Lyapunov优化
计算机科学
计算卸载
移动边缘计算
云计算
边缘计算
能源消耗
分布式计算
计算
任务(项目管理)
移动设备
实时计算
计算机网络
服务器
李雅普诺夫方程
算法
人工智能
李雅普诺夫指数
生物
操作系统
经济
管理
混乱的
生态学
作者
Nadine Abbas,Wissam Fawaz,Sanaa Sharafeddine,Azzam Mourad,Chadi Abou Rjeily
出处
期刊:IEEE Transactions on Network and Service Management
[Institute of Electrical and Electronics Engineers]
日期:2022-09-01
卷期号:19 (3): 3121-3135
被引量:16
标识
DOI:10.1109/tnsm.2022.3159478
摘要
Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to run complex applications. This can be realized through efficient computing data offloading and yet enhanced while simultaneously using multiple wireless interfaces for D2D, MEC and cloud offloading. In this work, we propose user-centric real-time computation task offloading and resource allocation strategies aiming at minimizing energy consumption and monetary cost while maximizing the number of completed tasks. We develop dynamic partial offloading solutions using the Lyapunov drift-plus-penalty optimization approach. Moreover, we propose a task admission solution based on support vector machines (SVM) to assess the potential of a task to be completed within its deadline, and accordingly, decide whether to drop from or add it to the user’s queue for processing. Results demonstrate high performance gains of the proposed solution that employs SVM-based task admission and Lyapunov-based computation offloading strategies. Significant increase in number of completed tasks, energy savings, and cost reductions are resulted as compared to alternative baseline approaches.
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