Workload characterization and synthesis for cloud using generative stochastic processes
Author:
Funder
LinkedIn
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Information Systems,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s11227-022-04597-y.pdf
Reference31 articles.
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3. Mishra A, Hellerstein J, Cirne W, Das C (2010) Towards characterizing cloud backend workloads: insights from google compute clusters. ACM SIGMETRICS Perform Eval Rev 37(4):34–41. https://doi.org/10.1145/1773394.1773400
4. Patel E, Kushwaha DS (2020) Clustering cloud workloads: K-Means vs Gaussian mixture model. Procedia Computer Sci 171:158–167. https://doi.org/10.1016/j.procs.2020.04.017
5. Blei D, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022
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