Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book

Network-Wide Monitoring And Debugging

Modern networks can encompass over 100,000 servers. Managing such an extensive network with a diverse set of network policies has become more complicated with the introduction of programmable hardwares and distributed network functions. Furthermore, service level agreements (SLAs) require operators to maintain high performance and availability with low latencies. Therefore, it is crucial for operators to resolve any issues in networks quickly. The problems can occur at any layer of stack: network (load imbalance), data-plane (incorrect packet processing), control-plane (bugs in configuration) and the coordination among them. Unfortunately, existing debugging tools are not sufficient to monitor, analyze, or debug modern networks; either they lack visibility in the network, require manual analysis, or cannot check for some properties. These limitations arise from the outdated view of the networks, i.e., that we can look at a single component in isolation. In this thesis, we describe a new approach that looks at measuring, understanding, and debugging the network across devices and time. We also target modern stateful packet processing devices: programmable data-planes and distributed network functions as these becoming increasingly common part of the network. Our key insight is to leverage both in-network packet processing (to collect precise measurements) and out-of-network processing (to coordinate measurements and scale analytics). The resulting systems we design based on this approach can support testing and monitoring at the data center scale, and can handle stateful data in the network. We automate the collection and analysis of measurement data to save operator time and take a step towards self driving networks