AI 中文总结
针对无服务器计算中大规模工作流部署的成本与时间优化问题，提出基于整数非线性规划的模型及分解策略，实验显示其比简单部署启发式算法平均提升10%。
AI 中文摘要
无服务器边缘计算已成为一种强大的云框架，支持执行大规模工作流，无需用户管理底层服务器和边缘设备。本研究旨在解决在大量不同现有服务器和边缘设备上部署此类工作流的挑战，以最小化用户的货币成本和工作流评估时间。为此，在数学框架中对工作流和云节点属性进行建模，进而将最优部署问题建模为非线性整数规划的新模型。为解决向更多云/边缘节点扩展以及节点属性分解知识的问题，提出一种新的分解策略。案例研究表明，该分解方法具有良好的扩展特性，相比简单部署启发式算法平均提升10%。
英文摘要
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.
CommentsThis work has been accepted for publication in IEEE Access
