Paper Info
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 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 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.
Year
DOI
Venue
2017
10.1145/3030207.3030214
ICPE
Field
DocType
Citations 
Resource management,Data science,Pairwise comparison,Scheduling (computing),Computer science,Quality of service,Control engineering,Provisioning,Autoscaling,Workflow,Cloud computing,Distributed computing
Conference
18
PageRank 
References 
Authors
0.88
29
7
Name
Order
Citations
PageRank
Alexey Ilyushkin1343.45
Ahmed Ali-Eldin244224.01
Nikolas Roman Herbst327823.18
Alessandro Papadopoulos428127.10
Bogdan Ghit5897.30
Dick H. J. Epema63134180.80
Alexandru Iosup72042125.89