Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers

By Anton Beloglazov, Rajkumar Buyya. Middleware for Grid Computing, 2010

471 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Elasticity in Cloud Computing: State of the Art and Research Challenges (2018, methodology): “[58], [60], [100], [101] use the adaptive utilization thresholds technique.”
Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers (2012, background): “This paper is a substantially extended version of the paper published in MGC 2010 [43].”
Energy-Aware Ant Colony Based Workload Placement in Clouds (2011, result): “Similar work can be found in [2], where the authors present simulation-driven results for a workload placement algorithm based on a modi ed version of the BFD algorithm and report substantial energy saving.”
A survey on virtual machine migration and server consolidation frameworks for cloud data centers (2015, methodology): “Dynamic resource assignment policy attributes (Beloglazov and Buyya, 2010c; Miet al., 2010; Wood et al., 2009) follow “resource on demand” mode to assign resources to the hosted VMs.”
A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments (2014, methodology): “[67] or the adaptive thresholds introduced by Beloglazov and Buyya [44], and it could be interesting to improve this technique.”