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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Main Authors: Koltuk, Furkan, Schmidt, Ece Guran
Format: Conference Proceeding
Language:English
Subjects:
cloud computing
Computers
Data centers
Data models
distribution fitting
Fitting
Internet
model-based workload generation
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Schmidt, Ece Guran
description 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.
doi_str_mv 10.1109/ISCC50000.2020.9219577
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subjects cloud computing
Computers
Data centers
Data models
distribution fitting
Fitting
Internet
model-based workload generation
title A Novel Method for the Synthetic Generation of Non-I.I.D Workloads for Cloud Data Centers
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