# Response Time and Availability Study of RAFT Consensus in Distributed SDN Control Plane

## TL;DR

This paper provides an analytical study of the RAFT consensus algorithm in SDN control planes, focusing on response time and availability, using stochastic modeling and real-world experiments to evaluate different cluster organizations.

## Contribution

It introduces a framework for numerical analysis of RAFT in SDN controllers, modeling response time and availability, and proposes a fast rejuvenation mechanism to improve performance and robustness.

## Key findings

- RAFT's response time is significantly affected by cluster organization.
- Fast rejuvenation reduces response time and increases system availability.
- Real-world parameters enable realistic performance modeling.

## Abstract

Software defined networking (SDN) promises unprecedented flexibility and ease of network operations. While flexibility is an important factor when leveraging advantages of a new technology, critical infrastructure networks also have stringent requirements on network robustness and control plane delays. Robustness in the SDN control plane is realized by deploying multiple distributed controllers, formed into clusters for durability and fast-failover purposes. However, the effect of the controller clustering on the total system response time is not well investigated in current literature. Hence, in this work we provide a detailed analytical study of the distributed consensus algorithm RAFT, implemented in OpenDaylight and ONOS SDN controller platforms. In those controllers, RAFT implements the data-store replication, leader election after controller failures and controller state recovery on successful repairs. To evaluate its performance, we introduce a framework for numerical analysis of various SDN cluster organizations w.r.t. their response time and availability metrics. We use Stochastic Activity Networks for modeling the RAFT operations, failure injection and cluster recovery processes, and using real-world experiments, we collect the rate parameters to provide realistic inputs for a representative cluster recovery model. We also show how a fast rejuvenation mechanism for the treatment of failures induced by software errors can minimize the total response time experienced by the controller clients, while guaranteeing a higher system availability in the long-term.