Network-wide Configuration Synthesis

Computer networks are hard to manage. Given a set of high-level requirements (e.g., reachability, security), operators have to manually figure out the individual configuration of potentially hundreds of devices running complex distributed protocols so that they, collectively, compute a compatible forwarding state. Not surprisingly, operators often make mistakes which lead to downtimes. To address this problem, we present a novel synthesis approach that automatically computes correct network configurations that comply with the operator's requirements. We capture the behavior of existing routers along with the distributed protocols they run in stratified Datalog. Our key insight is to reduce the problem of finding correct input configurations to the task of synthesizing inputs for a stratified Datalog program. To solve this synthesis task, we introduce a new algorithm that synthesizes inputs for stratified Datalog programs. This algorithm is applicable beyond the domain of networks. We leverage our synthesis algorithm to construct the first network-wide configuration synthesis system, called SyNET, that support multiple interacting routing protocols (OSPF and BGP) and static routes. We show that our system is practical and can infer correct input configurations, in a reasonable amount time, for networks of realistic size (> 50 routers) that forward packets for multiple traffic classes.Comment: 24 Pages, short version published in CAV 201

Model generation for horn logic with stratified negation

Abstract. Model generation is an important formal technique for finding interesting instances of computationally hard problems. In this paper we study model generation over Horn logic under the closed world assumption extended with stratified negation. We provide a novel threestage algorithm that solves this problem: First, we reduce the relevant Horn clauses to a set of non-monotonic predicates. Second, we apply a fixed-point procedure to these predicates that reveals candidate solutions to the model generation problem. Third, we encode these candidates into a satisfiability problem that is evaluated with a state-of-the-art SMT solver. Our algorithm is implemented, and has been successfully applied to key problems arising in model-based design.