Efficient resource provisioning for elastic Cloud services based on machine learning techniques
Abstract Automated resource provisioning techniques enable the implementation of elastic services, by adapting the available resources to the service demand. This is essential for reducing power consumption and guaranteeing QoS and SLA fulfillment, especially for those services with strict QoS requi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-04-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13677-019-0128-9 |
| _version_ | 1818114097415192576 |
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| author | Rafael Moreno-Vozmediano Rubén S. Montero Eduardo Huedo Ignacio M. Llorente |
| author_facet | Rafael Moreno-Vozmediano Rubén S. Montero Eduardo Huedo Ignacio M. Llorente |
| author_sort | Rafael Moreno-Vozmediano |
| collection | DOAJ |
| description | Abstract Automated resource provisioning techniques enable the implementation of elastic services, by adapting the available resources to the service demand. This is essential for reducing power consumption and guaranteeing QoS and SLA fulfillment, especially for those services with strict QoS requirements in terms of latency or response time, such as web servers with high traffic load, data stream processing, or real-time big data analytics. Elasticity is often implemented in cloud platforms and virtualized data-centers by means of auto-scaling mechanisms. These make automated resource provisioning decisions based on the value of specific infrastructure and/or service performance metrics. This paper presents and evaluates a novel predictive auto-scaling mechanism based on machine learning techniques for time series forecasting and queuing theory. The new mechanism aims to accurately predict the processing load of a distributed server and estimate the appropriate number of resources that must be provisioned in order to optimize the service response time and fulfill the SLA contracted by the user, while attenuating resource over-provisioning in order to reduce energy consumption and infrastructure costs. The results show that the proposed model obtains a better forecasting accuracy than other classical models, and makes a resource allocation closer to the optimal case. |
| first_indexed | 2024-12-11T03:45:18Z |
| format | Article |
| id | doaj.art-6fb7daa77a604ee38bb878b4684d7974 |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-12-11T03:45:18Z |
| publishDate | 2019-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-6fb7daa77a604ee38bb878b4684d79742022-12-22T01:22:02ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2019-04-018111810.1186/s13677-019-0128-9Efficient resource provisioning for elastic Cloud services based on machine learning techniquesRafael Moreno-Vozmediano0Rubén S. Montero1Eduardo Huedo2Ignacio M. Llorente3Computer Science School, Complutense UniversityComputer Science School, Complutense UniversityComputer Science School, Complutense UniversityComputer Science School, Complutense UniversityAbstract Automated resource provisioning techniques enable the implementation of elastic services, by adapting the available resources to the service demand. This is essential for reducing power consumption and guaranteeing QoS and SLA fulfillment, especially for those services with strict QoS requirements in terms of latency or response time, such as web servers with high traffic load, data stream processing, or real-time big data analytics. Elasticity is often implemented in cloud platforms and virtualized data-centers by means of auto-scaling mechanisms. These make automated resource provisioning decisions based on the value of specific infrastructure and/or service performance metrics. This paper presents and evaluates a novel predictive auto-scaling mechanism based on machine learning techniques for time series forecasting and queuing theory. The new mechanism aims to accurately predict the processing load of a distributed server and estimate the appropriate number of resources that must be provisioned in order to optimize the service response time and fulfill the SLA contracted by the user, while attenuating resource over-provisioning in order to reduce energy consumption and infrastructure costs. The results show that the proposed model obtains a better forecasting accuracy than other classical models, and makes a resource allocation closer to the optimal case.http://link.springer.com/article/10.1186/s13677-019-0128-9Cloud computingElasticityAuto-scalingMachine learning |
| spellingShingle | Rafael Moreno-Vozmediano Rubén S. Montero Eduardo Huedo Ignacio M. Llorente Efficient resource provisioning for elastic Cloud services based on machine learning techniques Journal of Cloud Computing: Advances, Systems and Applications Cloud computing Elasticity Auto-scaling Machine learning |
| title | Efficient resource provisioning for elastic Cloud services based on machine learning techniques |
| title_full | Efficient resource provisioning for elastic Cloud services based on machine learning techniques |
| title_fullStr | Efficient resource provisioning for elastic Cloud services based on machine learning techniques |
| title_full_unstemmed | Efficient resource provisioning for elastic Cloud services based on machine learning techniques |
| title_short | Efficient resource provisioning for elastic Cloud services based on machine learning techniques |
| title_sort | efficient resource provisioning for elastic cloud services based on machine learning techniques |
| topic | Cloud computing Elasticity Auto-scaling Machine learning |
| url | http://link.springer.com/article/10.1186/s13677-019-0128-9 |
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