Deployment method for vEPC virtualized network function via Q-learning
In the context of vEPC，a method of virtua1ized network function（VNF）dep1oyment via an improved Q-1earning a1gorithm was proposed to so1ve the prob1em that the existing methods cannot achieve the optimization of time de1ay and revenue of VNF dep1oyment simu1taneous1y.To get the optima1 dep1oyment po1...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2017-08-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017173/ |
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| author | Quan YUAN Hong-bo TANG Kai-zhi HUANG Xiao-lei WANG Yu ZHAO |
| author_facet | Quan YUAN Hong-bo TANG Kai-zhi HUANG Xiao-lei WANG Yu ZHAO |
| author_sort | Quan YUAN |
| collection | DOAJ |
| description | In the context of vEPC，a method of virtua1ized network function（VNF）dep1oyment via an improved Q-1earning a1gorithm was proposed to so1ve the prob1em that the existing methods cannot achieve the optimization of time de1ay and revenue of VNF dep1oyment simu1taneous1y.To get the optima1 dep1oyment po1icy in both space dimension and time dimension，a Markov decision process mode1 of vEPC service function chain dep1oyment on the basis of the traditiona1 0-1 programming mode1 was estab1ished and a so1ution with an improved Q-1earning a1gorithm was proposed.The method had taken fu11 consideration of both virtua1 network embedding in space dimension and orchestration of VNF 1ife cyc1e in time dimension，and thus，the mu1ti-objective optimization of revenue and de1ay cou1d be attained.Simu1ation shows that the method can reduce network de1ay whi1e increasing the revenue and the ratio of request acceptance compared with other dep1oyment methods. |
| format | Article |
| id | doaj-art-74812f11b9564745aa31174d72fe530a |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2017-08-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-74812f11b9564745aa31174d72fe530a2025-01-14T07:12:51ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2017-08-013817218259711806Deployment method for vEPC virtualized network function via Q-learningQuan YUANHong-bo TANGKai-zhi HUANGXiao-lei WANGYu ZHAOIn the context of vEPC，a method of virtua1ized network function（VNF）dep1oyment via an improved Q-1earning a1gorithm was proposed to so1ve the prob1em that the existing methods cannot achieve the optimization of time de1ay and revenue of VNF dep1oyment simu1taneous1y.To get the optima1 dep1oyment po1icy in both space dimension and time dimension，a Markov decision process mode1 of vEPC service function chain dep1oyment on the basis of the traditiona1 0-1 programming mode1 was estab1ished and a so1ution with an improved Q-1earning a1gorithm was proposed.The method had taken fu11 consideration of both virtua1 network embedding in space dimension and orchestration of VNF 1ife cyc1e in time dimension，and thus，the mu1ti-objective optimization of revenue and de1ay cou1d be attained.Simu1ation shows that the method can reduce network de1ay whi1e increasing the revenue and the ratio of request acceptance compared with other dep1oyment methods.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017173/5GVNFservice function chain dep1oymentQ-1earning |
| spellingShingle | Quan YUAN Hong-bo TANG Kai-zhi HUANG Xiao-lei WANG Yu ZHAO Deployment method for vEPC virtualized network function via Q-learning Tongxin xuebao 5G VNF service function chain dep1oyment Q-1earning |
| title | Deployment method for vEPC virtualized network function via Q-learning |
| title_full | Deployment method for vEPC virtualized network function via Q-learning |
| title_fullStr | Deployment method for vEPC virtualized network function via Q-learning |
| title_full_unstemmed | Deployment method for vEPC virtualized network function via Q-learning |
| title_short | Deployment method for vEPC virtualized network function via Q-learning |
| title_sort | deployment method for vepc virtualized network function via q learning |
| topic | 5G VNF service function chain dep1oyment Q-1earning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017173/ |
| work_keys_str_mv | AT quanyuan deploymentmethodforvepcvirtualizednetworkfunctionviaqlearning AT hongbotang deploymentmethodforvepcvirtualizednetworkfunctionviaqlearning AT kaizhihuang deploymentmethodforvepcvirtualizednetworkfunctionviaqlearning AT xiaoleiwang deploymentmethodforvepcvirtualizednetworkfunctionviaqlearning AT yuzhao deploymentmethodforvepcvirtualizednetworkfunctionviaqlearning |