Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization
By Kun Li, Gaochao Xu, Guangyu Zhao, Yushuang Dong, Dan Wang. 2011 Sixth Annual Chinagrid Conference, 2011
459 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/1737177098539e295235678d664fcdb833568b94
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Zhi-hui Zhan
- Henry Shu-Hung Chung
- Jun Zhang
- Yun Li
- Yue-jiao Gong
- Sarbjeet Singh
- Mala Kalra
- Yaochu Jin
In Computer Networks and Communications
- Henry Shu-Hung Chung
- Zhi-hui Zhan
- Jun Zhang
- Xiao Fang Liu
- Sarbjeet Singh
- Mala Kalra
- Yue-jiao Gong
- Yun Li
In Information Systems
- Henry Shu-Hung Chung
- Zhi-hui Zhan
- Jun Zhang
- Xiao Fang Liu
- Sarbjeet Singh
- Mala Kalra
- Yue-jiao Gong
- Yun Li
Citations in the corpus
- A review of metaheuristic scheduling techniques in cloud computing (2015, background): “Li et al. [22] presented Load Balancing Ant Colony Optimization (LBACO) algorithm, for scheduling of independent tasks with the aim of minimizing makespan and even load across all virtual machines.”
- Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches (2015, background): “Li et al. [2011] proposed scheduling the cloud resource oriented by the objectives of minimizing the makespan of the tasks set (for the cloud users) and optimizing the load balance of the entire system (for the cloud provider).”
- Load balancing in cloud computing: A big picture (2018, methodology): “Normally, the load balancing in cloud computing with a multi-objective system is a well known NP-complete problem [10, 11].”