FaaSDeliver: Cost-Efficient and QoS-Aware Function Delivery in Computing Continuum
Guangba Yu, Pengfei Chen, Zibin Zheng, Jingrun Zhang, Xiaoyun Li, Zilong He
Abstract
Serverless Function-as-a-Service (FaaS) is a rapidly growing computing paradigm in the cloud era. To provide rapid service response and save network bandwidth, traditional cloud-based FaaS platforms have been extended to the edge. However, launching functions in a heterogeneous computing continuum (HCC) that includes the cloud, fog, and the edge brings new challenges: determining <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">where functions should be delivered and how many resources should be allocated.</i> To optimize the cost of running functions in the HCC, we propose an adaptive and efficient function delivery engine, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FaaSDeliver</i> , which automatically unearths a cost-efficient function delivery policy (FDP) for each function, including the FaaS platform selection and resource allocation. Real system implementation and evaluations in a practical HCC demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FaaSDeliver</i> can unearth the most cost-efficient FDPs from among 180,200 FDPs after a few trials. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FaaSDeliver</i> reduces the average cost of function execution from 38% to 78% compared to some state-of-the-art approaches.