Kaleidoscope: cloud micro-elasticity via VM state coloring

  • Authors:

  • Roy Bryant;Alexey Tumanov;Olga Irzak;Adin Scannell;Kaustubh Joshi;Matti Hiltunen;Andres Lagar-Cavilla;Eyal de Lara

  • Affiliations:

  • University of Toronto, Toronto, ON, Canada;University of Toronto, Toronto, ON, Canada;University of Toronto, Toronto, ON, Canada;University of Toronto, Toronto, ON, Canada;AT&T Labs Research, Florham Park, NJ, USA;AT&T Labs Research, Florham Park, NJ, USA;AT&T Labs Research, Florham Park, NJ, USA;University of Toronto, Toronto, ON, Canada

  • Venue:
  • Proceedings of the sixth conference on Computer systems
  • Year:
  • 2011

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Abstract

We introduce cloud micro-elasticity, a new model for cloud Virtual Machine (VM) allocation and management. Current cloud users over-provision long-lived VMs with large memory footprints to better absorb load spikes, and to conserve performance-sensitive caches. Instead, we achieve elasticity by swiftly cloning VMs into many transient, short-lived, fractional workers to multiplex physical resources at a much finer granularity. The memory of a micro-elastic clone is a logical replica of the parent VM state, including caches, yet its footprint is proportional to the workload, and often a fraction of the nominal maximum. We enable micro-elasticity through a novel technique dubbed VM state coloring, which classifies VM memory into sets of semantically-related regions, and optimizes the propagation, allocation and deduplication of these regions. Using coloring, we build Kaleidoscope and empirically demonstrate its ability to create micro-elastic cloned servers. We model the impact of micro-elasticity on a demand dataset from AT&T's cloud, and show that fine-grained multiplexing yields infrastructure reductions of 30% relative to state-of-the art techniques for managing elastic clouds.