DocumentCode :
260467
Title :
Evaluating Auto-scaling Strategies for Cloud Computing Environments
Author :
Netto, Marco A. S. ; Cardonha, Carlos ; Cunha, Renato L. F. ; Assuncao, Marcos D.
Author_Institution :
IBM Res., Sao Paulo, Brazil
fYear :
2014
fDate :
9-11 Sept. 2014
Firstpage :
187
Lastpage :
196
Abstract :
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.
Keywords :
Web sites; cloud computing; recommender systems; auto-scaling strategies; cloud computing environments; log traces; production Google data centre cluster; recommender systems; resource pool modifications; Google; Indexes; Production; Quality of service; Time measurement; Upper bound; Auto-scaling; Auto-scaling Demand Index; Cloud Computing; Elasticity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Modelling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), 2014 IEEE 22nd International Symposium on
Conference_Location :
Paris
ISSN :
1526-7539
Type :
conf
DOI :
10.1109/MASCOTS.2014.32
Filename :
7033654
Link To Document :
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