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Software-defined wide area networking (SD-WAN) enables dynamic network policy control over a large distributed network via network updates . To be practical, network updates must be consistent (i.e., free of transient errors caused by updates to multiple switches), secure (i.e., only be executed when sent from valid controllers), and reliable (i.e., function despite the presence of faulty or malicious members in the control plane), while imposing only minimal overhead on controllers and switches. We present SERENE: a protocol for se cure and re liable ne twork updates for SD-WAN environments. In short: Consistency is provided through the combination of an update scheduler and a distributed transactional protocol. Security is preserved by authenticating network events and updates, the latter with an adaptive threshold cryptographic scheme. Reliability is provided by replicating the control plane and making it resilient to a dynamic adversary by using a distributed ledger as a controller failure detector. We ensure practicality by providing a mechanism for scalability through the definition of independent network domains and exploiting the parallelism of network updates both within and across domains. We formally define SERENE’s protocol and prove its safety with regards to event-linearizability. Extensive experiments show that SERENE imposes minimal switch burden and scales to large networks running multiple network applications all requiring concurrent network updates, imposing at worst a 16% overhead on short-lived flow completion and negligible overhead on anticipated normal workloads. 

This paper presents a formulation of multiparty session types (MPSTs) for practical fault-tolerant distributed programming. We tackle the challenges faced by session types in the context of distributed systems involving asynchronous and concurrent partial failures – such as supporting dynamic replacement of failed parties and retrying failed protocol segments in an ongoing multiparty session – in the presence of unreliable failure detection. Key to our approach is that we develop a novel model of event-driven concurrency for multiparty sessions. Inspired by real-world practices, it enables us to unify the session-typed handling of regular I/O events with failure handling and the combination of features needed to express practical fault-tolerant protocols. Moreover, the characteristics of our model allow us to prove a global progress property for well-typed processes engaged in multiple concurrent sessions, which does not hold in traditional MPST systems. To demonstrate its practicality, we implement our framework as a toolchain and runtime for Scala, and use it to specify and implement a session-typed version of the cluster management system of the industrial-strength Apache Spark data analytics framework. Our session-typed cluster manager composes with other vanilla Spark components to give a functioning Spark runtime; e.g., it can execute existing third-party Spark applications without code modification. A performance evaluation using the TPC-H benchmark shows our prototype implementation incurs an average overhead below 10%. 

In this paper, we present RoCC, a robust congestion control approach for datacenter networks based on RDMA. RoCC leverages switch queue size as an input to a PI controller, which computes the fair data rate of flows in the queue, signaling it to the flow sources. The PI parameters are self-tuning to guarantee stability, rapid convergence, and fair and near-optimal throughput in a wide range of congestion scenarios. Our simulation and DPDK implementation results show that RoCC can achieve up to 7× reduction in PFC frames generated under high average load levels, compared to DCQCN. At the same time, RoCC can achieve up to 8× lower tail latency, compared to DCQCN and HPCC. We also find that RoCC does not require PFC. The functional components of RoCC are implementable in P4-based and fixed-function switch ASICs. 

Developers are always on the lookout for simple solutions to manage their applications on cloud platforms. Major cloud providers have already been offering automatic elasticity management solutions (e.g., AWS Lambda, Azure durable function) to users. However, many cloud applications are stateful --- while executing, functions need to share their state with others. Providing elasticity for such stateful functions is much more challenging, as a deployment/elasticity decision for a stateful entity can strongly affect others in ways which are hard to predict without any application knowledge. Existing solutions either only support stateless applications (e.g., AWS Lambda) or only provide limited elasticity management (e.g., Azure durable function) to stateful applications. PLASMA (Programmable Elasticity for Stateful Cloud Computing Applications) is a programming framework for elastic stateful cloud applications. It includes (1) an elasticity programming language as a second "level" of programming (complementing the main application programming language) for describing elasticity behavior, and (2) a novel semantics-aware elasticity management runtime that tracks program execution and acts upon application features as suggested by elasticity behavior. We have implemented 10+ applications with PLASMA. Extensive evaluation on Amazon AWS shows that PLASMA significantly improves their efficiency, e.g., achieving same performance as a vanilla setup with 25% fewer resources, or improving performance by 40% compared to the default setup.