Opportunistic admission and resource allocation for slicing enhanced IoT networks  

作　　者：Long Zhang Bin Cao Gang Feng 

机构地区：[1]National Key Laboratory of Science and Technology on Communications,University of Electronic Science and Technology of China,Chengdu,611731,China [2]State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing,100876,China

出　　处：《Digital Communications and Networks》2023年第6期1465-1476,共12页数字通信与网络（英文版）

基　　金：This work was supported in part by the Chongqing Technological Innovation and Application Development Projects under Grant cstc2019jscx-msxm1322,in part by the Zhejiang Lab under Grant 2021KF0AB03;in part by the National Natural Science Foundation of China under Grant 62071091.

摘　　要：Network slicing is envisioned as one of the key techniques to meet the extremely diversified service requirements of the Internet of Things(IoT)as it provides an enhanced user experience and elastic resource configuration.In the context of slicing enhanced IoT networks,both the Service Provider(SP)and Infrastructure Provider(InP)face challenges of ensuring efficient slice construction and high profit in dynamic environments.These challenges arise from randomly generated and departed slice requests from end-users,uncertain resource availability,and multidimensional resource allocation.Admission and resource allocation for distinct demands of slice requests are the key issues in addressing these challenges and should be handled effectively in dynamic environments.To this end,we propose an Opportunistic Admission and Resource allocation(OAR)policy to deal with the issues of random slicing requests,uncertain resource availability,and heterogeneous multi-resources.The key idea of OAR is to allow the SP to decide whether to accept slice requests immediately or defer them according to the load and price of resources.To cope with the random slice requests and uncertain resource availability,we formulated this issue as a Markov Decision Process(MDP)to obtain the optimal admission policy,with the aim of maximizing the system reward.Furthermore,the buyer-seller game theory approach was adopted to realize the optimal resource allocation,while motivating each SP and InP to maximize their rewards.Our numerical results show that the proposed OAR policy can make reasonable decisions effectively and steadily,and outperforms the baseline schemes in terms of the system reward.

关 键 词：SLICE IOT Markov decision process Game theory Admission and resource allocation 

分 类 号：TN92[电子电信—通信与信息系统]