XFaaS:混合云上FaaS工作流的跨平台编排

Aakash Khochare, Tuhin Khare, Varad Kulkarni, Yogesh L. Simmhan
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引用次数: 0

摘要

功能即服务(FaaS)由于其简单的抽象、易于部署、轻松扩展和细粒度计费而在公共云编程中越来越受欢迎。云提供商还提供了将这些功能组合到工作流中的基本功能。然而,FaaS和FaaS工作流模型是每个云提供商专有的。这阻碍了它们在云提供商之间的可移植性,并且需要努力设计在不同云提供商或数据中心上运行的工作流。这些要求对于满足监管要求、利用成本套利和避免供应商锁定越来越重要。此外,FaaS执行模型也不同,由于消息间接和冷启动导致的FaaS工作流开销需要针对不同的平台进行自定义优化。在本文中,我们提出了XFaaS,这是一个跨平台的部署和编排引擎,用于在多个云上运行FaaS工作流。XFaaS通过自动生成必要的代码包装器、云队列以及与云提供商的本机FaaS引擎协调,允许跨AWS和Azure云的功能和工作流的“零接触”部署。它还使用智能功能融合和放置逻辑来减少混合云中的工作流执行延迟,同时降低成本,使用基于详细基准的特定于提供商的性能和计费模型。我们的经验结果表明,在最佳情况下,与基线相比,融合提供了高达约75%的延迟优势和≈57%的成本降低,而放置策略将延迟降低了约24%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
XFaaS: Cross-platform Orchestration of FaaS Workflows on Hybrid Clouds
Functions as a Service (FaaS) have gained popularity for programming public clouds due to their simple abstraction, ease of deployment, effortless scaling and granular billing. Cloud providers also offer basic capabilities to compose these functions into workflows. FaaS and FaaS workflow models, however, are proprietary to each cloud provider. This prevents their portability across cloud providers, and requires effort to design workflows that run on different cloud providers or data centers. Such requirements are increasingly important to meet regulatory requirements, leverage cost arbitrage and avoid vendor lock-in. Further, the FaaS execution models are also different, and the overheads of FaaS workflows due to message indirection and cold-starts need custom optimizations for different platforms. In this paper, we propose XFaaS, a cross-platform deployment and orchestration engine for FaaS workflows to operate on multiple clouds. XFaaS allows “zero touch” deployment of functions and workflows across AWS and Azure clouds by automatically generating the necessary code wrappers, cloud queues, and coordinating with the native FaaS engine of the cloud providers. It also uses intelligent function fusion and placement logic to reduce the workflow execution latency in a hybrid cloud while mitigating costs, using performance and billing models specific to the providers based in detailed benchmarks. Our empirical results indicate that fusion offers up to ≈75 % benefits in latency and ≈57% reduction in cost, while placement strategies reduce the latency by ≈ 24%, compared to baselines in the best cases.
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