Resource Allocation for Autonomic Data Centers using Analytic Performance Models
By Mohamed N. Bennani, D. Menascé. International Conference on Automation and Computing, 2005
434 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/5cc602e6502e6e58df01069ecc647f95ac2d9ebe
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
- Julie Ann McCann
- Markus C. Huebscher
- Rolf Stadler
- Brendan Jennings
- Rajarshi Das
- G. Tesauro
- Nicholas K. Jong
- Li Zhang
In Information Systems
- Julie Ann McCann
- Markus C. Huebscher
- Rolf Stadler
- Brendan Jennings
- Rajarshi Das
- G. Tesauro
- Nicholas K. Jong
- Li Zhang
Citations in the corpus
- A survey of autonomic computing—degrees, models, and applications (2008, background): “This if usually done with a utility function that quantifies the desirability of an application’s state as a function of the assigned resources [Bennani and Menasce 2005].”
- Efficient resource provisioning in compute clouds via VM multiplexing (2010, methodology): “Other work focus on short-term, dynamic provisioning techniques [20, 18, 15].”
- A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation (2006, methodology): “Following [16], [17], [18], [19], we reestimate the parameters of our models at the end of each allocation interval t, based on measurements of various state variables such as the mean arrival rate 2t, the mean response time Rt, the total number of servers allocated nt, and the number of customers M”
- Resource Management in Clouds: Survey and Research Challenges (2015, background): “Examples of earlier work focussing on demand profiling for web applications include: Chandra et al. [42], who present a prediction algorithm that estimates the workload parameters of applications based on time series analysis of request arrivals (which are modeled as an AR(1) process [33]); Bennani ”
- On the use of hybrid reinforcement learning for autonomic resource allocation (2007, methodology): “To bootstrap our hybrid RL approach with reasonable initial policies we adopt some of the recently proposed modelbased approaches for online performance management and resource allocation [30–33].”