  This paper presents SRB, a robust Byzantine fault-tolerant (BFT) total order broadcast protocol
  aimed at maximizing throughput on wide-area networks (WANs),
  targeting deployments in decentralized networks,
  such as permissioned and Proof-of-Stake permissionless blockchain systems.
 SRB is developed open-source and is planned to be integrated in a major permissioned blockchain project.

  SRB is the first BFT protocol that allows multiple leaders to propose request batches independently
  (i.e., parallel leaders), while effectively precluding performance degradation due to request duplication
  by rotating the assignment of a partitioned request hash space to leaders.
  As this mechanism removes the single-leader bandwidth bottleneck
  and exposes a computation bottleneck related to authenticating clients even on a WAN,
  our protocol further boosts throughput using a client signature verification sharding optimization.
  Our evaluation shows that SRB outperforms state-of-the-art
  single-leader protocols
  and orders more than 60000 signed Bitcoin-sized (500-byte) transactions per second
  on a widely distributed 100 nodes, 1 Gbps WAN setup, with typical latencies of few seconds.
  Moreover, our evaluation exposes the impact of duplicate requests on parallel leader protocols which SRB eliminates.
  We also evaluate SRB under different crash and Byzantine faults, demonstrating its performance robustness.

  SRB relies on classical BFT protocol constructs, which simplifies reasoning about its correctness.
  Specifically, SRB is a generalization of the celebrated and scrutinized PBFT protocol.
  In a nutshell, SRB follows PBFT ``safety-wise'', with changes needed to accommodate novel features restricted to PBFT liveness.

Solution: SRB: SCALABLE AND ROBUST BFT FOR DECENTRALIZED NETWORKS

MultiPaxos and Raft are the two most popular and widely deployed state machine replication protocols. There is a more sophisticated family of generalized multi-leader state machine replication protocols like EPaxos, Caesar, and Atlas that have better performance, but they are extremely complicated and hard to understand. Due to their complexity, they have seen little to no industry adoption, and academically there has been a lack of clarity in analyzing, comparing, and extending the protocols. This paper is a tutorial on generalized multi-leader protocols. We explain why the protocols work the way they do, what they have in common, where they differ, which parts of the protocols are straightforward, which are more subtle than they appear, and so on. In doing so, we present four new generalized multi-leader protocols, identify key insights into existing protocols, and taxonomize the space.

SoK: A Generalized Multi-Leader State Machine Replication Tutorial

State machine replication protocols, like MultiPaxos and Raft, are at the heart of numerous distributed systems. To tolerate machine failures, these protocols must replace failed machines with new machines, a process known as reconfiguration. Reconfiguration has become increasingly important over time as the need for frequent reconfiguration has grown. Despite this, reconfiguration has largely been neglected in the literature. In this paper, we present Donut Paxos and Donut MultiPaxos, a reconfigurable consensus and state machine replication protocol respectively. Our protocols can perform a reconfiguration with little to no impact on the latency or throughput of command processing; they can perform a reconfiguration in a few milliseconds; and they present a framework that can be generalized to other replication protocols in a way that previous reconfiguration techniques can not. We provide proofs of correctness for the protocols and
optimizations, and present empirical results from an open source implementation showing that throughput and latency do not change significantly during a reconfiguration.

Solution: Matchmaker Paxos: A Reconfigurable Consensus Protocol

In the past few years, FaaS has gained significant popularity and became a go-to choice for deploying cloud applications and micro-services. FaaS with its unique 'pay as you go' pricing model and key performance benefits over other cloud services, offers an easy and intuitive programming model to build cloud applications. In this model, a developer focuses on writing the code of the application while infrastructure management is left to the cloud provider who is responsible for the underlying resources, security, isolation, and scaling of the application. Recently, a number of commercial and open-source FaaS platforms have emerged, offering a wide range of features to application developers. In this paper, first, we present measurement studies demystifying various features and performance of commercial and open-source FaaS platforms that can help developers with deploying and configuring their serverless applications. Second, we discuss the distinct performance and cost benefits of FaaS and interesting use cases that leverage the performance, cost, or both aspects of FaaS. Lastly, we discuss challenges a developer may face while developing or deploying a serverless application. We also discuss state of the art solutions and open problems.

