Efficient Provisioning of Bursty Scientific Workloads on the Cloud Using Adaptive Elasticity Control
Author
Summary, in English
Elasticity is the ability of a cloud infrastructure to dynamically
change the amount of resources allocated to a running
service as load changes. We build an autonomous elasticity
controller that changes the number of virtual machines allocated
to a service based on both monitored load changes
and predictions of future load. The cloud infrastructure is
modeled as a G=G=N queue. This model is used to construct
a hybrid reactive-adaptive controller that quickly reacts
to sudden load changes, prevents premature release of
resources, takes into account the heterogeneity of the workload,
and avoids oscillations. Using simulations with Web
and cluster workload traces, we show that our proposed controller
lowers the number of delayed requests by a factor of
70 for the Web traces and 3 for the cluster traces when compared
to a reactive controller. Our controller also decreases
the average number of queued requests by a factor of 3 for
both traces, and reduces oscillations by a factor of 7 for
the Web traces and 3 for the cluster traces. This comes at
the expense of between 20% and 30% over-provisioning, as
compared to a few percent for the reactive controller.
change the amount of resources allocated to a running
service as load changes. We build an autonomous elasticity
controller that changes the number of virtual machines allocated
to a service based on both monitored load changes
and predictions of future load. The cloud infrastructure is
modeled as a G=G=N queue. This model is used to construct
a hybrid reactive-adaptive controller that quickly reacts
to sudden load changes, prevents premature release of
resources, takes into account the heterogeneity of the workload,
and avoids oscillations. Using simulations with Web
and cluster workload traces, we show that our proposed controller
lowers the number of delayed requests by a factor of
70 for the Web traces and 3 for the cluster traces when compared
to a reactive controller. Our controller also decreases
the average number of queued requests by a factor of 3 for
both traces, and reduces oscillations by a factor of 7 for
the Web traces and 3 for the cluster traces. This comes at
the expense of between 20% and 30% over-provisioning, as
compared to a few percent for the reactive controller.
Department/s
Publishing year
2012
Language
English
Publication/Series
[Host publication title missing]
Full text
Document type
Conference paper
Publisher
Association for Computing Machinery (ACM)
Topic
- Electrical Engineering, Electronic Engineering, Information Engineering
Conference name
ScienceCloud 3rd Workshop on Scientific Cloud Computing
Conference date
2012-06-18
Conference place
Netherlands
Status
Published
Project
- LCCC
Research group
- Broadband Communication