When managing wide-area networks, network architects must decide how to balance multiple conflicting metrics, and ensure fair allocations to competing traffic while prioritizing critical traffic. The state of practice poses challenges since architects must precisely encode their intent into formal optimization models using abstract notions such as utility functions, and ad-hoc manually tuned knobs. In this paper, we present the first effort to synthesize optimal network designs with indeterminate objectives using an interactive program-synthesis-based approach. We make three contributions. First, we present comparative synthesis, an interactive synthesis framework which produces near-optimal programs (network designs) through two kinds of queries (Validate and Compare), without an objective explicitly given. Second, we develop the first learning algorithm for comparative synthesis in which a voting-guided learner picks the most informative query in each iteration. We present theoretical analysis of the convergence rate of the algorithm. Third, we implemented Net10Q, a system based on our approach, and demonstrate its effectiveness on four real-world network case studies using black-box oracles and simulation experiments, as well as a pilot user study comprising network researchers and practitioners. Both theoretical and experimental results show the promise of our approach.

Traversals are commonly seen in tree data structures, and performance-enhancing transformations between tree traversals are critical for many applications. Existing approaches to reasoning about tree traversals and their transformations are ad hoc, with various limitations on the classes of traversals they can handle, the granularity of dependence analysis, and the types of possible transformations. We propose Retreet, a framework in which one can describe general recursive tree traversals, precisely represent iterations, schedules and dependences, and automatically check data-race-freeness and transformation correctness. The crux of the framework is a stack-based representation for iterations and an encoding to Monadic Second-Order (MSO) logic over trees. Experiments show that Retreet can automatically verify optimizations for complex traversals on real-world data structures, such as CSS and cycletrees, which are not possible before. Our framework is also integrated with other MSO-based analysis techniques to verify even more challenging program transformations.

Syntax-guided synthesis (SyGuS) aims to find a program satisfying semantic specification as well as user-provided structural hypotheses. There are two main synthesis approaches: enumerative synthesis, which repeatedly enumerates possible candidate programs and checks their correctness, and deductive synthesis, which leverages a symbolic procedure to construct implementations from specifications. Neither approach is strictly better than the other: automated deductive synthesis is usually very efficient but only works for special grammars or applications; enumerative synthesis is very generally applicable but limited in scalability.

While the networking community has extensively tackled network design problems using optimization or other techniques (e.g., in areas such as traffic-engineering, and resource allocation), much of this work focuses on efficiently generating designs assuming well-defined objectives. In this paper, we argue that in practice, the objectives of a network design task may not be easy to specify for an architect. We argue for, and present a structured approach where the objectives of a network design task are learnt through iterative interactions with the architect. Our approach is inspired by a programming-by-examples approach that has seen success in the programming languages community. However, conventional program synthesis techniques do not apply because in our context a user can only provide a relative comparison between multiple choices on which one is more desirable, rather than provide an exact output for a given input. We propose a novel comparative synthesis approach to tackle these challenges. We sketch the approach, present promising preliminary results, and discuss future research questions.