Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/EA294D54-5302-4702-96B3-788F482F12BE | - |
| Αναγνωριστικό | https://doi.org/10.1007/978-3-030-75075-6_35 | - |
| Αναγνωριστικό | https://link.springer.com/chapter/10.1007/978-3-030-75075-6_35 | - |
| Γλώσσα | en | - |
| Μέγεθος | 12 pages | en |
| Τίτλος | Smilax: statistical machine learning autoscaler agent for Apache Flink | en |
| Δημιουργός | Giannakopoulos Panagiotis | en |
| Δημιουργός | Γιαννακοπουλος Παναγιωτης | el |
| Δημιουργός | Petrakis Evripidis | en |
| Δημιουργός | Πετρακης Ευριπιδης | el |
| Εκδότης | Springer | en |
| Περίληψη | Smilax is a statistical machine learning autoscaler agent for applications running on Apache Flink. Smilax agent acts proactively by predicting the forthcoming workload in order to adjust the allocation of workers to the actual needs of an application ahead of time. During an online training phase, Smilax builds a model which maps the performance of the application to the minimum number of servers. During the work (optimal) phase, Smilax maintains the performance of the application within acceptable limits (i.e. defined in the form of SLAs) while minimizing the utilization of resources. The effectiveness of Smilax is assessed experimentally by running a data intensive fraud detection application. | en |
| Τύπος | Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Publication | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2023-07-04 | - |
| Ημερομηνία Δημοσίευσης | 2021 | - |
| Θεματική Κατηγορία | Machine learning autoscaler agent | en |
| Βιβλιογραφική Αναφορά | P. Giannakopoulos, and E. G. M. Petrakis, “Smilax: statistical machine learning autoscaler agent for Apache Flink,” in Advanced Information Networking and Applications, vol. 226, Lecture Notes in Networks and Systems, L. Barolli, I. Woungang, T. Enokido, Eds., Cham, Switzerland: Springer Nature, 2021, pp. 433–444, doi: 10.1007/978-3-030-75075-6_35. | en |
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