Deep Reinforcement Learning for Performance-Aware Adaptive Resource Allocation in Mobile Edge Computing
المؤلفون المشاركون
Wang, Shangguang
Hu, Haiyang
Huang, Binbin
Pan, Linxuan
Xu, Yunqiu
Li, Zhongjin
Chang, Victor
المصدر
Wireless Communications and Mobile Computing
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-02
دولة النشر
مصر
عدد الصفحات
17
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Mobile edge computing (MEC) enables to provide relatively rich computing resources in close proximity to mobile users, which enables resource-limited mobile devices to offload workloads to nearby edge servers, and thereby greatly reducing the processing delay of various mobile applications and the energy consumption of mobile devices.
Despite its advantages, when a large number of mobile users simultaneously offloads their computation tasks to an edge server, due to the limited computation and communication resources of edge server, inefficiency resource allocation will not make full use of the limited resource and cause waste of resource, resulting in low system performance (the weighted sum of the number of processed tasks, the number of punished tasks, and the number of dropped tasks).
Therefore, it is a challenging problem to effectively allocate the computing and communication resources to multiple mobile users.
To cope with this problem, we propose a performance-aware resource allocation (PARA) scheme, the goal of which is to maximize the long-term system performance.
More specifically, we first build the multiuser resource allocation architecture for computing workloads and transmitting result data to mobile devices.
Then, we formulate the multiuser resource allocation problem as a Markova Decision Process (MDP).
To achieve this problem, a performance-aware resource allocation (PARA) scheme based on a deep deterministic policy gradient (DDPG) is adopted to derive optimal resource allocation policy.
Finally, extensive simulation experiments demonstrate the effectiveness of the PARA scheme.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Huang, Binbin& Li, Zhongjin& Xu, Yunqiu& Pan, Linxuan& Wang, Shangguang& Hu, Haiyang…[et al.]. 2020. Deep Reinforcement Learning for Performance-Aware Adaptive Resource Allocation in Mobile Edge Computing. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1214367
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Huang, Binbin…[et al.]. Deep Reinforcement Learning for Performance-Aware Adaptive Resource Allocation in Mobile Edge Computing. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1214367
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Huang, Binbin& Li, Zhongjin& Xu, Yunqiu& Pan, Linxuan& Wang, Shangguang& Hu, Haiyang…[et al.]. Deep Reinforcement Learning for Performance-Aware Adaptive Resource Allocation in Mobile Edge Computing. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1214367
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1214367
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر