An Efficient Task Scheduling Technique in Heterogeneous Systems Using Self-Adaptive Selection-Based Genetic Algorithm

2006 ◽  
Author(s):  
R. Deepa ◽  
T. Srinivasan ◽  
D. Doreen ◽  
H. Miriam
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Heterogeneous Systems ◽  
Adaptive Selection ◽  
Scheduling Technique ◽  
Self Adaptive

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 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.

Multiphase fault tolerance genetic algorithm for vm and task scheduling in datacenter

2021 ◽  
Vol 58 (5) ◽  
pp. 102676
Author(s):  
Samira Kanwal ◽  
Zeshan Iqbal ◽  
Fadi Al-Turjman ◽  
Aun Irtaza ◽  
Muhammad Attique Khan
Keyword(s):  
Genetic Algorithm ◽  
Fault Tolerance ◽  
Task Scheduling ◽  
And Task
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