论文信息 - Multi-dimensional Constrained Cloud Computing Task Scheduling Mechanism Based on Genetic Algorithm

Multi-dimensional Constrained Cloud Computing Task Scheduling Mechanism Based on Genetic Algorithm

A new Cloud Computing task scheduling mechanism with multi-dimensional constrained based on Genetic Algorithm (GA) was proposed to reduce time to complete tasks and to improve users’ satisfaction by reducing expenses. The algorithm improves fitness function with comprehensive efficiency constituted of shortest completion time and minimum cost. Finally, simulation results showed that this algorithm is highly efficient and meet not only performance needs but also economic needs of users.

Youchan Zhu | Peng Liu | Youchan Zhu | Peng Liu

[1] Sun Jian. Optimal Model Redundant Task Scheduling for Cloud Computing , 2012 .

[2] Tian Ping-ping. Task Scheduling in Cloud Computing Based on Improved Immune Evolutionary Algorithm , 2012 .

[3] Wu Wu,et al. Scheduling Workflow in Cloud Computing Based on Hybrid Particle Swarm Algorithm , 2012 .

[4] Alexandru Iosup,et al. Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing , 2011, IEEE Transactions on Parallel and Distributed Systems.

[5] Rajkumar Buyya,et al. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[6] Li Hong-you. Demand elasticity algorithm for cloud computing based on ant colony optimization algorithm , 2011 .

[7] Lu Ke. Task Scheduling Algorithm Based on Euclidean Distance Liked-Load Balance in Cloud Computing Environment , 2012 .

[8] Wei Chen,et al. An Improved Genetic Algorithm and Its Application , 2012 .

[9] Rajkumar Buyya,et al. Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities , 2009, 2009 International Conference on High Performance Computing & Simulation.

[10] Jian Peng,et al. Task scheduling algorithm based on improved genetic algorithm in cloud computing environment , 2011 .