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Authors: Andreas Tsagkaropoulos 1 ; Nikos Papageorgiou 1 ; Dimitris Apostolou 2 ; Yiannis Verginadis 1 and Gregoris Mentzas 1

Affiliations: 1 National Technical University of Athens (NTUA), Greece ; 2 National Technical University of Athens (NTUA) and University of Piraeus, Greece

Keyword(s): Cloud Adaptivity, Fog Computing.

Abstract: Mainstream cloud technologies are challenged by real-time, big data processing requirements or emerging applications. This paper surveys recent research efforts on advancing cloud computing virtual infrastructures and real-time big data technologies in order to provide dynamically scalable and distributed architectures over federated clouds. We examine new methods for developing cloud systems operating in a real-time, big data environment that can sense the context of the application environment and can adapt the infrastructure accordingly. We describe research topics linked to the challenge of adaptivity such as situation awareness, context detection, service-level objectives, and the capability to predict extraordinary situations requiring remedying action. We also describe research directions for realising adaptivity in cloud computing and we present a conceptual framework that represents research directions and shows interrelations.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Tsagkaropoulos, A.; Papageorgiou, N.; Apostolou, D.; Verginadis, Y. and Mentzas, G. (2018). Challenges and Research Directions in Big Data-driven Cloud Adaptivity. In Proceedings of the 8th International Conference on Cloud Computing and Services Science - CLOSER; ISBN 978-989-758-295-0; ISSN 2184-5042, SciTePress, pages 190-200. DOI: 10.5220/0006761901900200

@conference{closer18,
author={Andreas Tsagkaropoulos. and Nikos Papageorgiou. and Dimitris Apostolou. and Yiannis Verginadis. and Gregoris Mentzas.},
title={Challenges and Research Directions in Big Data-driven Cloud Adaptivity},
booktitle={Proceedings of the 8th International Conference on Cloud Computing and Services Science - CLOSER},
year={2018},
pages={190-200},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006761901900200},
isbn={978-989-758-295-0},
issn={2184-5042},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Cloud Computing and Services Science - CLOSER
TI - Challenges and Research Directions in Big Data-driven Cloud Adaptivity
SN - 978-989-758-295-0
IS - 2184-5042
AU - Tsagkaropoulos, A.
AU - Papageorgiou, N.
AU - Apostolou, D.
AU - Verginadis, Y.
AU - Mentzas, G.
PY - 2018
SP - 190
EP - 200
DO - 10.5220/0006761901900200
PB - SciTePress

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