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Which cloud auto-scaler should i use for my application? Benchmarking auto-scaling algorithms : [Poster paper]

Ali-Eldin, Ahmed; Ilyushkin, Alexey; Ghit, Bogdan; Herbst, Nikolas Roman; Papadopoulos, Alessandro LU and Iosup, Alexandru (2016) 7th ACM/SPEC International Conference on Performance Engineering, ICPE 2016 In ICPE 2016 - Proceedings of the 7th ACM/SPEC International Conference on Performance Engineering p.131-132
Abstract

Rapid elasticity is one of the essential characteristics of cloud computing identified by NIST [17]. Elasticity allows resources to be provisioned and released to scale rapidly out ward and in ward according to demand. Tens - if not hundreds - of algorithms have been proposed in the literature to automatically achieve elastic provisioning [15, 23, 14, 21, 13, 20, 6, 12, 16, 10]. These algorithms are typically referred to as elasticity algorithms, dynamic provisioning techniques or autoscalers. While trying to solve the same problem, sometimes with differing assumption, many of these algorithms are either compared to static provisioning or to a predefined QoS target, e.g., predefined response time target, with very little - or no -... (More)

Rapid elasticity is one of the essential characteristics of cloud computing identified by NIST [17]. Elasticity allows resources to be provisioned and released to scale rapidly out ward and in ward according to demand. Tens - if not hundreds - of algorithms have been proposed in the literature to automatically achieve elastic provisioning [15, 23, 14, 21, 13, 20, 6, 12, 16, 10]. These algorithms are typically referred to as elasticity algorithms, dynamic provisioning techniques or autoscalers. While trying to solve the same problem, sometimes with differing assumption, many of these algorithms are either compared to static provisioning or to a predefined QoS target, e.g., predefined response time target, with very little - or no - comparison to previously published work. This reduces the ability of an application owner or a cloud operator to choose and deploy a suitable algorithm from the literature. Many of these algorithms have been tested with one single - real or synthetic -workload in a specific use-case [13, 14, 10]. While all published algorithms are shown to work in the specific use-case they were designed for with the, typically short, workloads tested with, it is seldom the case that the real scenarios will be any thing close to the test cases for which the algorithms are shown to work. Bursts occur in workloads occasionally. Workload dynamics change over time and the load-mix of an application significantly affects how provisioning should be done [21].

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Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
ICPE 2016 - Proceedings of the 7th ACM/SPEC International Conference on Performance Engineering
pages
2 pages
publisher
Association for Computing Machinery, Inc
conference name
7th ACM/SPEC International Conference on Performance Engineering, ICPE 2016
external identifiers
  • scopus:85020177861
ISBN
9781450340809
DOI
10.1145/2851553.2858677
language
English
LU publication?
yes
id
669c6cbf-b80d-4009-a77f-72e04520dc6f
date added to LUP
2017-07-03 10:48:13
date last changed
2017-07-03 10:48:13
@inproceedings{669c6cbf-b80d-4009-a77f-72e04520dc6f,
  abstract     = {<p>Rapid elasticity is one of the essential characteristics of cloud computing identified by NIST [17]. Elasticity allows resources to be provisioned and released to scale rapidly out ward and in ward according to demand. Tens - if not hundreds - of algorithms have been proposed in the literature to automatically achieve elastic provisioning [15, 23, 14, 21, 13, 20, 6, 12, 16, 10]. These algorithms are typically referred to as elasticity algorithms, dynamic provisioning techniques or autoscalers. While trying to solve the same problem, sometimes with differing assumption, many of these algorithms are either compared to static provisioning or to a predefined QoS target, e.g., predefined response time target, with very little - or no - comparison to previously published work. This reduces the ability of an application owner or a cloud operator to choose and deploy a suitable algorithm from the literature. Many of these algorithms have been tested with one single - real or synthetic -workload in a specific use-case [13, 14, 10]. While all published algorithms are shown to work in the specific use-case they were designed for with the, typically short, workloads tested with, it is seldom the case that the real scenarios will be any thing close to the test cases for which the algorithms are shown to work. Bursts occur in workloads occasionally. Workload dynamics change over time and the load-mix of an application significantly affects how provisioning should be done [21].</p>},
  author       = {Ali-Eldin, Ahmed and Ilyushkin, Alexey and Ghit, Bogdan and Herbst, Nikolas Roman and Papadopoulos, Alessandro and Iosup, Alexandru},
  booktitle    = {ICPE 2016 - Proceedings of the 7th ACM/SPEC International Conference on Performance Engineering},
  isbn         = {9781450340809},
  language     = {eng},
  month        = {03},
  pages        = {131--132},
  publisher    = {Association for Computing Machinery, Inc},
  title        = {Which cloud auto-scaler should i use for my application? Benchmarking auto-scaling algorithms : [Poster paper]},
  url          = {http://dx.doi.org/10.1145/2851553.2858677},
  year         = {2016},
}