Optimizing and dimensioning a data intensive cloud application for soccer player tracking
Cloud-based services revolutionize how applications are designed and provisioned in more and more application domains. Operating a cloud application, however, requires careful choices of configuration settings so that the quality of service is acceptable at all times, while cloud costs remain reason...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2022-03-01
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| Series: | International Journal of Computer Science in Sport |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/ijcss-2022-0004 |
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| _version_ | 1849738923637473280 |
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| author | Dobreff Gergely Molnar Marton Toka Laszlo |
| author_facet | Dobreff Gergely Molnar Marton Toka Laszlo |
| author_sort | Dobreff Gergely |
| collection | DOAJ |
| description | Cloud-based services revolutionize how applications are designed and provisioned in more and more application domains. Operating a cloud application, however, requires careful choices of configuration settings so that the quality of service is acceptable at all times, while cloud costs remain reasonable. We propose an analytical queuing model for cloud resource provisioning that provides an approximation on end-to-end application latency and on cloud resource usage, and we evaluate its performance. We pick an emerging use case of cloud deployment for validation: sports analytics. We have created a low-cost, cloud-based soccer player tracking system. We present the optimization of the cloud-deployed data processing of this system: we set the parameters with the aim of sacrificing as least as possible on accuracy, i.e., quality of service, while keeping latency and cloud costs low. We demonstrate that the analytical model we propose to estimate the end-to-end latency of a microservice-type cloud native application falls within a close range of what the measurements of the real implementation show. The model is therefore suitable for the planning of the cloud deployment costs for microservice-type applications as well. |
| format | Article |
| id | doaj-art-1b0ea4d408e94151aee0922555c8fc34 |
| institution | DOAJ |
| issn | 1684-4769 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Computer Science in Sport |
| spelling | doaj-art-1b0ea4d408e94151aee0922555c8fc342025-08-20T03:06:25ZengSciendoInternational Journal of Computer Science in Sport1684-47692022-03-01211304810.2478/ijcss-2022-0004Optimizing and dimensioning a data intensive cloud application for soccer player trackingDobreff Gergely0Molnar Marton1Toka Laszlo2MTA-BME Information Systems Research Group, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary.MTA-BME Information Systems Research Group, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary.MTA-BME Information Systems Research Group, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary.Cloud-based services revolutionize how applications are designed and provisioned in more and more application domains. Operating a cloud application, however, requires careful choices of configuration settings so that the quality of service is acceptable at all times, while cloud costs remain reasonable. We propose an analytical queuing model for cloud resource provisioning that provides an approximation on end-to-end application latency and on cloud resource usage, and we evaluate its performance. We pick an emerging use case of cloud deployment for validation: sports analytics. We have created a low-cost, cloud-based soccer player tracking system. We present the optimization of the cloud-deployed data processing of this system: we set the parameters with the aim of sacrificing as least as possible on accuracy, i.e., quality of service, while keeping latency and cloud costs low. We demonstrate that the analytical model we propose to estimate the end-to-end latency of a microservice-type cloud native application falls within a close range of what the measurements of the real implementation show. The model is therefore suitable for the planning of the cloud deployment costs for microservice-type applications as well.https://doi.org/10.2478/ijcss-2022-0004cloud nativemicroservicedimensioningsoccer player tracking |
| spellingShingle | Dobreff Gergely Molnar Marton Toka Laszlo Optimizing and dimensioning a data intensive cloud application for soccer player tracking International Journal of Computer Science in Sport cloud native microservice dimensioning soccer player tracking |
| title | Optimizing and dimensioning a data intensive cloud application for soccer player tracking |
| title_full | Optimizing and dimensioning a data intensive cloud application for soccer player tracking |
| title_fullStr | Optimizing and dimensioning a data intensive cloud application for soccer player tracking |
| title_full_unstemmed | Optimizing and dimensioning a data intensive cloud application for soccer player tracking |
| title_short | Optimizing and dimensioning a data intensive cloud application for soccer player tracking |
| title_sort | optimizing and dimensioning a data intensive cloud application for soccer player tracking |
| topic | cloud native microservice dimensioning soccer player tracking |
| url | https://doi.org/10.2478/ijcss-2022-0004 |
| work_keys_str_mv | AT dobreffgergely optimizinganddimensioningadataintensivecloudapplicationforsoccerplayertracking AT molnarmarton optimizinganddimensioningadataintensivecloudapplicationforsoccerplayertracking AT tokalaszlo optimizinganddimensioningadataintensivecloudapplicationforsoccerplayertracking |