KneeScale: Efficient Resource Scaling for Serverless Computing at the Edge
KneeScale: Efficient Resource Scaling for Serverless Computing at the Edge
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DOI：
10.1109/ccgrid54584.2022.00027
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Xue Li;Peng Kang;Jordan Molone;W. Wang;P. Lama
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文献类型：
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作者：
Xue Li;Peng Kang;Jordan Molone;W. Wang;P. Lama
无服务器计算是向网络边缘的物联网(IoT)应用提供服务的一种很有前途的模式。它的事件触发计算，以及细粒度和灵活的资源扩展，非常适合资源受限的边缘计算环境。然而，在云设置中占主导地位的通用自动缩放器在Edge的无服务器计算中表现不佳。这主要是因为在资源预算限制和动态工作负载下难以快速确定最优资源分配。在本文中，我们提出了一个自适应自动伸缩器，KneScale，它动态地调整无服务器功能的副本数量，以达到增加资源分配的相对成本不再值得相应的性能好处的点。我们将KneScale设计并实现为使用Kubernetes进行资源管理的轻量级系统软件。在功能即服务(FAAS)基准测试FunetionBeneh和开源无服务器计算平台OpenFaaS上的实验结果表明，KneScale具有优越的性能和资源效率。在给定的资源预算下，在累积性能方面，它分别比Kubernetes Horizative Pod AutoScaler(HPA)和OpenFaaS内置调度器高出32%和106%。对于各种无服务器功能，KneScale实现了比这两种竞争技术更高的累计吞吐量，比OpenFaaS内置调度器更低的延迟，以及与HPA类似的延迟。
Serverless computing is a promising paradigm for delivering services to the Internet of Things (IoT) applications at the edge of the network. Its event-triggered computation, as well as fine-grained and agile resource scaling, is well-suited for a resource-constrained edge computing environment. However, general-purpose auto-scalers that are predominant in the cloud settings perform poorly for serverless computing at the Edge. This is mainly due to the difficulty in quickly determining the optimal resource allocation under resource-budget constraints and dynamic workloads. In this paper, we present an adaptive auto-scaler, KneeScale, that dynamically adjusts the number of replicas for serverless functions to reach a point at which the relative cost to increase resource allocation is no longer worth the corresponding performance benefit. We have designed and implemented KneeScale as lightweight system software that utilizes Kubernetes for resource management. Experimental results with a function-as-a-service (FaaS) benchmark, FunetionBeneh, and an open-source serverless computing platform, OpenFaaS, demonstrate the superior performance and resource efficiency of KneeScale. It outperforms Kubernetes Horizontal Pod AutoScaler (HPA) and OpenFaaS built-in scheduler in terms of cumulative performance under a given resource budget by up to 32 % and 106 % respectively. KneeScale achieves higher cumulative throughput than both competing techniques, lower latencies than OpenFaaS built-in scheduler, and similar latencies compared to HPA for a variety of serverless functions.