Intelligent task migration with deep Qlearning in multi-access edge computing
學年 110
學期 1
出版（發表）日期 2021-11-27
作品名稱 Intelligent task migration with deep Qlearning in multi-access edge computing
作品名稱（其他語言）
著者 Sheng-Zhi Huang; Kun-Yu Lin; Chin-Lin Hu
單位
出版者
著錄名稱、卷期、頁數 IET Communications 16(11), p.1290-1302
摘要 Multi-access edge computing provides computation and network resources in proximity to user applications in mobile environments. Deploying edge servers in network boundary can not only offload the heavy task loading on the cloud, but also alleviate resource-limited capabilities of mobile devices. Rather than many stand-alone edge servers, the concept of multi-server edge computing is recently advocated to contend with the issues of system scalability and service quality against dynamic task workload. This study exploits collaborative computing resources and designs a task migration strategy for multiple edge servers in mobile networks. This study formulates a queueing optimization problem of minimizing the overall service time in a multi-server system. An intelligent task migration scheme is then developed using the deep reinforcement learning and Q-learning techniques. With a variety of numerical attributes derived from the queueing model, this intelligent scheme can arrange the task distribution among edge servers to enhance the task processing capability. Simulation-based results show that the proposed task migration scheme can sustain service efficiency and resource utilization, which is promising as compared with conventional designs without collaborative intelligence in mobile environments.
關鍵字
語言 en_US
ISSN
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 TWN
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126219 )