Reservation of Virtualized Resources with Optimistic Online Learning

被引:1
|
作者
Monteil, Jean-Baptiste [1 ]
Iosifidis, George [2 ]
Dusparic, Ivana [1 ]
机构
[1] Trinity Coll Dublin, Sch Comp Sci & Stat, Dublin, Ireland
[2] Delft Univ Technol, Delft, Netherlands
来源
ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS | 2023年
基金
中国国家自然科学基金; 爱尔兰科学基金会;
关键词
Online convex optimization; network slicing markets; virtualization; resource reservation; SP utility maximization; FTRL algorithm; 5G NETWORKS; ALLOCATION;
D O I
10.1109/ICC45041.2023.10279145
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
The virtualization of wireless networks enables new services to access network resources made available by the Network Operator (NO) through a Network Slicing market. The different service providers (SPs) have the opportunity to lease the network resources from the NO to constitute slices that address the demand of their specific network service. The goal of any SP is to maximize its service utility and minimize costs from leasing resources while facing uncertainties of the prices of the resources and the users' demand. In this paper, we propose a solution that allows the SP to decide its online reservation policy, which aims to maximize its service utility and minimize its cost of reservation simultaneously. We design the Optimistic Online Learning for Reservation (OOLR) solution, a decision algorithm built upon the Follow-the-Regularized Leader (FTRL), that incorporates key predictions to assist the decision-making process. Our solution achieves a O(root T) regret bound where T represents the horizon. We integrate a prediction model into the OOLR solution and we demonstrate through numerical results the efficacy of the combined models' solution against the FTRL baseline.
引用
收藏
页码:5147 / 5153
页数:7
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