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When managing wide-area networks, network architects must decide how tobalance multiple conflicting metrics, and ensure fair allocations to competingtraffic while prioritizing critical traffic. The state of practice poseschallenges since architects must precisely encode their intent into formaloptimization models using abstract notions such as utility functions, andad-hoc manually tuned knobs. In this paper, we present the first effort tosynthesize optimal network designs with indeterminate objectives using aninteractive program-synthesis-based approach. We make three contributions.First, we present comparative synthesis, an interactive synthesis frameworkwhich produces near-optimal programs (network designs) through two kinds ofqueries (Propose and Compare), without an objective explicitly given. Second,we develop the first learning algorithm for comparative synthesis in which avoting-guided learner picks the most informative query in each iteration. Wepresent theoretical analysis of the convergence rate of the algorithm. Third,we implemented Net10Q, a system based on our approach, and demonstrate itseffectiveness on four real-world network case studies using black-box oraclesand simulation experiments, as well as a pilot user study comprising networkresearchers and practitioners. Both theoretical and experimental results showthe promise of our approach.

Comparative Synthesis: Learning Near-Optimal Network Designs by Query