Fast Deployment of Reliable Distributed Control Planes With Performance Guarantees

Abstract

Current trends strongly indicate a transition towards large-scale programmable networks with virtual network functions. In such a setting, deployment of distributed control planes will be vital for guaranteed service availability and performance. Moreover, deployment strategies need to be completed quickly in order to respond flexibly to varying network conditions. We propose an effective optimization approach that automatically decides on the needed number of controllers, their locations, control regions, and traffic routes into a plan which fulfills control flow reliability and routability requirements, including bandwidth and delay bounds. The approach is also fast: the algorithms for bandwidth and delay bounds can reduce the running time at the level of 50x and 500x, respectively, compared to state-of-the-art and direct solvers such as CPLEX. Altogether, our results indicate that computing a deployment plan adhering to predetermined performance requirements over network topologies of various sizes can be produced in seconds and minutes, rather than hours and days. Such fast allocation of resources that guarantees reliable connectivity and service quality is fundamental for elastic and efficient use of network resources. INDEX TERMS Software-defined networking, distributed control plane, controller placement problem, latency, reliability, routability, optimization. V K possible ways to map them on the network. For each mapping, there are K V possible ways of defining control regions.

Demo Abstract: Elastic Deployment of Robust Distributed Control Planes with Performance Guarantees

“…3 outlines the main steps of the demo, here exemplified by the use of the DataXchange topology [6] chosen for visualization simplicity. Note that the framework scales well with significantly larger networksin [1] we showed that a deployment plan for the larger Inter-netMCI [6] topology can be retrieved within around 22s, which is 80x faster than when using CPLEX. A provisional video of the demo can be found online at https://bit.ly/36hnOWc.…”

“…The overall approach is a generic black-box optimization framework consisting of four main steps [1], [2]. We assume a programmable network consisting of commodity hardware, where networking nodes can be a controller and/or aggregator (a.k.a.…”

“…1d). Finally, the routability-check step verifies whether all control flows can be scheduled or not, given (bandwidth and latency) requirements [1], [2]. Input to the framework are the topology description, traffic patterns in the network and a set of optimization parameters.…”

“…In general, it is NPhard. Most DCP research prior to [1], [2] has mainly focused on i). However, assuring routability is key for guaranteeing optimal DCP deployment, especially since control-traffic must share resources with other network traffic, such as time-critical services.…”

“…We presented a novel optimisation approach in [1]- [3], which solves both i) and ii) extremely fast. The running time to guarantee routability in line with set performance requirements can for mid-sized and large topologies be reduced to minutes (and seconds) while still producing close to optimal solutions [1], in contrast to hours and days when using direct solvers such as CPLEX. In practice, this is a fundamental step towards enabling DCP elasticity, which is crucial for effective utilization of networked infrastructure resources and service connectivity.…”