On demand elastic capacity planning for service auto-scaling

Pavel Chuprikov; Sergey Nikolenko; Kirill Kogan

doi:10.1109/INFOCOM.2016.7524616

On demand elastic capacity planning for service auto-scaling

Pavel Chuprikov, Sergey Nikolenko, Kirill Kogan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

Cloud computing allows on demand elastic service scaling. The capability of a service to predict resource requirements for the next operational period defines how well it will exploit the elasticity of cloud computing in order to reduce operational costs. In this work, we consider a capacity planning process for service scale-out as an online pricing model. In particular, we study the impact of buffering service requests on revenues in various settings with allocation and maintenance costs. In addition, we analyze the incurred latency implied by buffering service requests. We believe that our insights will allow to significantly simplify predictions and mitigate the unknowns of future demands on resources.

Original language	English
Title of host publication	IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467399531
DOIs	https://doi.org/10.1109/INFOCOM.2016.7524616
State	Published - 27 Jul 2016
Externally published	Yes
Event	35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016 - San Francisco, United States Duration: 10 Apr 2016 → 14 Apr 2016

Publication series

Name	Proceedings - IEEE INFOCOM
Volume	2016-July
ISSN (Print)	0743-166X

Conference

Conference	35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016
Country/Territory	United States
City	San Francisco
Period	10/04/16 → 14/04/16

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10.1109/INFOCOM.2016.7524616

Cite this

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title = "On demand elastic capacity planning for service auto-scaling",

abstract = "Cloud computing allows on demand elastic service scaling. The capability of a service to predict resource requirements for the next operational period defines how well it will exploit the elasticity of cloud computing in order to reduce operational costs. In this work, we consider a capacity planning process for service scale-out as an online pricing model. In particular, we study the impact of buffering service requests on revenues in various settings with allocation and maintenance costs. In addition, we analyze the incurred latency implied by buffering service requests. We believe that our insights will allow to significantly simplify predictions and mitigate the unknowns of future demands on resources.",

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Chuprikov, P, Nikolenko, S & Kogan, K 2016, On demand elastic capacity planning for service auto-scaling. in IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications., 7524616, Proceedings - IEEE INFOCOM, vol. 2016-July, Institute of Electrical and Electronics Engineers Inc., 35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016, San Francisco, United States, 10/04/16. https://doi.org/10.1109/INFOCOM.2016.7524616

On demand elastic capacity planning for service auto-scaling. / Chuprikov, Pavel; Nikolenko, Sergey; Kogan, Kirill.
IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications. Institute of Electrical and Electronics Engineers Inc., 2016. 7524616 (Proceedings - IEEE INFOCOM; Vol. 2016-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Cloud computing allows on demand elastic service scaling. The capability of a service to predict resource requirements for the next operational period defines how well it will exploit the elasticity of cloud computing in order to reduce operational costs. In this work, we consider a capacity planning process for service scale-out as an online pricing model. In particular, we study the impact of buffering service requests on revenues in various settings with allocation and maintenance costs. In addition, we analyze the incurred latency implied by buffering service requests. We believe that our insights will allow to significantly simplify predictions and mitigate the unknowns of future demands on resources.

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On demand elastic capacity planning for service auto-scaling

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