@Article{2019.100000115,
AUTHOR = {Tongmao Ma, Shanchen Pang, Weiguang Zhang, Shaohua Hao},
TITLE = {Virtual Machine Based on Genetic Algorithm Used in Time and Power Oriented Cloud Computing Task Scheduling},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {3},
PAGES = {605--613},
URL = {http://www.techscience.com/iasc/v25n3/39689},
ISSN = {2326-005X},
ABSTRACT = {In cloud computing, task scheduling is a challenging problem in cloud data
center, and there are many different kinds of task scheduling strategies. A good
scheduling strategy can bring good effectiveness, where plenty of parameters
should be regulated to achieve acceptable performance of cloud computing
platform. In this work, combined elitist strategy, three parameters values
oriented genetic algorithms are proposed. Specifically, a model built by
Generalized Stochastic Petri Nets (GSPN) is introduced to describe the process
of scheduling in cloud datacenter, and then the workflow of the algorithms is
showed. After that, the effectiveness of the algorithms is found to be valid by
the simulations on CloudSim.},
DOI = {10.31209/2019.100000115}
}