Evaluating Auto-scaling Strategies for Cloud Computing Environments

Netto, Marco A. S.; Cardonha, Carlos; Cunha, Renato L. F.; Assunção, Marcos Dias de

doi:10.1109/mascots.2014.32

Cited by 58 publications

(32 citation statements)

References 11 publications

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“…In order to address this challenge, auto-scaling [21], [22] has been proposed. Current solutions typically rely on threshold-based rules, offered by several commercial cloud providers/platforms such as Amazon EC2, Microsoft Azure and OpenStack.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures

Jamshidi

Sharifloo

Pahl

et al. 2016

2016 12th International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA)

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Abstract-Cloud controllers support the operation and quality management of dynamic cloud architectures by automatically scaling the compute resources to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of architecture adaptation rules. However, for a cloud provider, deployed application architectures are black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. We propose the dynamic learning of adaptation rules for deployed application architectures in the cloud. We introduce FQL4KE, a self-learning fuzzy controller that learns and modifies fuzzy rules at runtime. The benefit is that we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to configure cloud controllers by simply adjusting weights representing priorities for architecture quality instead of defining complex rules. FQL4KE has been experimentally validated using the cloud application framework ElasticBench in Azure and OpenStack. The experimental results demonstrate that FQL4KE outperforms both a fuzzy controller without learning and the native Azure auto-scaling.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures

Jamshidi

Sharifloo

Pahl

et al. 2016

2016 12th International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA)

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“…• Unbounded-resource-pool Utilisation Trigger (UUT): uses system utilisation and employs a formula to determine an upper bound and a lower bound on step sizes for activating/releasing resource pool capacity, based on previous work [11];…”

Section: Auto-scaling and Scheduling Strategiesmentioning

confidence: 99%

“…Under these circumstances, we employ Unboundedresource-pool Utilisation Trigger (UUT), a strategy introduced in our previous work [11] in which the upper bound on s for scaleout operations is given by …”

Section: Utilisation-based Auto-scalingmentioning

confidence: 99%

Impact of user patience on auto-scaling resource capacity for cloud services

Assunção

Cardonha

Netto

et al. 2016

Future Generation Computer Systems

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h i g h l i g h t s• Mechanisms for resource auto-scaling in clouds considering users' patience.• Methods for determining the step size of scaling operations under bound and unbounded maximum capacity. • Users patience model inspired in prospect theory. a b s t r a c tAn 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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“…In addition to the aforementioned computing infrastructure sources, cloud computing has proved itself as a new way to acquire computing resources on demand (Lorido-Botrán et al, 2012;Beernaert et al, 2012;Gong et al, 2010). An important characteristic, not available on traditional architectures (e.g., clusters and grids) emerged on cloud computing: elasticity (Chilipirea et al, 2016;Netto et al, 2014;da Rosa Righi et al, 2015). Elasticity is defined as the ability of a system to dynamically add or remove computational resources used by either an application or user to match the current demand as closely as possible.…”

Section: Introductionmentioning

confidence: 99%

Towards providing middleware-level proactive resource reorganisation for elastic HPC applications in the cloud

Righi

Rodrigues

Nardin

et al. 2019

IJGUC

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Elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Particularly for High Performance Computing (HPC), elasticity can provide a better use of resources and also a reduction in the execution time of applications. Today, we observe the emergence of proactive initiatives to handle the elasticity and HPC duet, but they present at least one problem related to the need of a previous user experience, large processing time or completion of parameters. Concerning the aforesaid context, this paper presents ProElastic-a lightweight model that uses proactive elasticity to drive resource reorganisation decisions for HPC applications. Our idea is to explore both performance and adaptivity at middleware level in an effortless way at user perspective. The results showed performance gains and a competitive cost (application time  consumed resources).

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Evaluating Auto-scaling Strategies for Cloud Computing Environments

Cited by 58 publications

References 11 publications

Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures

Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures

Impact of user patience on auto-scaling resource capacity for cloud services

Towards providing middleware-level proactive resource reorganisation for elastic HPC applications in the cloud

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