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We develop FLM, a high-level language that enables network operators to write programs that recognize and react to specific packet sequences. To be able to examine every packet, our compilation procedure can transform FLM programs into P4 code that can run on programmable switch ASICs. It first splits FLM programs into a state management component and a classical regular expression, then generates an efficient implementation of the regular expression using SMT-based program synthesis. Our experiments find that FLM can express 15 sequence monitoring tasks drawn from prior literature. Our compiler can convert all of these programs to run on switch hardware in way that fit within available pipeline stages and consume less than 15% additional header fields and instruction words when run alongside switch programs. 

Packet drops caused by congestion are a fundamental problem in network operation. Yet, it is difficult to detect where drops are happening, let alone which flows are most affected. Detecting the small-timescale drops caused by short bursts of traffic is even more challenging, and traditional monitoring techniques can easily miss them. To uncover packet drops as they occur inside a switch, the analysis must be real-time, fine-grained, and efficient. However, modern switches have distributed packet-processing pipelines that see either the arriving or departing traffic, but not the packet drops. Plus, they do not have enough memory to store per-flow state. Our MIDST system addresses these challenges through a distributed compact data structure with lightweight coordination between ingress and egress pipelines. MIDST identifies the flows experiencing loss, as well as the bursty flows responsible, across different burst durations. Our evaluation with real-world traces and TCP connections shows that MIDST uses little memory (e.g., 320KB) while providing high accuracy (95% to 98%) under varying loss rates and burst durations. We evaluate a low-rate DDoS attack and demonstrate the potential use of our measurement results for attack detection and mitigation. 

Many promising networking research ideas in programmable networks never see the light of day. Yet, deploying research prototypes in production networks can help validate research ideas, improve them with faster feedback, uncover new research questions, and also ease the subsequent transition to practice. In this paper, we show how researchers can run and validate their research ideas in their own backyards---on their production campus networks---and we have seen that such a demonstrator can expedite the deployment of a research idea in practice to solve real network operation problems. We present P4Campus , a proof-of-concept that encompasses tools, an infrastructure design, strategies, and best practices---both technical and non-technical---that can help researchers run experiments against their programmable network idea in their own network. We use network tapping devices, packet brokers, and commodity programmable switches to enable running experiments to evaluate research ideas on a production campus network. We present several compelling data-plane applications as use cases that run on our campus and solve production network problems. By sharing our experiences and open-sourcing our P4 apps [28], we hope to encourage similar efforts on other campuses. 

We present Arbitrum, a cryptocurrency system that supports smart contracts without the limitations of scalability and privacy of systems previous systems such as Ethereum. Arbitrum, like Ethereum, allows parties to create smart contracts by using code to specify the behavior of a virtual machine (VM) that implements the contract’s functionality. Arbitrum uses mechanism design to incentivize parties to agree off-chain on what a VMwould do, so that the Arbitrum miners need only verify digital signatures to confirm that parties have agreed on a VM’s behavior. In the event that the parties cannot reach unanimous agreement off-chain, Arbitrum still allows honest parties to advance the VM state on-chain. If a party tries to lie about a VM’s behavior, the verifier (or miners) will identify and penalize the dishonest party by using a highly-efficient challenge-based protocol that exploits features of the Arbitrum virtual machine architecture. Moving the verification of VMs’ behavior off-chain in this way provides dramatic improvements in scalability and privacy. We describe Arbitrum’s protocol and virtual machine architecture, and we present a working prototype implementation.