An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: Formal verification, simulation, and statistical testing

2017 ◽  
Vol 124 ◽  
pp. 1-21 ◽  
Author(s):  
Bahman Keshanchi ◽  
Alireza Souri ◽  
Nima Jafari Navimipour
Keyword(s):  
Genetic Algorithm ◽  
Formal Verification ◽  
Task Scheduling ◽  
Priority Queues ◽  
Statistical Testing ◽  
Improved Genetic Algorithm ◽  
Cloud Environments

An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments

2019 ◽  
Vol 32 (6) ◽  
pp. 1531-1541 ◽  
Author(s):  
Zhou Zhou ◽  
Fangmin Li ◽  
Huaxi Zhu ◽  
Houliang Xie ◽  
Jemal H. Abawajy ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Improved Genetic Algorithm ◽  
Scheduling Optimization ◽  
Greedy Strategy ◽  
Cloud Environments

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.

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.

An Improved Genetic Algorithm for Document Clustering on the Cloud

2018 ◽  
Vol 8 (4) ◽  
pp. 20-28
Author(s):  
Ruksana Akter ◽  
Yoojin Chung
Keyword(s):  
Genetic Algorithm ◽  
Virtual Machines ◽  
Document Clustering ◽  
Local Minima ◽  
Improved Genetic Algorithm ◽  
Crossover Point ◽  
Text Document ◽  
Modified Genetic Algorithm ◽  
Cloud Environments ◽  
Traditional Approaches

This article presents a modified genetic algorithm for text document clustering on the cloud. Traditional approaches of genetic algorithms in document clustering represents chromosomes based on cluster centroids, and does not divide cluster centroids during crossover operations. This limits the possibility of the algorithm to introduce different variations to the population, leading it to be trapped in local minima. In this approach, a crossover point may be selected even at a position inside a cluster centroid, which allows modifying some cluster centroids. This also guides the algorithm to get rid of the local minima, and find better solutions than the traditional approaches. Moreover, instead of running only one genetic algorithm as done in the traditional approaches, this article partitions the population and runs a genetic algorithm on each of them. This gives an opportunity to simultaneously run different parts of the algorithm on different virtual machines in cloud environments. Experimental results also demonstrate that the accuracy of the proposed approach is at least 4% higher than the other approaches.

Research on Parallel Test Task Scheduling Based on Improved Genetic Algorithm and Petri Net

2014 ◽  
Vol 543-547 ◽  
pp. 1119-1122
Author(s):  
Pei Pei Chen ◽  
Bao Mei Qiu ◽  
Hao Ba
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Task Scheduling ◽  
Petri Net ◽  
Simulated Annealing Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Test Task ◽  
Parallel Test

Parallel test task scheduling is always complex and difficult to optimize. Aiming at this problem, an improved Genetic Simulated Annealing Algorithm based on Petri net is posed to. At first, a Petri net model is established for the system, then the transition sequence is used as task scheduling sequence set path. Genetic Algorithm is introduced in order to get the optimal path. In the process of search, the sequence will be able to stimulate changes as chromosomes, selection, crossover and mutation. In order to prevent premature convergence of the algorithm appears, into the phenomenon of local optimal solution, the individual needs simulated annealing operation, and finally, we can get the shortest time to complete the test task scheduling sequence.

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