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 is often 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 detailed comparative study of general, state-of-the-art, generic 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 highlights the trade-offs between the suggested policies, and thus enable a better understanding of the current state-of-the-art.
%0 Conference Paper
%1 IlAlHePa-ICPE17-AutoScalerWorkflowEval
%A Ilyushkin, Alexey
%A Ali-Eldin, Ahmed
%A Herbst, Nikolas
%A Papadopoulos, Alessandro V.
%A Ghit, Bogdan
%A Epema, Dick
%A Iosup, Alexandru
%B Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017)
%C New York, NY, USA
%D 2017
%I ACM
%K BUNGEE Cloud Elasticity Metrics_and_benchmarking_methodologies Resource_management SPEC Self-adaptive-systems Virtualization descartes t_full
%T An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows
%X 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 is often 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 detailed comparative study of general, state-of-the-art, generic 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 highlights the trade-offs between the suggested policies, and thus enable a better understanding of the current state-of-the-art.
@inproceedings{IlAlHePa-ICPE17-AutoScalerWorkflowEval,
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 is often 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 detailed comparative study of general, state-of-the-art, generic 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 highlights the trade-offs between the suggested policies, and thus enable a better understanding of the current state-of-the-art.},
added-at = {2020-04-06T11:28:35.000+0200},
address = {New York, NY, USA},
author = {Ilyushkin, Alexey and Ali-Eldin, Ahmed and Herbst, Nikolas and Papadopoulos, Alessandro V. and Ghit, Bogdan and Epema, Dick and Iosup, Alexandru},
biburl = {https://www.bibsonomy.org/bibtex/2338ed83abad1eceb6a540999fc9ce3d4/se-group},
booktitle = {Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017)},
interhash = {879f973ce9c49039444be7bc31d43f6f},
intrahash = {338ed83abad1eceb6a540999fc9ce3d4},
keywords = {BUNGEE Cloud Elasticity Metrics_and_benchmarking_methodologies Resource_management SPEC Self-adaptive-systems Virtualization descartes t_full},
month = {April},
note = {<b>Best Paper Candidate (1/4)</b>},
publisher = {ACM},
timestamp = {2020-10-20T11:45:17.000+0200},
title = {{An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows}},
year = 2017
}