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publications

An Autoscaling Platform supporting Graph Data Modelling Big Data Analytics

Authors: Athanasios Kiourtis, Panagiotis Karamolegkos, Andreas Karabetian, Konstantinos Voulgaris, Yannis Poulakis, Argyro Mavrogiorgou, Dimosthenis Kyriazis

Published in: ICIMTH (2022)

This paper describes a domain-agnostic single access autoscaling Big Data analytics platform, named Diastema.

An Environmentally-sustainable Dimensioning Workbench towards Dynamic Resource Allocation in Cloud-computing Environments

Authors: Andreas Karabetian, Athanasios Kiourtis, Konstantinos Voulgaris, Panagiotis Karamolegkos, Yannis Poulakis, Argyro Mavrogiorgou, Dimosthenis Kyriazis

Published in: IISA (2022)

The aim of this paper is to present a solution on dynamic resource allocation for efficient cloud scalability.

A Comparison of Container Systems for Machine Learning Scenarios: Docker and Podman

Authors: Konstantinos Voulgaris, Athanasios Kiourtis, Andreas Karabetian, Panagiotis Karamolegkos, Yannis Poulakis, Argyro Mavrogiorgou, Dimosthenis Kyriazis

Published in: CompAuto (2022)

The aim of this paper is to compare two of the most popular container engines to see what differences exist in performance and architectural levels between the so-called “drop-in” replacements.

Diastema: Data-driven Stack for Big Data Applications Management and Deployment

Authors: Athanasios Kiourtis, Yannis Poulakis, Panagiotis Karamolegkos, Andreas Karabetian, Konstantinos Voulgaris, Argyro Mavrogiorgou, Dimosthenis Kyriazis

Published in: IJBDM (2023)

Diastema is a data-driven stack for big data applications management and deployment, bringing efficient data-as-a-service solutions.