DNS misconfiguration can result in severe social and financial consequences. Existing DNS configuration verification tools employ a centralized architecture, where all zone files are collected for verification. This architecture faces significant scalability issues (e.g., the verifier becoming the performance bottleneck and not supporting incremental verification). Inspired by the recent proposal of distributed data plane verification and the resemblance between the network data plane and DNS configuration, we propose to rearchitect DNS configuration verification with a distributed design. Our key insight is that by analyzing the query processing behavior of each DNS zone file in parallel and stitching the results in a symbolic way, we can substantially scale up the verification of DNS configuration. Evaluation shows that an up to 9.51× speed up on a dataset with over 410,000 resource records while having small overhead.

All existing coflow scheduling algorithms compute dynamic-rate schedules that change the rates of flows during transmission. In this paper, we make a crucial finding: although dynamically adjusting the rates of flows could lead to a better coflow completion time (CCT) in theory, it would introduce additional pressures on the congestion control mechanism in the underlying network, which result in poor CCT in practice. This difference between theoretical CCT and practical CCT is further exacerbated in wide-area networks, where the topology does not provide any bisection guarantee as data center networks do. To this end, we designed a fixed-rate coflow scheduling policy called FSCO. Although in theory, the best fixed-rate schedule is usually suboptimal, it keeps the in-flight traffic relatively steady, reducing the risk of triggering congestion control. The core of FSCO is an efficient scheduling algorithm based on the classic network utilization maximization (NUM) framework. We implement a prototype of FSCO and evaluate its performance extensively using real-world topologies and coflow traces. Experimental results show that the total CCT reduces up to 30% compared to baselines while yielding up to 12× speedups compared to the solver.