Marcin Copik

Function-as-a-Service (FaaS) is one of the most promising directions for the future of cloud services, and serverless functions have immediately become a new middleware for building scalable and cost-efficient microservices and applications. However, the quickly moving technology hinders reproducibility, and the lack of a standardized benchmarking suite leads to ad-hoc solutions and microbenchmarks being used in serverless research, further complicating metaanalysis and comparison of research solutions. To address this challenge, we propose the Serverless Benchmark Suite: the first benchmark for FaaS computing that systematically covers a wide spectrum of cloud resources and applications. Our benchmark consists of the specification of representative workloads, the accompanying implementation and evaluation infrastructure, and the evaluation methodology that facilitates reproducibility and enables interpretability. We demonstrate that the abstract model of a FaaS execution environment ensures the applicability of our benchmark to multiple commercial providers such as AWS, Azure, and Google Cloud. Our work facilities experimental evaluation of serverless systems, and delivers a standardized, reliable and evolving evaluation methodology of performance, efficiency, scalability and reliability of middleware FaaS platforms.

FaaS (Function-as-a-Service) brought a fundamental shift into cloud computing: (persistent) virtual machines have been replaced with dynamically allocated resources, trading locality and statefulness for a pay-as-you-go model more suitable for varying and infrequent workloads. However, adapting services to functions in the serverless paradigm while still fulfilling functional requirements is challenging. In this work, we demonstrate how ZooKeeper, a centralized coordination service that offers a safe and wait-free consensus mechanism, can be redesigned to benefit from serverless computing. We define synchronization primitives to extend the capabilities of scalable cloud storage and contribute a set of requirements for efficient and scalable FaaS computing. We introduce FaaSKeeper, the first coordination service built on serverless functions and cloud-native services, and share serverless design lessons based on our experiences of implementing a ZooKeeper model deployable to clouds today. FaaSKeeper provides the same consistency guarantees and interface as ZooKeeper, with a serverless price model that lowers costs up to 450 times on infrequent workloads.

Serverless functions provide elastic scaling and a fine-grained billing model, making Function-as-a-Service (FaaS) an attractive programming model. However, for distributed jobs that benefit from large-scale and dynamic parallelism, the lack of fast and cheap communication is a major limitation. Individual functions cannot communicate directly, group operations do not exist, and users resort to manual implementations of storage-based communication. This results in communication times multiple orders of magnitude slower than those found in HPC systems. We overcome this limitation and present the FaaS Message Interface (FMI). FMI is an easy-to-use, high-performance framework for general-purpose point-to-point and collective communication in FaaS applications. We support different communication channels and offer a model-driven channel selection according to performance and cost expectations. We model the interface after MPI and show that message passing can be integrated into serverless applications with minor changes, providing portable communication closer to that offered by high-performance systems. In our experiments, FMI can speed up communication for a distributed machine learning FaaS application by up to 162x, while simultaneously reducing cost by up to 397 times.

High performance is needed in many computing systems, from batch-managed supercomputers to general-purpose cloud platforms. However, scientific clusters lack elastic parallelism, while clouds cannot offer competitive costs for high-performance applications. In this work, we investigate how modern cloud programming paradigms can bring the elasticity needed to allocate idle resources, decreasing computation costs and improving overall data center efficiency. Function-as-a-Service (FaaS) brings the pay-as-you-go execution of stateless functions, but its performance characteristics cannot match coarse-grained cloud and cluster allocations. To make serverless computing viable for high-performance and latency-sensitive applications, we present rFaaS, an RDMA-accelerated FaaS platform. We identify critical limitations of serverless - centralized scheduling and inefficient network transport - and improve the FaaS architecture with allocation leases and microsecond invocations. We show that our remote functions add only negligible overhead on top of the fastest available networks, and we decrease the execution latency by orders of magnitude compared to contemporary FaaS systems. Furthermore, we demonstrate the performance of rFaaS by evaluating real-world FaaS benchmarks and parallel applications. Overall, our results show that new allocation policies and remote memory access help FaaS applications achieve high performance and bring serverless computing to HPC.