3458559
doi
10.5281/zenodo.3458559
oai:zenodo.org:3458559
user-eu
Garcia Leiva, Rafael
IMDEA Networks Institute, Madrid, Spain
Le Duc, Thang
Tieto AB, Umeå, Sweden and Department of Computing Science, Umeå University, Sweden
Svorobej, Sergej
IC4, Dublin City University Business School, Dublin, Ireland
Närvä, Linus
Tieto AB, Umeå, Sweden
Noya Mariño, Manuel
Linknovate Science SL, Spain
Willis, Peter
BT R&I, Ipswich, UK
Giannoutakis, Konstantinos M.
Information Technologies Institute, Centre for Research and Technology Hellas, Greece
Loomba, Radhika
Intel Labs Europe, Leixlip, Ireland
Humanes, Héctor
Sistemas Avanzados de Tecnología S.A. (SATEC), Madrid, Spain
López, Miguel Ángel
Sistemas Avanzados de Tecnología S.A. (SATEC), Madrid, Spain
Östberg, P-O
Department of Computing Science, Umeå University, Sweden
Casari, Paolo
IMDEA Networks Institute, Madrid, Spain
Domaschka, Jörg
Institute of Information Resource Management, Ulm University, Ulm, Germany
RECAP Artificial Data Traces
Leznik, Mark
Institute of Information Resource Management, Ulm University, Ulm, Germany
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
artificial data
workload
time series
distributed networks
cloud computing
generative data
data modelling
server utilization
<p>The objective of the work package "Data Collection, Visualization and Analysis" of RECAP is to provide the necessary tools for managing and refining the data needed for the rest of the work packages. This includes the collection as well as the generation of data.</p>
<p>Within this work package, the task of Artificial Workload Generation is responsible for the generation of a collection of datasets with artificial workloads, that complement the real data traces collected from industrial partners. Moreover, because publicly available workload data is scarce we provide the data as public data sets.</p>
<p>This document is a companion report to deliverable which is of type “dataset”. The aim of the report is to describe the collection of datasets that constitute D5.3 and the mathematical techniques (structural time series models, generative adversarial networks, and workload based on traffic propagation) by which one can artificially generate and/or augment such datasets.</p>
<p>The datasets described include real data traces collected by industrial partners and artificial data traces generated by the use of statistical models and neural networks. Each published data set can be used by the scientific and industrial community as a starting point for the modelling and experimental validation of distributed edge and cloud applications, facilitating the repeatability of the results.</p>
<p> </p>
For details on the data itself, see RECAP-Artificial Workload Generation.pdf
Zenodo
2019-10-01
info:eu-repo/semantics/other
3458558
user-eu
1.0
award_title=Reliable Capacity Provisioning and Enhanced Remediation for Distributed Cloud Applications; award_number=732667; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/732667; funder_id=00k4n6c32; funder_name=European Commission;
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