Dataset Open Access

RECAP Artificial Data Traces

Leznik, Mark; Garcia Leiva, Rafael; Le Duc, Thang; Svorobej, Sergej; Närvä, Linus; Noya Mariño, Manuel; Willis, Peter; Giannoutakis, Konstantinos M.; Loomba, Radhika; Humanes, Héctor; López, Miguel Ángel; Östberg, P-O; Casari, Paolo; Domaschka, Jörg

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.

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.

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.

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.

 

For details on the data itself, see RECAP-Artificial Workload Generation.pdf
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RECAP-Artificial Workload Generation.pdf
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