A Clustering-based Consistency Adaptation Strategy for Distributed SDN Controllers

Abstract

Distributed controllers are oftentimes used in large-scale SDN deployments where they simultaneously run a myriad of network applications each possibly having different consistency and availability preferences. Those controllers need to communicate in order to synchronize their state information. The consistency and the availability of the distributed state information are governed by an underlying consistency model. In earlier work, 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 online clustering techniques (sequential and incremental k-means) in order to map a given application performance indicator ($\chi$) into a feasible consistency level ($\Phi$) that can be used with the underlying tunable consistency model. In the cases that we modeled and tested, our results showed that a plausible mapping (low RMSE) could be estimated between the application performance ($\chi$) and the consistency level ($\Phi$) indicators using the clustering techniques.