A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

By Yongqiang Gao, Haibing Guan, Zhengwei Qi, Yang Hou, Liang Liu. Journal of computer and system sciences (Print), 2013

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An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing (2018, methodology): “These algorithms include multiobjective ACS [35], multiobjective ACO (MACO) [36], and hybrid ACO with PSO, termed HACOPSO [37].”
A Survey on Mobile Edge Computing: The Communication Perspective (2017, background): “problems were also investigated in [161] and [162] for maximizing the energy savings of cloud servers and in [163] for different cloud scheduling strategies.”
Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks (2018, background): “VM placement over multiple clouds is studied in [15], [16], [17], where the goal is to reduce the deployment cost, maximize energy saving and improve user experience, given constraints on hardware configuration and load balancing.”
Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches (2015, methodology): “Similar work to schedule the VMs on PMs via the ACO approach was also conducted by Gao et al. [2013].”
Cloud task scheduling based on ant colony optimization (2013, methodology): “An optimized algorithm for virtual machine placement in cloud computing scheduling based on A multi-objective ant colony system algorithm in cloud computing is proposed in [2].”