A new temporal locality-based workload prediction approach for SaaS services in a cloud environment
As the paradigm shift toward Software as a Service (SaaS) continues to gain the interest of companies and the scientific community, performances must be optimal. Indeed, cloud providers must provide an optimal quality of service (QoS) for their users in order to survive in such a competitive cloud m...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S131915782100094X |
| _version_ | 1811329425581539328 |
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| author | Wiem Matoussi Tarek Hamrouni |
| author_facet | Wiem Matoussi Tarek Hamrouni |
| author_sort | Wiem Matoussi |
| collection | DOAJ |
| description | As the paradigm shift toward Software as a Service (SaaS) continues to gain the interest of companies and the scientific community, performances must be optimal. Indeed, cloud providers must provide an optimal quality of service (QoS) for their users in order to survive in such a competitive cloud market. Workload forecasting techniques have been proposed in order to improve capacity planning, ensure efficient management of resources and, hence, maintain SLA contracts with end users. In this context, we propose a new approach to predict the number of requests arriving at a SaaS service in order to prepare the virtualized resources necessary to respond to user requests. The method will be implemented in order to simultaneously achieve a twofold benefit: obtain precise forecast results while optimizing response time. In this regard, we have chosen to control the computation time by dynamizing the size of the sliding window associated to the recent history to be analyzed, since the larger the size of the entry in the prediction model, the more the algorithmic complexity increases. Then, the prediction will be established based on the temporal locality principle and the dynamic assignment of weights to different data points in recent history. Moreover, the proposed method can be extended to cover other uses in prediction. Experiments were carried out to assess the performance of the proposed method using two real workload traces and compared to state-of-the-art methods. The proposed method offers a compromise between the execution time and the accuracy of the prediction. |
| first_indexed | 2024-04-13T15:43:41Z |
| format | Article |
| id | doaj.art-f141f5772bc7475d812fb96acb03a94b |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-13T15:43:41Z |
| publishDate | 2022-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-f141f5772bc7475d812fb96acb03a94b2022-12-22T02:41:03ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-07-0134739733987A new temporal locality-based workload prediction approach for SaaS services in a cloud environmentWiem Matoussi0Tarek Hamrouni1LIPAH, Faculty of Sciences of Tunis, Tunis El Manar University, University Campus, Tunis, TunisiaCorresponding author.; LIPAH, Faculty of Sciences of Tunis, Tunis El Manar University, University Campus, Tunis, TunisiaAs the paradigm shift toward Software as a Service (SaaS) continues to gain the interest of companies and the scientific community, performances must be optimal. Indeed, cloud providers must provide an optimal quality of service (QoS) for their users in order to survive in such a competitive cloud market. Workload forecasting techniques have been proposed in order to improve capacity planning, ensure efficient management of resources and, hence, maintain SLA contracts with end users. In this context, we propose a new approach to predict the number of requests arriving at a SaaS service in order to prepare the virtualized resources necessary to respond to user requests. The method will be implemented in order to simultaneously achieve a twofold benefit: obtain precise forecast results while optimizing response time. In this regard, we have chosen to control the computation time by dynamizing the size of the sliding window associated to the recent history to be analyzed, since the larger the size of the entry in the prediction model, the more the algorithmic complexity increases. Then, the prediction will be established based on the temporal locality principle and the dynamic assignment of weights to different data points in recent history. Moreover, the proposed method can be extended to cover other uses in prediction. Experiments were carried out to assess the performance of the proposed method using two real workload traces and compared to state-of-the-art methods. The proposed method offers a compromise between the execution time and the accuracy of the prediction.http://www.sciencedirect.com/science/article/pii/S131915782100094XCloud computingSaaSWorkloadPredictionMachine learningTemporal locality |
| spellingShingle | Wiem Matoussi Tarek Hamrouni A new temporal locality-based workload prediction approach for SaaS services in a cloud environment Journal of King Saud University: Computer and Information Sciences Cloud computing SaaS Workload Prediction Machine learning Temporal locality |
| title | A new temporal locality-based workload prediction approach for SaaS services in a cloud environment |
| title_full | A new temporal locality-based workload prediction approach for SaaS services in a cloud environment |
| title_fullStr | A new temporal locality-based workload prediction approach for SaaS services in a cloud environment |
| title_full_unstemmed | A new temporal locality-based workload prediction approach for SaaS services in a cloud environment |
| title_short | A new temporal locality-based workload prediction approach for SaaS services in a cloud environment |
| title_sort | new temporal locality based workload prediction approach for saas services in a cloud environment |
| topic | Cloud computing SaaS Workload Prediction Machine learning Temporal locality |
| url | http://www.sciencedirect.com/science/article/pii/S131915782100094X |
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