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Date
2023Type
- Conference Paper
ETH Bibliography
yes
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Abstract
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 highperformance 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-aService (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. Show more
Publication status
publishedExternal links
Book title
2023 IEEE International Parallel and Distributed Processing SymposiumPages / Article No.
Publisher
IEEEEvent
Subject
Serverless; Function-as-a-Service; Function-as-a-Service; RDMAOrganisational unit
03950 - Hoefler, Torsten / Hoefler, Torsten
Funding
801039 - Exascale Programming Models for Heterogeneous Systems (EC)
955776 - Network Solution for Exascale Architectures (EC)
170415 - Automatic Performance Modeling of HPC Applications with Multiple Model Parameters (SNF)
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ETH Bibliography
yes
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