scholarly journals A Genetic Algorithm for Task Scheduling on NoC Using FDH Cross Efficiency

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Song Chai ◽  
Yubai Li ◽  
Jian Wang ◽  
Chang Wu
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Relative Advantage ◽  
Scheduling Problem ◽  
Data Routing ◽  
Ga Algorithm ◽  
Simulation Results ◽  
Energy Average ◽  
Efficient Schedule

A CrosFDH-GA algorithm is proposed for the task scheduling problem on the NoC-based MPSoC regarding the multicriterion optimization. First of all, four common criterions, namely, makespan, data routing energy, average link load, and workload balance, are extracted from the task scheduling problem on NoC and are used to construct the DEA DMU model. Then the FDH analysis is applied to the problem, and a FDH cross efficiency formulation is derived for evaluating the relative advantage among schedule solutions. Finally, we introduce the DEA approach to the genetic algorithm and propose a CrosFDH-GA scheduling algorithm to find the most efficient schedule solution for a given scheduling problem. The simulation results show that our FDH cross efficiency formulation effectively evaluates the performance of schedule solutions. By conducting comparative simulations, our CrosFDH-GA proposal produces more metrics-balanced schedule solution than other multicriterion algorithms.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals AN EFFICIENT TASK SCHEDULING IN DISTRIBUTED COMPUTING SYSTEMS BY IMPROVED GENETIC ALGORITHM

2013 ◽  
pp. 52-58
Author(s):  
K. SUNITHA ◽  
MRS. P V SUDHA
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Heterogeneous Systems ◽  
Simultaneous Optimization ◽  
Multiprocessor System ◽  
Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Distributed Computing Systems

Task Scheduling problem for heterogeneous systems is concerned with arranging the various tasks to be executed on various processors of a system so that computing resources are utilized most effectively. Parallel processing refers to the concept of speeding-up the execution of a task by dividing the task into multiple fragments that can execute simultaneously, each on its own processor i.e. it is the simultaneous processing of the task on two or more processors in order to obtain faster results. It can be effectively used for tasks that involve a large number of calculations, have time constraints and can be divided into a number of smaller tasks. The scheduling problem deals with the optimal assignment of a set of tasks onto parallel multiprocessor system and orders their execution so that the total completion time is minimized. An Optimal scheduling of parallel tasks with some precedence relationship, onto a parallel machine is known to be NP-complete. This precedence relationship among tasks can be represented as Directed Acyclic Graph (DAG). In this paper, a scheduling algorithm has been proposed to schedule DAG tasks on Heterogeneous processor which uses Genetic algorithm to get optimal schedule. The scheduling problem is also considered. This study includes a search for an optimal mapping of the task and their sequence of execution and also search for an optimal configuration of the parallel system. An approach for the simultaneous optimization of all these three components of scheduling method using genetic algorithm is presented and its performance is evaluated in comparison with the Min-Min and Max-Min scheduling methods.

Task Scheduling in Heterogeneous Computing Systems Based on Machine Learning Approach

2020 ◽  
Vol 34 (12) ◽  
pp. 2051012
Author(s):  
Hui Xie ◽  
Li Wei ◽  
Dong Liu ◽  
Luda Wang
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Scheduling Problem ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Machine Learning Approach ◽  
Heterogeneous Computing Systems ◽  
Regression Theory

Task scheduling problem of heterogeneous computing system (HCS), which with increasing popularity, nowadays has become a research hotspot in this domain. The task scheduling problem of HCS, which can be described essentially as assigning tasks to the proper processor for executing, has been shown to be NP-complete. However, the existing scheduling algorithm suffers from an inherent limitation of lacking global view. Here, we reported a novel task scheduling algorithm based on Multi-Logistic Regression theory (called MLRS) in heterogeneous computing environment. First, we collected the best scheduling plans as the historical training set, and then a scheduling model was established by which we could predict the following schedule action. Through the analysis of experimental results, it is interpreted that the proposed algorithm has better optimization effect and robustness.

Based on Improved Genetic Algorithm for Task Scheduling of Heterogeneous Multi-Core Processor

2014 ◽  
Vol 1030-1032 ◽  
pp. 1671-1675
Author(s):  
Yue Qiu ◽  
Jing Feng Zang
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Experimental Results ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
High Quality ◽  
Multi Core Processor ◽  
Better Than

This paper puts forward an improved genetic scheduling algorithm in order to improve the execution efficiency of task scheduling of the heterogeneous multi-core processor system and give full play to its performance. The attribute values and the high value of tasks were introduced to structure the initial population, randomly selected a method with the 50% probability to sort for task of individuals of the population, thus to get high quality initial population and ensured the diversity of the population. The experimental results have shown that the performance of the improved algorithm was better than that of the traditional genetic algorithm and the HEFT algorithm. The execution time of tasks was reduced.

scholarly journals Fixed Task Scheduling of Industrial Robot using Genetic Algorithm Based Travelling Salesman Problem

2020 ◽  
Vol 9 (5) ◽  
pp. 1771-1775
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Shortest Path ◽  
Completion Time ◽  
Travelling Salesman Problem ◽  
Industrial Robots ◽  
Scheduling Problem ◽  
Search Technique ◽  
Travelling Salesman ◽  
Algorithm Search

The task scheduling of any industrial robots is a prior requirement to effectively use the capability by obtaining shortest path with optimum completion time. In this article, we have presented Travelling Salesman Problem (TSP) with Genetic Algorithm (GA) search technique based task scheduling technique for obtaining optimum shortest path of the task.TSP finds an optimal solution to search for the shortest route by considering every location for completing the required tasks by setting up GA. This article embrace the adaption and implementation of the Genetic Algorithm search strategy for the task scheduling problem in the cooperative control of multiple resources for getting shortest path with minimize the completion time for two zone specific task allocation problem. It can be inferred from the simulation results that the Genetic Algorithm search technique can be considered as a viable solution for the task scheduling problem.

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