Evaluating Auto-scaling Strategies for Cloud Computing Environments

Auto-scaling is a key feature in clouds responsible for adjusting the number of available resources to meet service demand. Resource pool modifications are necessary to keep performance indicators, such as utilisation level, between user-defined lower and upper bounds. Auto-scaling strategies that a...

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Bibliographic Details
Published in2014 IEEE 22nd International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems pp. 187 - 196
Main Authors Netto, Marco A. S., Cardonha, Carlos, Cunha, Renato L. F., Assuncao, Marcos D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2014
Subjects
Auto-scaling
Auto-scaling Demand Index
Cloud Computing
Elasticity
Google
Indexes
Production
Quality of service
Time measurement
Upper bound
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Summary:Auto-scaling is a key feature in clouds responsible for adjusting the number of available resources to meet service demand. Resource pool modifications are necessary to keep performance indicators, such as utilisation level, between user-defined lower and upper bounds. Auto-scaling strategies that are not properly configured according to user workload characteristics may lead to unacceptable QoS and large resource waste. As a consequence, there is a need for a deeper understanding of auto-scaling strategies and how they should be configured to minimise these problems. In this work, we evaluate various auto-scaling strategies using log traces from a production Google data centre cluster comprising millions of jobs. Using utilisation level as performance indicator, our results show that proper management of auto-scaling parameters reduces the difference between the target utilisation interval and the actual values-we define such difference as Auto-scaling Demand Index. We also present a set of lessons from this study to help cloud providers build recommender systems for auto-scaling operations.
ISSN:1526-7539
2375-0227
DOI:10.1109/MASCOTS.2014.32