Cost Optimization for the Edge-Cloud Continuum by Energy-Aware Workload Placement

Rickard Brännvall, Tina Stark, Jonas Gustafsson, Mats Eriksson, J. Summers
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Abstract

This article investigates the problem of where to place the computation workload in an edge-cloud network topology considering the trade-off between the location-specific cost of computation and data communication. For this purpose, a Monte Carlo simulation model is defined that accounts for different workload types, their distribution across time and location, as well as correlation structure. Results confirm and quantify the intuition that optimization can be achieved by distributing a part of cloud computation to make efficient use of resources in an edge data center network, with operational energy savings of 4–6% and up to 50% reduction in its claim for cloud capacity.
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基于能量感知工作负载配置的边缘云连续体成本优化
考虑到特定位置的计算成本和数据通信之间的权衡,本文研究了在边缘云网络拓扑中将计算工作负载放在何处的问题。为此,定义了一个蒙特卡罗仿真模型,该模型考虑了不同的工作负载类型、它们在时间和地点上的分布以及相关结构。结果证实并量化了这样一种直觉,即可以通过分配一部分云计算来实现优化,从而有效利用边缘数据中心网络中的资源,从而节省4-6%的运营能源,并减少高达50%的云容量要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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