Abstract
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.