Impact of user patience on auto-scaling resource capacity for cloud services
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...
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| Published in | Future generation computer systems Vol. 55; pp. 41 - 50 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.02.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-739X 1872-7115 1872-7115 |
| DOI | 10.1016/j.future.2015.09.001 |
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| 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.
•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. |
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| AbstractList | 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.
•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. |
| Author | Cunha, Renato L.F. Cardonha, Carlos H. Netto, Marco A.S. de Assunção, Marcos Dias |
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| Cites_doi | 10.1109/TPDS.2007.70606 10.1006/jpdc.2000.1714 10.1287/msom.5.2.79.16071 10.1109/CLOUD.2012.103 10.1109/ISCC.1997.615962 10.1016/S1389-1286(00)00087-6 10.1007/978-3-642-45005-1_45 10.1109/CCGrid.2011.71 10.1109/MIC.2011.20 10.1198/016214504000001808 10.1109/TPDS.2007.1053 10.2307/1914185 10.1109/CNSM.2013.6727813 10.1002/cpe.1634 10.1177/002224299405800205 10.1007/3-540-63574-2 10.1016/S0953-5438(97)00019-2 10.1145/1272366.1272381 |
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| Snippet | An important feature of most cloud computing solutions is auto-scaling, an operation that enables dynamic changes on resource capacity. Auto-scaling algorithms... |
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| SubjectTerms | Auto-scaling Cloud computing Resource management Scheduling |
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| Title | Impact of user patience on auto-scaling resource capacity for cloud services |
| URI | https://dx.doi.org/10.1016/j.future.2015.09.001 https://inria.hal.science/hal-01199207 |
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