are also
considered. Previous research has shown that Genetic Algorithms (GA) typically
are a good choice to solve this problem when the solution space is relatively
small. However, when the size of the problem space increases, classic genetic
algorithms still suffer from the problem of long evolution times. To address
this problem, this paper proposes a novel bias-elitist genetic algorithm that
is guided by domain-specific heuristics to speed up the evolution process.
Experimental results reveal that our proposed algorithm is able to handle large
scale task mapping problems and produces high-quality mapping solutions in only
a short time period.Comment: 9 pages, 11 figures, uses algorithm2e.st
Hybrid Genetic Algorithms for Scheduling High-Speed Multimedia Systems
It has been observed that most conventional operating systems could not cope with the scheduling of multimedia tasks owing to the large size of these files. For instance, processing of multimedia tasks using the traditional operating systems are fraught with problems such as low quality of service and delay jitters. In order to address these problems, a scheduling algorithm christened hybrid genetic algorithm for multimedia task scheduling (HGAMTS) was developed. It employed heuristic knowledge of the problem domain to model a hybrid genetic algorithm in a multiprocessor environment. The system is made up of the scheduler model and the task model. The scheduler model consist a centralized dynamic scheduling scheme. In this scheme, all tasks arrive at a central processor (scheduler). The model has a minimum of five and maximum of ten processors. Attached to each processor is a dispatch queue
An Enhanced Model for Job Sequencing and Dispatch in Identical Parallel Machines
This paper has developed an efficient scheduling model that is robust and minimizes the total completion time for job completion in identical parallel machines. The new model employs Genetic-Fuzzy technique for job sequencing and dispatch in identical parallel machines. It uses genetic algorithm technique to develop a job scheduler that does the job sequencing and optimization while fuzzy logic technique was used to develop a job dispatcher that dispatches job to the identical parallel machines. The methodology used for the design is the Object Oriented Analysis and Design Methodology (OOADM) and the system was implemented using C# and .NET framework. The model was tested with fifteen identical parallel machines used for printing. The parameters used in analyzing this model include the job scheduling length, average execution time, load balancing and machines utilization. The result generated from the developed model was compare with the result of other job scheduling models like First Come First Sever (FCFS) scheduling approach and Genetic Model (GA) scheduling approach. The result of the new model shows a better load balancing and high machine utilization among the individual machines when compared with the First Come First Serve (FCFS) scheduling model and Genetic Algorithm (GA) scheduling model. Keywords: Parallel Machines, Genetic Model, Job Scheduler, Fuzzy Logic Technique, Load Balancing, Machines   Utilization DOI: 10.7176/CEIS/11-2-05 Publication date: March 31st 202
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