ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
Dynamic resource provisioning is made more accessible with cloud computing. Monitoring a running service is critical, and modifications are performed when specific criteria are exceeded. It is a standard practice to add or delete resources in such situations. We investigate the method to ensure the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/14/5/131 |
| _version_ | 1797499783882997760 |
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| author | Hassan Mahmood Khan Fang-Fang Chua Timothy Tzen Vun Yap |
| author_facet | Hassan Mahmood Khan Fang-Fang Chua Timothy Tzen Vun Yap |
| author_sort | Hassan Mahmood Khan |
| collection | DOAJ |
| description | Dynamic resource provisioning is made more accessible with cloud computing. Monitoring a running service is critical, and modifications are performed when specific criteria are exceeded. It is a standard practice to add or delete resources in such situations. We investigate the method to ensure the Quality of Service (QoS), estimate the required resources, and modify allotted resources depending on workload, serialization, and parallelism due to resources. This article focuses on cloud QoS violation remediation using resource planning and scaling. A Resource Quantified Scaling for QoS Violation (ReSQoV) model is proposed based on the Universal Scalability Law (USL), which provides cloud service capacity for specific workloads and generates a capacity model. ReSQoV considers the system overheads while allocating resources to maintain the agreed QoS. As the QoS violation detection decision is Probably Violation and Definitely Violation, the remedial action is triggered, and required resources are added to the virtual machine as vertical scaling. The scenarios emulate QoS parameters and their respective resource utilization for ReSQoV compared to policy-based resource allocation. The results show that after USLbased Quantified resource allocation, QoS is regained, and validation of the ReSQoV is performed through the statistical test ANOVA that shows the significant difference before and after implementation. |
| first_indexed | 2024-03-10T03:52:23Z |
| format | Article |
| id | doaj.art-9b9b5d817507464bbea9fafa57b3cb53 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T03:52:23Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-9b9b5d817507464bbea9fafa57b3cb532023-11-23T11:04:06ZengMDPI AGFuture Internet1999-59032022-04-0114513110.3390/fi14050131ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud ServicesHassan Mahmood Khan0Fang-Fang Chua1Timothy Tzen Vun Yap2Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, MalaysiaFaculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, MalaysiaFaculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, MalaysiaDynamic resource provisioning is made more accessible with cloud computing. Monitoring a running service is critical, and modifications are performed when specific criteria are exceeded. It is a standard practice to add or delete resources in such situations. We investigate the method to ensure the Quality of Service (QoS), estimate the required resources, and modify allotted resources depending on workload, serialization, and parallelism due to resources. This article focuses on cloud QoS violation remediation using resource planning and scaling. A Resource Quantified Scaling for QoS Violation (ReSQoV) model is proposed based on the Universal Scalability Law (USL), which provides cloud service capacity for specific workloads and generates a capacity model. ReSQoV considers the system overheads while allocating resources to maintain the agreed QoS. As the QoS violation detection decision is Probably Violation and Definitely Violation, the remedial action is triggered, and required resources are added to the virtual machine as vertical scaling. The scenarios emulate QoS parameters and their respective resource utilization for ReSQoV compared to policy-based resource allocation. The results show that after USLbased Quantified resource allocation, QoS is regained, and validation of the ReSQoV is performed through the statistical test ANOVA that shows the significant difference before and after implementation.https://www.mdpi.com/1999-5903/14/5/131cloud computingSaaSresource allocationQoSscalabilityUSL |
| spellingShingle | Hassan Mahmood Khan Fang-Fang Chua Timothy Tzen Vun Yap ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services Future Internet cloud computing SaaS resource allocation QoS scalability USL |
| title | ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services |
| title_full | ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services |
| title_fullStr | ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services |
| title_full_unstemmed | ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services |
| title_short | ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services |
| title_sort | resqov a scalable resource allocation model for qos satisfied cloud services |
| topic | cloud computing SaaS resource allocation QoS scalability USL |
| url | https://www.mdpi.com/1999-5903/14/5/131 |
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