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Choreographic programming (CP) is a paradigm for implementing distributed systems that uses a single global program to define the actions and interactions of all participants. Library-level CP implementations, like HasChor, integrate well with mainstream programming languages but have several limitations: Their conditionals require extra communication; they require specific host-language features (e.g., monads); and they lack support for programming patterns that are essential for implementing realistic distributed applications. We make three contributions to library-level CP to specifically address these challenges. First, we propose and formalizeconclavesandmultiply-located values, which enable efficient conditionals in library-level CP without redundant communication. Second, we proposecensus polymorphism, a technique for abstracting over the number of participants in a choreography. Third, we introduce a design pattern for library-level CP in host languages without support for monads. We demonstrate these contributions via implementations in Haskell, Rust, and TypeScript. 

Software-defined networking (SDN) in conjunction with programmable switches revolutionizes network management, yet crafting optimal switch configurations remains complex. Traditional P4 optimizations rely on data plane level tuning. In this paper, we argue an essential piece for such optimizations is the control plane itself. We present P4CGO, a P4 compilation framework which focuses on realizing specifications based on control policies. P4CGO leverages user-defined objective functions and control plane policies to guide P4 program optimization through table merging and splitting. We have prototyped P4CGO and applied it solving real-world policy optimization problems. 

Online traffic classification enables critical applications such as network intrusion detection and prevention, providing Quality-of-Service, and real-time IoT analytics. However, with increasing network speeds, it has become extremely challenging to analyze and classify traffic online. In this paper, we present Leo, a system for online traffic classification at multi-terabit line rates. At its core, Leo implements an online machine learning (ML) model for traffic classification, namely the decision tree, in the network switch's data plane. Leo's design is fast (can classify packets at switch's line rate), scalable (can automatically select a resource-efficient design for the class of decision tree models a user wants to support), and runtime programmable (the model can be updated on-the-fly without switch downtime), while achieving high model accuracy. We implement Leo on top of Intel Tofino switches. Our evaluations show that Leo is able to classify traffic at line rate with nominal latency overhead, can scale to model sizes more than twice as large as state-of-the-art data plane ML classification systems, while achieving classification accuracy on-par with an offline traffic classifier. 

The open-source and community-supported gem5 simulator is one of the most popular tools for computer architecture research. This simulation infrastructure allows researchers to model modern computer hardware at the cycle level, and it has enough fidelity to boot unmodified Linux-based operating systems and run full applications for multiple architectures including x86, Arm, and RISC-V. The gem5 simulator has been under active development over the last nine years since the original gem5 release. In this time, there have been over 7500 commits to the codebase from over 250 unique contributors which have improved the simulator by adding new features, fixing bugs, and increasing the code quality. In this paper, we give and overview of gem5's usage and features, describe the current state of the gem5 simulator, and enumerate the major changes since the initial release of gem5. We also discuss how the gem5 simulator has transitioned to a formal governance model to enable continued improvement and community support for the next 20 years of computer architecture research.