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A Novel Method for the Synthetic Generation of Non-I.I.D Workloads for Cloud Data Centers
Date
2020-07-01
Author
Koltuk, Furkan
Schmidt, Şenan Ece
Metadata
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© 2020 IEEE.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.
Subject Keywords
cloud computing
,
distribution fitting
,
model-based workload generation
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85094164200&origin=inward
https://hdl.handle.net/11511/99887
DOI
https://doi.org/10.1109/iscc50000.2020.9219577
Conference Name
2020 IEEE Symposium on Computers and Communications, ISCC 2020
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
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F. Koltuk and Ş. E. Schmidt, “A Novel Method for the Synthetic Generation of Non-I.I.D Workloads for Cloud Data Centers,” Rennes, Fransa, 2020, vol. 2020-July, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85094164200&origin=inward.