This paper presents Flutter, the first Byzantine Total Order Broadcast implementation with a broadcast-to-delivery latency of $2Δ+ ε$ time units, $Δ$ being the message delay and $ε$ an arbitrarily small constant margin, when all processes are correct, the network is synchronous, hence local clocks are well-synchronized. Under the same conditions, state-of-the-art protocols require at least $3Δ$ time units in practical deployments where clients differ from servers. We prove Flutter's good-case latency is quasi-optimal, meaning it cannot be improved upon by any finite amount. Flutter is deterministic, leaderless, and signature-free hence quantum-resilient; it assumes partial synchrony and at least $5f + 1$ servers, where $f$ bounds the number of faults. Under the hood, Flutter builds upon Blink, a novel Binary Consensus implementation with Representative Validity, whose fast path enables decisions in $Δ$ time units when all correct servers propose the same value.

This paper introduces Carbon, a high-throughput system enabling asynchronous (safe) and consensus-free (efficient) payments and votes within a dynamic set of clients. Carbon is operated by a dynamic set of validators that may be reconfigured asynchronously, offering its clients eclipse resistance as well as lightweight bootstrap. Carbon offers clients the ability to select validators by voting them in and out of the system thanks to its novel asynchronous and stake-less voting mechanism. Carbon relies on an asynchronous and deterministic implementation of Byzantine reliable broadcast that uniquely leverages a permissionless set of untrusted servers, brokers, to slash the cost of client authentication inherent to Byzantine fault tolerant systems. Carbon is able to sustain a throughput of one million payments per second in a geo-distributed environment, outperforming the state of the art by three orders of magnitude with equivalent latencies.

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.

Eventual consistency is a consistency model that favors liveness over safety. It is often used in large-scale distributed systems where models ensuring a stronger safety incur performance that are too low to be deemed practical. Eventual consistency tends to be uniformly applied within a system, but we argue a demand exists for differentiated eventual consistency, e.g. in blockchain systems. We propose update-query consistency with primaries and secondaries (UPS) to address this demand. UPS is a novel consistency mechanism that works in pair with our novel two-phase epidemic broadcast protocol gossip primary-secondary (GPS) to offer differentiated eventual consistency and delivery speed. We propose two complementary analyses of the broadcast protocol: a continuous analysis and a discrete analysis based on compartmental models used in epidemiology. Additionally, we propose the formal definition of a scalable consistency metric to measure the consistency trade-off at runtime. We evaluate UPS in two simulated worldwide settings: a one-million-node network and a network emulating that of the Ethereum blockchain. In both settings, UPS reduces inconsistencies experienced by a majority of the nodes and reduces the average message latency for the remaining nodes.

Traditionally, the only option for developers was to implement file systems (FSs) via drivers within the operating system kernel. However, there exists a growing number of file systems (FSs), notably distributed FSs for the cloud, whose interfaces are implemented solely in user space to (i) isolate FS logic, (ii) take advantage of user space libraries, and/or (iii) for rapid FS prototyping. Common interfaces for implementing FSs in user space exist, but they do not guarantee POSIX compliance in all cases, or suffer from considerable performance penalties due to high amounts of wait context switchs between kernel and user space processes. We propose DEFUSE: an interface for user space FSs that provides fast accesses while ensuring access correctness and requiring no modifications to applications. DEFUSE: achieves significant performance improvements over existing user space FS interfaces thanks to its novel design that drastically reduces the number of wait context switchs for FS accesses. Additionally, to ensure access correctness, DEFUSE: maintains POSIX compliance for FS accesses thanks to three novel concepts of bypassed file descriptor (FD) lookup , FD stashing , and user space paging . Our evaluation spanning a variety of workloads shows that by reducing the number of wait context switchs per workload from as many as 16,000 or 41,000 with filesystem in user space down to 9 on average, DEFUSE: increases performance 2× over existing interfaces for typical workloads and by as many as 10× in certain instances.

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.

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 both consistent, i.e., free of transient errors caused by updates to multiple switches, and secure, i.e., free of errors caused by faulty or malicious members of the control plane. Besides, these properties must incur minimal overhead to controllers and switches.

A major challenge in writing applications that execute across hosts, such as distributed online services, is to reconcile (a) parallelism (i.e., allowing components to execute independently on disjoint tasks), and (b)cooperation (i.e., allowing components to work together on common tasks). A good compromise between the two is vital to scalability, a core concern in distributed networked applications. The actor model of computation is a widely promoted programming model for distributed applications, as actors can execute in individual threads (parallelism) across different hosts and interact via asynchronous message passing (collaboration). However, this makes it hard for programmers to reason about combinations of messages as opposed to individual messages, which is essential in many scenarios. This paper presents a pragmatic variant of the actor model in which messages can be grouped into units that are executed in a serializable manner, whilst still retaining a high degree of parallelism. In short, our model is based on an orchestration of actors along a directed acyclic graph that supports efficient decentralized synchronization among actors based on their actual interaction. We present the implementation of this model, based on a dynamic DAG-inducing referencing discipline, in the actor-based programming language AEON. We argue serializability and the absence of deadlocks in our model, and demonstrate its scalability and usability through extensive evaluation and case studies of wide-ranging applications.

Distributed ledgers are among the most replicated data repositories in the world. They offer data consistency, immutability, and auditability, based on the assumption that each participating node locally verifies their entire content. Although their content, currently extending up to a few hundred gigabytes, can be accommodated by dedicated commodity hard disks, downloading it, processing it, and storing it in general-purpose desktop and laptop computers can prove largely impractical. Even worse, this becomes a prohibitive restriction for smartphones, mobile devices, and resource-constrained IoT devices. In this demo, we present an implementation of Dietcoin, a Bitcoin protocol extension that allows nodes to perform secure local verification of Bitcoin transactions with small bandwidth and storage requirements. This demo presents and benchmarks the main features of Dietcoin that are important for today's cryptocurrencies and smart contract systems, but are missing in the current state-of-the-art: (i) allowing resource-constrained devices to verify the correctness of selected blocks locally without having to download the complete ledger; (ii) enabling devices to join a blockchain quickly yet securely, dropping bootstrap time from days down to a matter of seconds; (iii) providing a generic solution that can be applied to other distributed ledgers secured with Proof-of-Work.

Eventual consistency is a consistency model that emphasizes liveness over safety, it is often used for its ability to scale as distributed systems grow larger. Eventual consistency tends to be uniformly applied to an entire system, but we argue that there is a growing demand for differentiated eventual consistency requirements. We address this demand with UPS, a novel consistency mechanism that offers differentiated eventual consistency and delivery speed by working in pair with a two-phase epidemic broadcast protocol. We propose a closed-form analysis of our approach's delivery speed, and we evaluate our complete mechanism experimentally on a simulated network of one million nodes. To measure the consistency trade-off, we formally define a novel and scalable consistency metric that operates at runtime. In our simulations, UPS divides by more than 4 the inconsistencies experienced by a majority of the nodes, while reducing the average latency incurred by a small fraction of the nodes from 6 rounds down to 3 rounds.