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

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

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 2021 Mar Papers Submissions

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

Solution: Matchmaker 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