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

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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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Citation Formats

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.