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Impact of User Patience on Auto-Scaling Resource Capacity for Cloud Services

Marcos Dias de Assuncao 1 Carlos Cardonha 2 Marco Netto 2 Renato Cunha 2 
1 AVALON - Algorithms and Software Architectures for Distributed and HPC Platforms
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme
Abstract : An important feature of most cloud computing solutions is auto-scaling, an operation that enables dynamic changes on resource capacity. Auto-scaling algorithms generally take into account aspects such as system load and response time to determine when and by how much a resource pool capacity should be extended or shrunk. In this article, we propose a scheduling algorithm and auto-scaling triggering strategies that explore user patience, a metric that estimates the perception end-users have from the Quality of Service (QoS) delivered by a service provider based on the ratio between expected and actual response times for each request. The proposed strategies help reduce costs with resource allocation while maintaining perceived QoS at adequate levels. Results show reductions on resource-hour consumption by up to approximately 9% compared to traditional approaches.
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Submitted on : Tuesday, September 15, 2015 - 10:06:50 AM
Last modification on : Tuesday, October 25, 2022 - 4:21:49 PM
Long-term archiving on: : Tuesday, December 29, 2015 - 6:59:31 AM


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  • HAL Id : hal-01199207, version 1



Marcos Dias de Assuncao, Carlos Cardonha, Marco Netto, Renato Cunha. Impact of User Patience on Auto-Scaling Resource Capacity for Cloud Services. Future Generation Computer Systems, 2015, pp.1-10. ⟨hal-01199207⟩



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