Monte Carlo‐based service migration under multiple constraints in mobile edge computing
Abstract Mobile edge computing as an emerging technique can provide services for mobile terminals, and meanwhile the mobility of users brings new challenges. When a user moves across different areas, the system needs to determine whether to migrate service so as to guarantee quality of experience fo...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12705 |
| _version_ | 1797353587561463808 |
|---|---|
| author | Qiang Zhang Hao Yu |
| author_facet | Qiang Zhang Hao Yu |
| author_sort | Qiang Zhang |
| collection | DOAJ |
| description | Abstract Mobile edge computing as an emerging technique can provide services for mobile terminals, and meanwhile the mobility of users brings new challenges. When a user moves across different areas, the system needs to determine whether to migrate service so as to guarantee quality of experience for the user. However, it is difficult to obtain the optimal migration policy in real time due to the huge state space. Considering delay‐sensitive data‐intensive applications run by mobile terminals with limited battery power, an efficient service migration policy should be able to make a good tradeoff among service cost, service delay and terminal energy consumption. Here, an online Monte Carlo‐based service migration (MCSM) policy is proposed to minimize service cost under constraints of deadline and terminal energy consumption. A penalty mechanism is designed to update reward when partial or all constraints are not meet. State‐action value estimation and policy improvement are triggered only on the completion of each episode. Each episode is traversed reversely to calculate the average cumulative reward so as to improve policy. Experimental results show that the proposed approach can improve service success ratio and reduce average service cost compared to the existing service migration policies. |
| first_indexed | 2024-03-08T13:33:10Z |
| format | Article |
| id | doaj.art-32e00d032f634035baf5bd030b3d8a41 |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-03-08T13:33:10Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-32e00d032f634035baf5bd030b3d8a412024-01-17T02:51:21ZengWileyIET Communications1751-86281751-86362024-01-01181283910.1049/cmu2.12705Monte Carlo‐based service migration under multiple constraints in mobile edge computingQiang Zhang0Hao Yu1College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing ChinaCollege of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing ChinaAbstract Mobile edge computing as an emerging technique can provide services for mobile terminals, and meanwhile the mobility of users brings new challenges. When a user moves across different areas, the system needs to determine whether to migrate service so as to guarantee quality of experience for the user. However, it is difficult to obtain the optimal migration policy in real time due to the huge state space. Considering delay‐sensitive data‐intensive applications run by mobile terminals with limited battery power, an efficient service migration policy should be able to make a good tradeoff among service cost, service delay and terminal energy consumption. Here, an online Monte Carlo‐based service migration (MCSM) policy is proposed to minimize service cost under constraints of deadline and terminal energy consumption. A penalty mechanism is designed to update reward when partial or all constraints are not meet. State‐action value estimation and policy improvement are triggered only on the completion of each episode. Each episode is traversed reversely to calculate the average cumulative reward so as to improve policy. Experimental results show that the proposed approach can improve service success ratio and reduce average service cost compared to the existing service migration policies.https://doi.org/10.1049/cmu2.12705decision makingdynamic schedulingMarkov processesmobile communicationmobile computingMonte Carlo methods |
| spellingShingle | Qiang Zhang Hao Yu Monte Carlo‐based service migration under multiple constraints in mobile edge computing IET Communications decision making dynamic scheduling Markov processes mobile communication mobile computing Monte Carlo methods |
| title | Monte Carlo‐based service migration under multiple constraints in mobile edge computing |
| title_full | Monte Carlo‐based service migration under multiple constraints in mobile edge computing |
| title_fullStr | Monte Carlo‐based service migration under multiple constraints in mobile edge computing |
| title_full_unstemmed | Monte Carlo‐based service migration under multiple constraints in mobile edge computing |
| title_short | Monte Carlo‐based service migration under multiple constraints in mobile edge computing |
| title_sort | monte carlo based service migration under multiple constraints in mobile edge computing |
| topic | decision making dynamic scheduling Markov processes mobile communication mobile computing Monte Carlo methods |
| url | https://doi.org/10.1049/cmu2.12705 |
| work_keys_str_mv | AT qiangzhang montecarlobasedservicemigrationundermultipleconstraintsinmobileedgecomputing AT haoyu montecarlobasedservicemigrationundermultipleconstraintsinmobileedgecomputing |