Auto-scaling to minimize cost and meet application deadlines in cloud workflows

By Ming Mao, Marty A. Humphrey. 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2011

626 citations recorded by the source corpus.

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Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds (2012, background): “Our work is also different from cloud-targeted autoscaling solution [14] in that we consider ensembles of workflows rather than unpredictable workloads containing workflows.”
Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds (2014, methodology): “For example, Mao and Humphrey propose a dynamic approach for scheduling workflow ensembles on clouds [12].”
Cloud auto-scaling with deadline and budget constraints (2010, methodology): “This dissertation uses the deadline assignment and load vector [29][49][55] techniques to determine the size of the resource pool, i.”
Big Data and cloud computing: innovation opportunities and challenges (2017, background): “…al. 2014), resource auto-and intelligent-scaling (Röme 2010; Jam et al. 2013; Xia et al. 2015b), location-aware smart job scheduling and workflow (Mao and Humphrey 2011; Lorido-Botrán, Miguel-Alonso, and Lozano 2012; Li et al. 2015b; Gui et al. 2016) and hybrid cloud solutions (Shen et al. 2011;…”
A Performance Study on the VM Startup Time in the Cloud (2012, background): “This information is particularly important to the time-critical applications that rely on the cloud's on-demand resources and is crucial to the cloud auto-scaling mechanisms to make correct decisions [7][8].”