PRESS: PRedictive Elastic ReSource Scaling for cloud systems
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.
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.
Research Areas
