Efficient Provisioning of Bursty Scientific Workloads on the Cloud Using Adaptive Elasticity Control
(2012) ScienceCloud 3rd Workshop on Scientific Cloud Computing- Abstract
- 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... (More) - 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2539762
- author
- Ali-Eldin, Ahmed ; Kihl, Maria LU ; Tordsson, Johan and Elmroth, Erik
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- [Host publication title missing]
- publisher
- Association for Computing Machinery (ACM)
- conference name
- ScienceCloud 3rd Workshop on Scientific Cloud Computing
- conference location
- Netherlands
- conference dates
- 2012-06-18
- external identifiers
-
- scopus:84863924453
- project
- LCCC
- language
- English
- LU publication?
- yes
- id
- 49e6a5f6-a4e2-44cc-b6c5-b098ebc53e35 (old id 2539762)
- date added to LUP
- 2016-04-04 09:57:49
- date last changed
- 2022-05-17 02:07:51
@inproceedings{49e6a5f6-a4e2-44cc-b6c5-b098ebc53e35,
abstract = {{Elasticity is the ability of a cloud infrastructure to dynamically<br/><br>
change the amount of resources allocated to a running<br/><br>
service as load changes. We build an autonomous elasticity<br/><br>
controller that changes the number of virtual machines allocated<br/><br>
to a service based on both monitored load changes<br/><br>
and predictions of future load. The cloud infrastructure is<br/><br>
modeled as a G=G=N queue. This model is used to construct<br/><br>
a hybrid reactive-adaptive controller that quickly reacts<br/><br>
to sudden load changes, prevents premature release of<br/><br>
resources, takes into account the heterogeneity of the workload,<br/><br>
and avoids oscillations. Using simulations with Web<br/><br>
and cluster workload traces, we show that our proposed controller<br/><br>
lowers the number of delayed requests by a factor of<br/><br>
70 for the Web traces and 3 for the cluster traces when compared<br/><br>
to a reactive controller. Our controller also decreases<br/><br>
the average number of queued requests by a factor of 3 for<br/><br>
both traces, and reduces oscillations by a factor of 7 for<br/><br>
the Web traces and 3 for the cluster traces. This comes at<br/><br>
the expense of between 20% and 30% over-provisioning, as<br/><br>
compared to a few percent for the reactive controller.}},
author = {{Ali-Eldin, Ahmed and Kihl, Maria and Tordsson, Johan and Elmroth, Erik}},
booktitle = {{[Host publication title missing]}},
language = {{eng}},
publisher = {{Association for Computing Machinery (ACM)}},
title = {{Efficient Provisioning of Bursty Scientific Workloads on the Cloud Using Adaptive Elasticity Control}},
url = {{https://lup.lub.lu.se/search/files/5427572/3242652.pdf}},
year = {{2012}},
}