Cloud auto-scaling with deadline and budget constraints
By Ming Mao, Jie Li, Marty A. Humphrey. IEEE/ACM International Conference on Grid Computing, 2010
377 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/c1a3ff5e54afe439cd3f748fb32f11ecd64401ff
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Rajkumar Buyya
- Marty A. Humphrey
- Ming Mao
- Rodrigo Neves Calheiros
- R. Ranjan
- Anton Beloglazov
- Blesson Varghese
- Cheol-Ho Hong
In Information Systems
- Rajkumar Buyya
- Rodrigo Neves Calheiros
- R. Ranjan
- Marty A. Humphrey
- Ming Mao
- Anton Beloglazov
- Blesson Varghese
- Nikolay Grozev
Citations in the corpus
- 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].”
- Auto-scaling to minimize cost and meet application deadlines in cloud workflows (2011, methodology): “In our previous research [3], we explored the cloud auto-scaling problem with deadline and budget constraints for a batch-queue application model.”
- Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications’ QoS (2015, background): “Therefore, time windows as short as 10 minutes could be suitable depending on the selected cloud provider [19].”