An adaptive scaling mechanism for managing performance variations in network functions virtualization: A case study in an NFV-based EPC | IEEE Conference Publication | IEEE Xplore

An adaptive scaling mechanism for managing performance variations in network functions virtualization: A case study in an NFV-based EPC

Publisher: IEEE

Abstract:

The scaling is a fundamental task that allows addressing performance variations in Network Functions Virtualization (NFV). In the literature, several approaches propose s...View more

Abstract:

The scaling is a fundamental task that allows addressing performance variations in Network Functions Virtualization (NFV). In the literature, several approaches propose scaling mechanisms that differ in the utilized technique (e.g., reactive, predictive and machine learning-based). The scaling in NFV must be accurate both at the time and the number of instances to be scaled, aiming at avoiding unnecessary procedures of provisioning and releasing of resources; however, achieving a high accuracy is a non-trivial task. In this paper, we propose for NFV an adaptive scaling mechanism based on Q-Learning and Gaussian Processes that are utilized by an agent to carry out an improvement strategy of a scaling policy, and therefore, to make better decisions for managing performance variations. We evaluate our mechanism by simulations, in a case study in a virtualized Evolved Packet Core, corroborating that it is more accurate than approaches based on static threshold rules and Q-Learning without a policy improvement strategy.
Date of Conference: 26-30 November 2017
Date Added to IEEE Xplore: 15 January 2018
ISBN Information:
Electronic ISSN: 2165-963X
Publisher: IEEE
Conference Location: Tokyo, Japan

References

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