Entropy: a consolidation manager for clusters

By Fabien Hermenier, Xavier Lorca, Jean-Marc Menaud, Gilles Muller, Julia L. Lawall. International Conference on Virtual Execution Environments, 2009

602 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture, Information Systems

View Paper: https://www.semanticscholar.org/paper/7c6174999a92e68284784f3e483fa73d622a01e9

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In Computer Networks and Communications

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In Information Systems

Citations in the corpus

Classification framework for graphene-based materials. (2014, methodology): “We develop pioneer multi-probes magneto-transport experiments under a 35 T pulsed field which reveal an unexpected oscillatory behavior of RðHÞ inconsistent with existing theories.”
Live virtual machine migration with adaptive, memory compression (2009, background): “Live migration of virtual machines provides a flexible way to implement power-budget, power reservation and power-saving [5][6][7].”
Optimization of Resource Provisioning Cost in Cloud Computing (2012, background): “Therefore, purchasing this on-demand plan, the consumers can dynamically provision resources at the moment when the resources are needed to fit the fluctuated and unpredictable demands.”
A multi-objective ant colony system algorithm for virtual machine placement in cloud computing (2013, background): “In [18], the authors proposed the Entropy resource manager for homogeneous clusters, which performs dynamic consolidation based on constraint programming and takes into account both the problem of allocating the VMs to the available nodes and the problem of how to migrate the VMs to these nodes.”
Optimal virtual machine placement across multiple cloud providers (2009, background): “In [15], a dynamic consolidation mechanism based on constraint programming was developed.”
CloudScale: elastic resource scaling for multi-tenant cloud systems (2011, background): “Entropy [27] is a resource manager for homogeneous clusters, which performs dynamic consolidation based on constraint programming and takes migration overhead into account.”
Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments (2010, background): “The VM placing/replacement problems are formulated as constraint satisfaction problems with the objective of minimizing the number of used servers and migration costs in [14].”