PRESS: PRedictive Elastic ReSource Scaling for cloud systems
Venue
6th IEEE/IFIP International Conference on Network and Service Management (CNSM 2010), Niagara Falls, Canada
Publication Year
2010
Authors
Zhenhuan Gong, Xiaohui Gu, John Wilkes
BibTeX
Abstract
Cloud systems require elastic resource allocation to minimize resource provisioning
costs while meeting service level objectives (SLOs). In this paper, we present a
novel PRedictive Elastic reSource Scaling (PRESS) scheme for cloud systems. PRESS
unobtrusively extracts fine-grained dynamic patterns in application resource demands
and adjust their resource allocations automatically. Our approach leverages
light-weight signal processing and statistical learning algorithms to achieve
online predictions of dynamic application resource requirements. We have
implemented the PRESS system on Xen and tested it using RUBiS and an application
load trace from Google. Our experiments show that we can achieve good resource
prediction accuracy with less than 5% over-estimation error and near zero
under-estimation error, and elastic resource scaling can both significantly reduce
resource waste and SLO violations.
