A Clustering-based Consistency Adaptation Strategy for Distributed SDN Controllers

TL;DR

This paper proposes a clustering-based adaptation strategy for distributed SDN controllers that dynamically tunes consistency levels to meet application performance needs, improving synchronization efficiency.

Contribution

It introduces a novel clustering-based method to map application performance indicators to tunable consistency levels in distributed SDN controllers.

Findings

Sequential k-means achieves low RMSE with >= 50 clusters.

Incremental k-means also achieves low RMSE with similar clusters.

The strategy effectively adapts consistency levels to application performance requirements.

Abstract

Distributed controllers are oftentimes used in large-scale SDN deployments where they run a myriad of network applications simultaneously. Such applications could have different consistency and availability preferences. These controllers need to communicate via east/west interfaces in order to synchronize their state information. The consistency and the availability of the distributed state information are governed by an underlying consistency model. Earlier, we suggested the use of adaptively-consistent controllers that can autonomously tune their consistency parameters in order to meet the performance requirements of a certain application. In this paper, we examine the feasibility of employing adaptive controllers that are built on-top of tunable consistency models similar to that of Apache Cassandra. We present an adaptation strategy that uses clustering techniques (sequential k-means and incremental k-means) in order to map a given application performance indicator into a feasible consistency level that can be used with the underlying tunable consistency model. In the cases that we modeled and tested, our results show that in the case of sequential k-means, with a reasonable number of clusters (>= 50), a plausible mapping (low RMSE) could be estimated between the application performance indicators and the consistency level indicator. In the case of incremental k-means, the results also showed that a plausible mapping (low RMSE) could be estimated using a similar number of clusters (>= 50) by using a small threshold (~$ 0.01).