Using Quantile Regression for Reclaiming Unused Cloud Resources while achieving SLA - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2018

Using Quantile Regression for Reclaiming Unused Cloud Resources while achieving SLA

Abstract

Although Cloud computing techniques have reduced the total cost of ownership thanks to virtualization, the average usage of resources (e.g., CPU, RAM, Network, I/O) remains low. To address such issue, one may sell unused resources. Such a solution requires the Cloud provider to determine the resources available and estimate their future use to provide availability guarantees. This paper proposes a technique that uses machine learning algorithms (Random Forest, Gradient Boosting Decision Tree, and Long Short Term Memory) to forecast 24-hour of available resources at the host level. Our technique relies on the use of quantile regression to provide a flexible trade-off between the potential amount of resources to reclaim and the risk of SLA violations. In addition, several metrics (e.g., CPU, RAM, disk, network) were predicted to provide exhaustive availability guarantees. Our methodology was evaluated by relying on four in production data center traces and our results show that quantile regression is relevant to reclaim unused resources. Our approach may increase the amount of savings up to 20% compared to traditional approaches.
Fichier principal
Vignette du fichier
Using_Quantile_Regression_for_Reclaiming_Unused_Cloud_Resources_with_SLA_Guarantees.pdf (1.55 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01898438 , version 1 (18-10-2018)

Identifiers

Cite

Jean-Emile Dartois, Anas Knefati, Jalil Boukhobza, Olivier Barais. Using Quantile Regression for Reclaiming Unused Cloud Resources while achieving SLA. CloudCom 2018 - 10th IEEE International Conference on Cloud Computing Technology and Science, Dec 2018, Nicosia, Cyprus. pp.89-98, ⟨10.1109/CloudCom2018.2018.00030⟩. ⟨hal-01898438⟩
406 View
519 Download

Altmetric

Share

Gmail Facebook X LinkedIn More