Paper Info
Title
Model-Driven Geo-Elasticity in Database Clouds
Abstract
Motivated by the emergence of distributed clouds, we argue for the need for geo-elastic provisioning of application replicas to effectively handle temporal and spatial workload fluctuations seen by such applications. We present DB Scale, a system that tracks geographic variations in the workload to dynamically provision database replicas at different cloud locations across the globe. Our geo-elastic provisioning approach comprises a regression-based model to infer the database query workload from observations of the spatially distributed front-end workload and a two-node open queueing network model to provision databases with both CPU and I/O-intensive query workloads. We implement a prototype of our DB Scale system on Amazon EC2's distributed cloud. Our experiments with our prototype show up to a 66% improvement in response time when compared to local elasticity approaches.
Year
DOI
Venue
2015
10.1109/ICAC.2015.46
International Conference on Autonomic Computing
Keywords
Field
DocType
Distributed Clouds, Database Elasticity
Database query,Computer science,Workload,Queueing network models,Server,Response time,Provisioning,Real-time computing,Elasticity (economics),Database,Distributed computing,Cloud computing
Conference
Citations 
PageRank 
References 
4
0.39
22
Authors
2
Name
Order
Citations
PageRank
Tian Guo1965.34
Prashant J. Shenoy26386521.30