A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments
By Tania Lorido-Botran, José Miguel-Alonso, José Antonio Lozano. Journal of Grid Computing, 2014
786 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/745c5d7e1459794200dbaccaf250f65dc4313236
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computer Networks and Communications
- Yahya Al-Dhuraibi
- Nabil Bachir Djarallah
- Fawaz Paraiso
- Philippe Merle
- Haoran Qiu
- John Wilkes
- Saurabh Jha
- Subho Sankar Banerjee
In Information Systems
- Yahya Al-Dhuraibi
- Nabil Bachir Djarallah
- Fawaz Paraiso
- Philippe Merle
- Haoran Qiu
- John Wilkes
- Saurabh Jha
- Subho Sankar Banerjee
Citations in the corpus
- Autopilot: workload autoscaling at Google (2020, background): “Autoscaling is a well-developed research area; recent surveys include [11, 16, 22].”
- FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices (2020, methodology): “Why RL? Existing performance-modeling-based [23, 38, 39,56,94,101,127] or heuristic-based approaches [6,7,37,65, 84] suffer from model reconstruction and retraining problems because they do not address dynamic system status.”
- Elasticity in Cloud Computing: State of the Art and Research Challenges (2018, background): “measurements taken at fixed or uniform intervals [151].”
- Swayam: distributed autoscaling to meet SLAs of machine learning inference services with resource efficiency (2017, background): “[22] survey a wide range of resource management systems, focusing on horizontal and vertical scaling, stateless and stateful services, single- and multitier infrastructure, etc.”