Tango: Simplifying SDN Control with Automatic Switch Property Inference, Abstraction, and Optimization

A major benefit of software-defined networking (SDN) over traditional networking is simpler and easier control of network devices. The diversity of SDN switch implementation properties, which include both diverse switch hardware capabilities and diverse control-plane software behaviors, however, can make it difficult to understand and/or to control the switches in an SDN network. In this paper, we present Tango, a novel framework to explore the issues of understanding and optimization of SDN control, in the presence of switch diversity. The basic idea of Tango is novel, simple, and yet quite powerful. In particular, different from all previous SDN control systems, which either ignore switch diversity or depend on that switches can and will report diverse switch implementation properties, Tango introduces a novel, proactive probing engine that infers key switch capabilities and behaviors, according to a well-structured set of Tango patterns, where a Tango pattern consists of a sequence of standard OpenFlow commands and a corresponding data traffic pattern. Utilizing the inference results from Tango patterns and additional application API hints, Tango conducts automatic switch control optimization, despite diverse switch capabilities and behaviors. Evaluating Tango on both hardware switches and emulated software switches, we show that Tango can infer flow table sizes, which are key switch implementation properties, within less than 5% of actual values, despite diverse switch caching algorithms, using a probing algorithm that is asymptotically optimal in terms of probing overhead. We demonstrate cases where routing and scheduling optimizations based on Tango improves the rule installation time by up to 70% in our hardware switch testbed.