Energy-Aware Ant Colony Based Workload Placement in Clouds

By Eugen Feller, Louis Rilling, Christine Morin. IEEE/ACM International Conference on Grid Computing, 2011

360 citations recorded by the source corpus.

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A review of metaheuristic scheduling techniques in cloud computing (2015, methodology): “The algorithm reduces power consumption by 2.20%, 5.77%, 11.06% and 11.94% in comparison with [30], a greedy algorithm [35], FFDVolume [36] and modiﬁed FFD based on L1 norm mean estimator.”
Data Center Energy Consumption Modeling: A Survey (2016, methodology): “In recent times, Swarm-inspired techniques have been employed for reducing power consumption in data centers in [397][398][399].”
Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches (2015, methodology): “Feller et al. [2011] scheduled the VMs on PMs via the ACO approach to reduce the number of the physical resources.”
An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing (2018, methodology): “In cloud computing domain, Feller et al. [11] applied an ACO-based approach to minimize the number of cloud servers to support current load.”
A multi-objective ant colony system algorithm for virtual machine placement in cloud computing (2013, methodology): “[24] proposed a single-objective algorithm based on the MMAS metaheuristic to minimize the number of physical machines required to support the current load.”