A Novel Method for the Synthetic Generation of Non-I.I.D Workloads for Cloud Data Centers

Cloud data center workloads have time- dependencies and are hence non-i.i.d (independent and identically distributed). In this paper, we propose a new model-based method for creating synthetic workload traces for cloud data centers that have similar time characteristics and cumulative distributions...

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Bibliographic Details
Published inProceedings - IEEE Symposium on Computers and Communications pp. 1 - 6
Main Authors Koltuk, Furkan, Schmidt, Ece Guran
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2020
Subjects
cloud computing
Computers
Data centers
Data models
distribution fitting
Fitting
Internet
model-based workload generation
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ISSN2642-7389
DOI10.1109/ISCC50000.2020.9219577

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Summary:Cloud data center workloads have time- dependencies and are hence non-i.i.d (independent and identically distributed). In this paper, we propose a new model-based method for creating synthetic workload traces for cloud data centers that have similar time characteristics and cumulative distributions to those of the actual traces. We evaluate our method using the actual resource request traces of Azure collected in 2019 and the well-known Google cloud trace. Our method enables generating synthetic traces that can be used for a more realistic evaluation of cloud data centers.
ISSN:2642-7389
DOI:10.1109/ISCC50000.2020.9219577