Large-scale workflow placement in serverless computing using integer nonlinear programming

Abstract

Serverless edge computing has become a powerful cloud framework that enables the execution of large workflows without the need for the user to manage the underlying servers and edge devices. In this work, we address the challenge of deploying these workflows on a large number of different existing servers and edge devices such that monetary costs for the users and workflow evaluation times are minimized. To this end, the workflow and cloud node attributes are modeled in a mathematical framework. As a result, we present a novel model of the optimal placement problem as a nonlinear integer program. To solve both the issues of scaling towards a larger number of cloud/edge nodes as well as decomposed knowledge of node attributes, we propose a novel decomposition strategy. In a case study, we show the beneficial scaling properties of the decomposition approach and a mean improvement of 10% against a simple deployment heuristic.

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