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Modeling the Impact of Workload on Cloud Resource Scaling.

, , , , and . SBAC-PAD, page 310-317. IEEE Computer Society, (2014)

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Adaptive, Model-driven Autoscaling for Cloud Applications., , , , and . ICAC, page 57-64. USENIX Association, (2014)Application Performance Management in Virtualized Server Environments., , , and . NOMS, page 373-381. IEEE, (2006)Management issues in storage area networks: detection and isolation of performance problems., , , and . NOMS (1), page 453-466. IEEE, (2004)VMAR: Optimizing I/O Performance and Resource Utilization in the Cloud., , , , , , , and . Middleware, volume 8275 of Lecture Notes in Computer Science, page 183-203. Springer, (2013)Managing responsiveness of virtual desktops using passive monitoring., , and . Integrated Network Management, page 319-326. IEEE, (2009)Towards efficient on-demand VM provisioning: Study of VM runtime I/O access patterns to shared image content., , and . IM, page 321-329. IEEE, (2015)MLscale: A machine learning based application-agnostic autoscaler., , , and . Sustain. Comput. Informatics Syst., (2019)On Impact of Dynamic Virtual Machine Reallocation on Data Center Efficiency.. MASCOTS, page 13-20. IEEE Computer Society, (2008)Model-Driven Autoscaling for Hadoop Clusters., , , and . ICAC, page 155-156. IEEE Computer Society, (2015)Evaluation of Redundancy Driven Provisioning for Hypervisors with Locally Attached Storage., and . MASCOTS, page 441-443. IEEE Computer Society, (2011)