RHINO: An Efficient Serverless Container System for Small-Scale HPC Applications

IF 5.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2025-06-04 DOI:10.1109/TPDS.2025.3576584

He Zhu;Mingyu Li;Haihang You

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引用次数: 0

Abstract

Serverless computing, characterized by its pay-as-you-go and auto-scaling features, offers a promising alternative for High Performance Computing (HPC) applications, as traditional HPC clusters often face long waiting times and resources over/under-provisioning. However, current serverless platforms struggle to support HPC applications due to restricted inter-function communication and high coupling runtime. To address these issues, we introduce RHINO, which offers end-to-end support for the development and deployment of serverless HPC. Using the Two-Step Adaptive Build strategy, the HPC code is packaged into lightweight, scalable functions. The Rhino Function Execution Model decouples HPC applications from the underlying infrastructures. The Auto-scaling Engine dynamically scales cloud resources and schedules tasks based on performance and cost requirements. We deploy RHINO on AWS Fargate and evaluate it on both benchmarks and real-world workloads. Experimental results show that, when compared to the traditional VM clusters, RHINO can achieve a performance improvement of 10% –30% for small-scale applications and more than 40% cost reduction.

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RHINO：用于小型高性能计算应用的高效无服务器容器系统

无服务器计算的特点是随用随付和自动扩展特性，它为高性能计算（HPC）应用程序提供了一个很有前途的替代方案，因为传统的HPC集群经常面临很长的等待时间和资源过剩/不足的问题。然而，由于受限制的功能间通信和高耦合运行时，当前的无服务器平台难以支持HPC应用程序。为了解决这些问题，我们引入了RHINO，它为无服务器HPC的开发和部署提供端到端的支持。使用两步自适应构建策略，HPC代码被打包成轻量级的、可扩展的函数。Rhino函数执行模型将HPC应用程序与底层基础设施解耦。自动缩放引擎根据性能和成本需求动态缩放云资源和调度任务。我们在AWS Fargate上部署了RHINO，并在基准测试和实际工作负载上对其进行了评估。实验结果表明，与传统VM集群相比，RHINO在小规模应用中性能提升10% -30%，成本降低40%以上。

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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.