Energy-Efficient Service Placement for Latency-Sensitive Applications in Edge Computing

被引:12
|
作者
Premsankar, Gopika [1 ]
Ghaddar, Bissan [2 ]
机构
[1] Univ Helsinki, Dept Comp Sci, Helsinki 00560, Finland
[2] Western Univ, Ivey Business Sch, London, ON N6G 0N1, Canada
来源
IEEE INTERNET OF THINGS JOURNAL | 2022年 / 9卷 / 18期
基金
加拿大自然科学与工程研究理事会; 芬兰科学院;
关键词
Deep neural network (DNN) model placement; edge computing; optimization; service placement; DATA-INTENSIVE APPLICATIONS; DELIVERY; INTERNET;
D O I
10.1109/JIOT.2022.3162581
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Edge computing is a promising solution to host artificial intelligence (AI) applications that enable real-time insights on user-generated and device-generated data. This requires edge computing resources (storage and compute) to be widely deployed close to end devices. Such edge deployments require a large amount of energy to run as edge resources are typically overprovisioned to flexibly meet the needs of time-varying user demand with a low latency. Moreover, AI applications rely on deep neural network (DNN) models that are increasingly larger in size to support high accuracy. These DNN models must be efficiently stored and transferred, so as to minimize their energy consumption. In this article, we model the problem of energy-efficient placement of services (namely, DNN models) for AI applications as a multiperiod optimization problem. The formulation jointly places services and schedules requests such that the overall energy consumption is minimized and latency is low. We propose a heuristic that efficiently solves the problem while taking into account the impact of placing services across time periods. We assess the quality of the proposed heuristic by comparing its solution to a lower bound of the problem, obtained by formulating and solving a Lagrangian relaxation of the original problem. Extensive simulations show that our proposed heuristic outperforms baseline approaches in achieving a low energy consumption by packing services on a minimal number of edge nodes, while at the same time keeping the average latency of served requests below a configured threshold in nearly all time periods.
引用
收藏
页码:17926 / 17937
页数:12
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