计算机科学
计算卸载
数学优化
分布式计算
双层优化
边缘计算
GSM演进的增强数据速率
服务(商务)
最优化问题
运筹学
计算机网络
人工智能
算法
经济
经济
工程类
数学
作者
Tarannum Nisha,Duong Tung Nguyen,V.K. Bhargava
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2022-09-15
卷期号:9 (18): 17280-17291
被引量:5
标识
DOI:10.1109/jiot.2022.3152139
摘要
The emerging edge computing (EC) paradigm promises to provide low latency and ubiquitous computation to numerous mobile and Internet of Things (IoT) devices at the network edge. How to efficiently allocate geographically distributed heterogeneous edge resources to a variety of services is a challenging task. While this problem has been studied extensively in recent years, most of the previous work has largely ignored the preferences of the services when making edge resource allocation decisions. To this end, this article introduces a novel bilevel optimization model, which explicitly takes the service preferences into consideration, to study the interaction between an EC platform and multiple services. The platform manages a set of edge nodes (ENs) and acts as the leader while the services are the followers. Given the service placement and resource pricing decisions of the leader, each service decides how to optimally divide its workload to different ENs. The proposed framework not only maximizes the profit of the platform but also minimizes the cost of every service. When there is a single EN, we derive a simple analytic solution for the underlying problem. For the general case with multiple ENs and multiple services, we present a Karush–Kuhn–Tucker-based solution and a duality-based solution, combining with a series of linearizations, to solve the bilevel problem. Extensive numerical results are shown to illustrate the efficacy of the proposed model.
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