An Evaluation of Bin-Packing Algorithms Using Real Program Traces

The use of the dynamic memory management problem as an instance of a onedimensional bin-packing problem is a very natural one. As fundamental compute

Help Conquer Cancer: Using GPUs to Accelerate Protein Crystallography Image Analysis

As part of the greater effort to find a cure for cancer, Igor Jurisica’s group at the Ontario Cancer Institute (OCI) is running a project to improve the throughput of protein crystallography [4, 5]. When proteins crystallize, their structure can be determined by observin

Kaleidoscope: Cloud Micro-Elasticity via VM State Coloring

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 microelasticity 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