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Pro-active Automatic scaling support for Apache Flink in Kubernetes in the Cloud

Zafeirakopoulos Alexandros-Nikolaos

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URIhttp://purl.tuc.gr/dl/dias/B015D2A5-35D3-44F0-ABBA-C6D07796F14C-
Identifierhttps://doi.org/10.26233/heallink.tuc.95102-
Languageen-
Extent1.8 megabytesen
Extent76 pagesen
TitlePro-active Automatic scaling support for Apache Flink in Kubernetes in the Clouden
TitleΈγκυρη υποστήριξη αυτόματης κλιμάκωσης του Apache Flink σε υποδομή Kubernetes στο υπολογιστικό νέφοςel
CreatorZafeirakopoulos Alexandros-Nikolaosen
CreatorΖαφειρακοπουλος Αλεξανδρος-Νικολαοςel
Contributor [Thesis Supervisor]Petrakis Evripidisen
Contributor [Thesis Supervisor]Πετρακης Ευριπιδηςel
Contributor [Committee Member]Samoladas Vasilisen
Contributor [Committee Member]Σαμολαδας Βασιληςel
Contributor [Committee Member]Zervakis Michailen
Contributor [Committee Member]Ζερβακης Μιχαηλel
PublisherΠολυτεχνείο Κρήτηςel
PublisherTechnical University of Creteen
Academic UnitTechnical University of Crete::School of Electrical and Computer Engineeringen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστώνel
DescriptionΠροπτυχιακή Διατριβή που υποβλήθηκε στη σχολή ΗΜΜΥ του Πολ. Κρήτης για την πλήρωση προϋποθέσεων λήψης του Προπτυχιακού Διπλώματος.el
Content SummaryApache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. It executes arbitrary dataflow programs in a data-parallel and pipelined manner in event-driven applications such as, fraud detection (i.e. detection of suspicious transactions), anomaly detection (i.e. detection of rare or suspicions events), rule-based alerting (i.e. identification of data which satisfy one or more rules) and many more. Despite its versatility, Apache Flink cannot automatically and optimally adjust the utilization of its underlying computing resources when streaming sources produce data at varying speeds. In order to address this issue, we describe an autonomous agent to support dynamic autoscaling for Apache Flink on Kubernetes. This agent monitors, models and adjusts Flink's behaviour by optimally modifying its allocated resources in order to match the incoming workload while achieving minimum cost. The decision making process is based on operator idleness and changes to the input's record lag. We prove that our model not only successfully maintains the performance of the application while minimizing infrastructure costs, but can provide a better performance-to-cost ratio compared to already existing work on Flink autoscaling. The effectiveness of our model is supported by an exhaustive set of synthetic and real life workloads aimed to simulate a plethora of possible scenarios.en
Type of ItemΔιπλωματική Εργασίαel
Type of ItemDiploma Worken
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-03-01-
Date of Publication2022-
SubjectAutoscaling Microservice Architectures in the Clouden
Bibliographic CitationAlexandros-Nikolaos Zafeirakopoulos, "Pro-active Automatic scaling support for Apache Flink in Kubernetes in the Cloud", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2022en
Bibliographic CitationΑλέξανδρος-Νικόλαος Ζαφειρακόπουλος, "Έγκυρη υποστήριξη αυτόματης κλιμάκωσης του Apache Flink σε υποδομή Kubernetes στο υπολογιστικό νέφος", Διπλωματική Εργασία, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2022el

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