HyFaaS: Accelerating Serverless Workflows by Unleashing Hybrid Resource Elasticity

IF 5.9 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2025-11-12 DOI:10.1109/TPDS.2025.3632089

Xiaofei Yue;Song Yang;Fan Li;Liehuang Zhu;Xu Wang;Zhen Feng;Fernando A. Kuipers

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引用次数: 0

Abstract

Serverless computing promises fine-grained resource elasticity and billing, making it an attractive way to build complex applications as multi-stage workflows. Nonetheless, existing workflow orchestration ignores the heterogeneous demands of the computation and communication parts within a stage, potentially resulting in resource inefficiency on either side. In this paper, we advocate for computation-communication-separated orchestration to unleash hybrid resource (i.e., compute and network) elasticity. We present HyFaaS, a serverless workflow orchestrator that improves performance while ensuring cost efficiency. It seamlessly decouples computation and communication as a series of hybrid stages re-expressed within HyDAG, a novel workflow abstraction. HyFaaS uses a gray-box profiling model to identify their Pareto-optimal saturated configurations, and then deploys the saturated workflow to juggle communication and scaling overheads through two-level HyDAG partitioning. Along with event-driven runtime fine-tuning, HyFaaS further scales down the non-critical stages to reduce cost via branch-aware coordination. Experimental results show that HyFaaS surpasses existing solutions by 32.7%–50.4% on end-to-end latency, while lowering cost by up to 1.37×.

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HyFaaS：通过释放混合资源弹性来加速无服务器工作流

无服务器计算保证了细粒度的资源弹性和计费，使其成为将复杂应用程序构建为多阶段工作流的一种有吸引力的方式。尽管如此，现有的工作流编排忽略了阶段内计算和通信部分的异构需求，这可能导致任何一方的资源效率低下。在本文中，我们提倡计算-通信分离的编排，以释放混合资源（即计算和网络）的弹性。我们介绍HyFaaS，这是一种无服务器工作流编排器，可在确保成本效率的同时提高性能。它将计算和通信无缝解耦，作为一系列混合阶段，在HyDAG中重新表达，这是一种新颖的工作流抽象。HyFaaS使用灰盒分析模型来识别其pareto最优饱和配置，然后部署饱和工作流，通过两级HyDAG分区来处理通信和扩展开销。随着事件驱动的运行时微调，HyFaaS进一步缩小了非关键阶段，通过分支感知协调来降低成本。实验结果表明，HyFaaS的端到端延迟比现有方案提高了32.7% ~ 50.4%，同时降低了1.37倍的成本。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Parallel and Distributed Systems 工程技术-工程：电子与电气

CiteScore

11.00

自引率

9.40%

发文量

281

审稿时长

5.6 months

期刊介绍： IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.