Standard

An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows. / Ilyushkin, Alexey; Ali-Eldin, Ahmed; Herbst, Nikolas; Papadopoulos, Alessandro; Ghit, Bogdan; Epema, Dick; Iosup, Alexandru.

Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE 2017. New York, NY : Association for Computing Machinery (ACM), 2017. p. 75-86.

Research output: Scientific - peer-reviewConference contribution

Harvard

Ilyushkin, A, Ali-Eldin, A, Herbst, N, Papadopoulos, A, Ghit, B, Epema, D & Iosup, A 2017, An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows. in Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE 2017. Association for Computing Machinery (ACM), New York, NY, pp. 75-86, ICPE 2017, L'Aquila, Italy, 22/04/17. DOI: 10.1145/3030207.3030214

APA

Ilyushkin, A., Ali-Eldin, A., Herbst, N., Papadopoulos, A., Ghit, B., Epema, D., & Iosup, A. (2017). An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows. In Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE 2017 (pp. 75-86). New York, NY: Association for Computing Machinery (ACM). DOI: 10.1145/3030207.3030214

Vancouver

Ilyushkin A, Ali-Eldin A, Herbst N, Papadopoulos A, Ghit B, Epema D et al. An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows. In Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE 2017. New York, NY: Association for Computing Machinery (ACM). 2017. p. 75-86. Available from, DOI: 10.1145/3030207.3030214

Author

Ilyushkin, Alexey ; Ali-Eldin, Ahmed ; Herbst, Nikolas ; Papadopoulos, Alessandro ; Ghit, Bogdan ; Epema, Dick ; Iosup, Alexandru. / An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows. Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE 2017. New York, NY : Association for Computing Machinery (ACM), 2017. pp. 75-86

BibTeX

@inbook{72778db789aa4dc98105217a2d1d6f64,
title = "An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows",
abstract = "Simplifying the task of resource management and scheduling for customers, while still delivering complex Quality-of-Service (QoS), is key to cloud computing. Many autoscaling policies have been proposed in the past decade to decide on behalf of cloud customers when and how to provision resources to a cloud application utilizing cloud elasticity features. However, in prior work, when a new policy is proposed, it is seldom compared to the state-of-the-art, and isoften compared only to static provisioning using a predefined QoS target. This reduces the ability of cloud customers and of cloud operators to choose and deploy an autoscaling policy. In our work, we conduct an experimental performance evaluation of autoscaling policies, using as application model workflows, a commonly used formalism for automating resource management for applications with well-defined yet complex structure. We present a detailedcomparative study of general state-of-the-art autoscaling policies, along with two new workflow-specific policies. To understand the performance differences between the 7 policies, we conduct various forms of pairwise and group comparisons. We report both individual and aggregated metrics. Our results highlight the trade-offs between the suggested policies, and thus enable a better understanding of the current state-of-the-art.",
author = "Alexey Ilyushkin and Ahmed Ali-Eldin and Nikolas Herbst and Alessandro Papadopoulos and Bogdan Ghit and Dick Epema and Alexandru Iosup",
year = "2017",
doi = "10.1145/3030207.3030214",
pages = "75--86",
booktitle = "Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE 2017",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - CHAP

T1 - An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows

AU - Ilyushkin,Alexey

AU - Ali-Eldin,Ahmed

AU - Herbst,Nikolas

AU - Papadopoulos,Alessandro

AU - Ghit,Bogdan

AU - Epema,Dick

AU - Iosup,Alexandru

PY - 2017

Y1 - 2017

N2 - Simplifying the task of resource management and scheduling for customers, while still delivering complex Quality-of-Service (QoS), is key to cloud computing. Many autoscaling policies have been proposed in the past decade to decide on behalf of cloud customers when and how to provision resources to a cloud application utilizing cloud elasticity features. However, in prior work, when a new policy is proposed, it is seldom compared to the state-of-the-art, and isoften compared only to static provisioning using a predefined QoS target. This reduces the ability of cloud customers and of cloud operators to choose and deploy an autoscaling policy. In our work, we conduct an experimental performance evaluation of autoscaling policies, using as application model workflows, a commonly used formalism for automating resource management for applications with well-defined yet complex structure. We present a detailedcomparative study of general state-of-the-art autoscaling policies, along with two new workflow-specific policies. To understand the performance differences between the 7 policies, we conduct various forms of pairwise and group comparisons. We report both individual and aggregated metrics. Our results highlight the trade-offs between the suggested policies, and thus enable a better understanding of the current state-of-the-art.

AB - Simplifying the task of resource management and scheduling for customers, while still delivering complex Quality-of-Service (QoS), is key to cloud computing. Many autoscaling policies have been proposed in the past decade to decide on behalf of cloud customers when and how to provision resources to a cloud application utilizing cloud elasticity features. However, in prior work, when a new policy is proposed, it is seldom compared to the state-of-the-art, and isoften compared only to static provisioning using a predefined QoS target. This reduces the ability of cloud customers and of cloud operators to choose and deploy an autoscaling policy. In our work, we conduct an experimental performance evaluation of autoscaling policies, using as application model workflows, a commonly used formalism for automating resource management for applications with well-defined yet complex structure. We present a detailedcomparative study of general state-of-the-art autoscaling policies, along with two new workflow-specific policies. To understand the performance differences between the 7 policies, we conduct various forms of pairwise and group comparisons. We report both individual and aggregated metrics. Our results highlight the trade-offs between the suggested policies, and thus enable a better understanding of the current state-of-the-art.

UR - http://resolver.tudelft.nl/uuid:72778db7-89aa-4dc9-8105-217a2d1d6f64

U2 - 10.1145/3030207.3030214

DO - 10.1145/3030207.3030214

M3 - Conference contribution

SP - 75

EP - 86

BT - Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE 2017

PB - Association for Computing Machinery (ACM)

ER -

ID: 29617131