SoK: Function-as-a-Service: From An Application Developer's Perspective

When designing their performance evaluations, networking researchers often encounter questions such as: How long should a run be? How many runs to perform? How to account for the variability across multiple runs? What statistical methods should be used to analyze the data? Despite their best intentions, researchers often answer these questions differently, thus impairing the replicability of their  evaluations and the confidence in their results.  

In this paper, we propose a concrete methodology for the design and analysis of performance evaluations. Our approach  hierarchically partitions the performance evaluation into three timescales, following the principle of separation of concerns. The idea is to understand, for each timescale, the temporal characteristics of variability sources, and then to apply rigorous statistical methods to derive  performance results with quantifiable confidence in spite of the inherent variability. We implement this methodology in a software  framework called TriScale. For each performance metric, TriScale computes a variability score that estimates, with a given confidence, how similar the results would be if the evaluation were replicated; in other words, TriScale quantifies the replicability of evaluations. We showcase the practicality and usefulness of TriScale on four different case studies demonstrating that TriScale helps to generalize and strengthen published results. 

Improving the standards of replicability in networking is a complex challenge. This paper is an important  contribution to this endeavor; it provides networking researchers with a rational and concrete experimental methodology rooted in sound  statistical foundations. The first of its kind.

Tool: Designing Replicable Networking Experiments with TriScale

TCP is known to perform poorly in cellular network environments. Yet, most mobile applications are explicitly built on the conventional TCP stack or implicitly leverage TCP tunnels to various cellular middleboxes, including performance-enhancing proxies, application-specific edge proxies, VPN proxies and NAT boxes.  Despite significant advances in the design of new congestion control (CC) protocols for cellular networks, deploying these protocols without bypassing the underlying TCP tunnels has remained a challenging proposition.
    This paper proposes the design of a new \emph{Application Layer Congestion Control (ALCC)} framework that allows any new CC protocol to be implemented easily at the application layer, within or above an application-layer protocol that sits atop a legacy TCP stack. It drives it to deliver approximately the same as the native performance. The ALCC socket sits on top of a traditional TCP socket. Still, it can leverage the large congestion windows opened by TCP connections to carefully execute an application-level CC within the window bounds of the underlying TCP connection. This paper demonstrates how ALCC can be applied to three well-known cellular CC protocols: Verus, Copa, and Sprout. For these protocols, ALCC can achieve comparable throughput and delay characteristics (within 3-10\%) as the native protocols at the application layer across different networks and traffic conditions. ALCC allows a server-side implementation of these protocols with no client modifications and with zero bytes overhead. The ALCC framework can be easily integrated with off-the-shelf applications such as file transfers and video streaming.

ALCC: Migrating Congestion Control to the Application Layer in Cellular Networks

Test Submission

Large-scale HPC infrastructures are enablers for scientific research in many domains. The recent advances in machine learning (ML) have led to an ever increasing demand for computation power, as well as the design of complex operational workflows. Understanding the performance and efficiency of these workflows is key to productivity, knowledge and model sharing, and energy efficiency. Even though there have been efforts in studying and designing portability protocols, performance analysis of large-scale ML is still an expert-driven task, tightly locked-in to specific physical and software infrastructure. Much like in other domains, this hinders reproducibility of both results and overall workflow performance. To overcome this challenge, we propose the design of a container-based framework for reproducible performance analysis of ML workflows at scale. We validate our framework using a case-study on two different large-scale production systems running ML workflows. We show empirically that our containerized approach is portable and allows arbitrarily low-level performance evaluation when run on two different, production-based HPC clusters with hundreds of GPUs. We report our findings on widely-used open-source software stacks and datasets and offer practitioners insights into what types of analyses our framework enables. To benefit the community, we open-source our software and results.

JSYS Submissions

Solution: SRB: SCALABLE AND ROBUST BFT FOR DECENTRALIZED NETWORKS

SoK: A Generalized Multi-Leader State Machine Replication Tutorial

Solution: Matchmaker Paxos: A Reconfigurable Consensus Protocol

SoK: Function-as-a-Service: From An Application Developer's Perspective

Tool: Designing Replicable Networking Experiments with TriScale

ALCC: Migrating Congestion Control to the Application Layer in Cellular Networks

Test Submission

SURFBoard: Reproducible Performance Analysis for Distributed Machine Learning Workflows

Test Submission