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

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), in a way that precludes request duplication attacks by malicious (Byzantine) clients, by rotating the assignment of a partitioned request hash space to leaders.
  As this mechanism removes a 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 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.
  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

Test Submission

In the past few years, serverless computing has gained significant popularity and became a go-to choice for deploying cloud applications and micro-services. Serverless computing 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 serverless 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 serverless platforms that can help developers with deploying and configuring their serverless applications. Second, we discuss the distinct performance and cost benefits of serverless computing and present a set of potential applications that can leverage the performance, cost, or both aspects of serverless computing. In the end, we discuss future research directions for serverless computing and suggest building tools and technologies, which would not only make serverless usage efficient but will also accelerate serverless adoption.

SoK: Serverless Computing: From An Application Developer's Perspective

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.

Solution: Donut Paxos: A Reconfigurable Consensus Protocol

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.

SURFBoard: Reproducible Performance Analysis for Distributed Machine Learning Workflows

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.

JSYS Submissions

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

Solution: SRB: SCALABLE AND ROBUST BFT FOR DECENTRALIZED NETWORKS

Test Submission

SoK: Serverless Computing: From An Application Developer's Perspective

Solution: Donut Paxos: A Reconfigurable Consensus Protocol

SURFBoard: Reproducible Performance Analysis for Distributed Machine Learning Workflows

SoK: A Generalized Multi-Leader State Machine Replication Tutorial

Test Submission