A Lagrangian-Relaxation-Based Approach for Service Function Chain Dynamic Orchestration for the Internet of Things

Liu, Yicen; Lü, Yu; Li, Xi; Li, Zhiwei; Qiao, Wenxin; Zhang, Yang; Zhao, Donghao

doi:10.1109/jiot.2021.3075563

Cited by 6 publications

(1 citation statement)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lagrangian relaxation is a well-known relaxation method to obtain a bound on difficult optimization problems [35]. It has been widely used in vehicle routing [36], water pump scheduling [37], and more recently in orchestration of services in edge networks [38]. In our formulation, constraint (2) links the time periods and complicates the problem.…”

Section: Lagrangian Relaxationmentioning

confidence: 99%

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

Premsankar

Ghaddar

2022

IEEE Internet Things J.

View full text Add to dashboard Cite

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.

show abstract