Reinforcement-Learning-Assisted Service Function Chain Embedding Algorithm in Edge Computing Networks

被引:0
|
作者
Wang, Wei [1 ]
Chen, Shengpeng [2 ]
Zhang, Peiying [2 ,3 ]
Liu, Kai [4 ,5 ]
机构
[1] Guangzhou Panyu Polytech, Sch Informat Engn, Guangzhou 511483, Peoples R China
[2] China Univ Petr East China, Qingdao Inst Software, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China
[3] Qilu Univ Technol, Shandong Acad Sci, Key Lab Comp Power Network & Informat Secur, Minist Educ,Shandong Comp Sci Ctr,Natl Supercomp C, Jinan 250013, Peoples R China
[4] Tsinghua Univ, State Key Lab Space Network & Commun, Beijing 100084, Peoples R China
[5] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
edge computing; resource allocation; service function chaining; distributed reinforcement learning; RESOURCE-ALLOCATION;
D O I
10.3390/electronics13153007
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 networks are critical infrastructures for processing massive data and providing instantaneous services. However, how to efficiently allocate resources in edge computing networks to meet the embedding requirements of service function chains has become an urgent problem. In this paper, we model the resource allocation problem in edge computing networks as a service function chain embedding problem model, aiming to optimize the resource allocation through reinforcement learning algorithms to achieve the goals of low cost, high revenue, and high embedding rate. In this paper, the basic concepts of edge computing network and service function chain are elaborated, and the resource allocation problem is transformed into a service function chain embedding problem by establishing a mathematical model, which provides a foundation for the subsequent algorithm design. In this paper, a service function chain embedding algorithm based on reinforcement learning is designed to gradually optimize the resource allocation decision by simulating the learning process. In order to verify the effectiveness of the algorithm, a series of simulation experiments are conducted in this paper and compared with other algorithms. The experimental results show that the service function chain embedding algorithm based on reinforcement learning proposed in this paper exhibits superior performance in resource allocation. Compared with traditional resource allocation methods, the algorithm achieves significant improvement in terms of low cost, high revenue, and high embedding rate.
引用
收藏
页数:14
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