Inter-Controller Traffic to Support Consistency in ONOS Clusters

Abstract

In distributed SDN architectures, the network is controlled by a cluster of multiple controllers. This distributed approach permits to meet the scalability and reliability requirements of large operational networks. Despite that, a logical centralized view of the network state should be guaranteed, enabling the simple development of network applications. Achieving a consistent network state requires a consensus protocol, which generates control traffic among the controllers whose timely delivery is crucial for network performance. We focus on the state-of-art ONOS controller, designed to scale to large networks, based on a cluster of self-coordinating controllers. In particular, we study the inter-controller control traffic due to the adopted consistency protocols. Based on real traffic measurements and the analysis of the adopted consistency protocols, we develop some empirical models to quantify the traffic exchanged among the controllers, depending on the considered shared data structures, the current network state (e.g. topology) and the occurring network events (e.g. flow or host addition). Our models provide a formal tool to be integrated into the design and dimension the control network interconnecting the controllers. Our results are of paramount importance for the proper design of large SDN networks, in which the control plane is implemented in-band and cannot exploit dedicated network resources. Index Terms-Software Defined Networking, ONOS controller, consistency protocols, inter-controller traffic measurements.

MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design

“…As discussed briefly in Section I, frequent dissemination of inter-controller synchronization messages introduce prohibitively large overheads [3], [4]. Moreover, excessive status updates can potentially lead to network performance degradation caused by instability [14].…”

“…The physically distributed controllers synchronize with each other to maintain a logically centralised network view, which is referred to as controller synchronization. Since complete synchronization among controllers, i.e., all controllers always maintain the same global view, will incur high costs especially in large networks [3], [4], most practical distributed SDN networks can only afford partial inter-controller synchronizations and allow temporary inconsistency in controllers' network view, which is known as the eventual consistency model [5].…”

“…. , N } by instantiating the MLP; Set initial state s0; t = 0; 2 Initialize matrix D with past (s, a, R(s, a) − ρι, s ) tuples;3 Initialize the delayed Q-functions θ i ← θi, i ∈ {1, . .…”

Learning the Optimal Synchronization Rates in Distributed SDN Control Architectures

“…. , τ }; 3 Estimate Ψ( x) = 1 τ τ t=1 Ψ t (x t ); 4 for each iteration k from 1 to B do 5 Pick σ random controller pairs p for which x p < R; 6 for each picked pair p from 1 to σ do 7 Set x = x where x p = x p + 1; 8 Try out x t = x and observe Ψ t (x t ) ∀t ∈ {(k−1)στ +pτ +1, . .…”

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

“…Muqaddas et al [5], [35] investigate the load overhead of the intra-cluster communication in a 2-and 3-controller ONOS cluster. They propose a model to quantify the traffic exchanged among the controllers and express it as a function of the network topology.…”