HashCache: Accelerating Serverless Computing by Skipping Duplicated Function Execution

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

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2023-10-10 DOI:10.1109/TPDS.2023.3323330

Zhaorui Wu;Yuhui Deng;Yi Zhou;Lin Cui;Xiao Qin

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引用次数: 0

Abstract

Serverless computing is a leading force behind deploying and managing software in cloud computing. One inherent challenge in serverless computing is the increased overall latency due to duplicate computations. Our initial investigation into the function invocations of serverless applications reveals an abundance of duplicate invocations. Inspired by this critical observation, we introduce HashCache , a system designed to cache duplicate function invocations, thereby mitigating duplicate computations. In HashCache, serverless functions are classified into three categories, namely, computational functions, stateful functions, and environment-related functions. On the grounds of such a function classification, HashCache associates the stateful functions and their states to build an adaptive synchronization mechanism. With this support, HashCache exploits the cached results of computational and stateful functions to serve upcoming invocation requests to the same functions, thereby reducing duplicate computations. Moreover, HashCache stores remote files probed by stateful functions into a local cache layer, which further curtails invocation latency. We implement HashCache within the Apache OpenWhisk to forge a cache-enabled serverless computing platform. We conduct extensive experiments to quantitatively evaluate the performance of HashCache in terms of invocation latency and resource utilization. We compare HashCache against two state-of-the-art approaches - FaaSCache and OpenWhisk . The experimental results unveil that our HashCache remarkably reduces invocation latency and resource overhead. More specifically, HashCache curbs the 99-tail latency of FaaSCache and OpenWhisk by up to 91.37% and 95.96% in real-world serverless applications. HashCache also slashes the resource utilization of FaaSCache and OpenWhisk by up to 31.62% and 35.51%, respectively.

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HashCache：通过跳过重复函数执行加速无服务器计算

无服务器计算是云计算中部署和管理软件的主导力量。无服务器计算中的一个固有挑战是由于重复计算而增加的总延迟。我们对无服务器应用程序的函数调用的初步调查揭示了大量重复调用。受这一关键观察的启发，我们介绍了HashCache，这是一个旨在缓存重复函数调用的系统，从而减少重复计算。在HashCache中，无服务器函数分为三类，即计算函数、有状态函数和环境相关函数。在这种函数分类的基础上，HashCache将有状态函数与其状态相关联，构建了一种自适应的同步机制。有了这种支持，HashCache利用计算函数和有状态函数的缓存结果，为即将到来的对相同函数的调用请求提供服务，从而减少重复计算。此外，HashCache将有状态函数探测到的远程文件存储到本地缓存层，这进一步减少了调用延迟。我们在Apache OpenWhisk中实现了HashCache，以打造一个支持缓存的无服务器计算平台。我们进行了大量的实验来定量评估HashCache在调用延迟和资源利用率方面的性能。我们将HashCache与两种最先进的方法FaaSCache和OpenWhisk进行比较。实验结果表明，我们的HashCache显著降低了调用延迟和资源开销。更具体地说，在现实世界的无服务器应用程序中，HashCache将FaaSCache和OpenWhisk的99尾延迟分别控制了91.37%和95.96%。HashCache还将FaaSCache和OpenWhisk的资源利用率分别降低了31.62%和35.51%。

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.