计算卸载
计算机科学
任务(项目管理)
服务器
移动边缘计算
边缘计算
地铁列车时刻表
GSM演进的增强数据速率
分布式计算
计算复杂性理论
最优化问题
计算机网络
算法
人工智能
管理
经济
操作系统
作者
Sheng Yue,Ju Ren,Nan Qiao,Yongmin Zhang,Hongbo Jiang,Yaoxue Zhang,Yuanyuan Yang
出处
期刊:IEEE Transactions on Parallel and Distributed Systems
[Institute of Electrical and Electronics Engineers]
日期:2021-10-27
卷期号:33 (7): 1650-1665
被引量:62
标识
DOI:10.1109/tpds.2021.3123535
摘要
Edge computing has been an efficient way to provide prompt and near-data computing services for resource-and-delay sensitive IoT applications via computation offloading. Effective computation offloading strategies need to comprehensively cope with several major issues, including 1) the allocation of dynamic communication and computational resources, 2) delay constraints of heterogeneous tasks, and 3) requirements for computationally inexpensive and distributed algorithms. However, most of the existing works mainly focus on part of these issues, which would not suffice to achieve expected performance in complex and practical scenarios. To tackle this challenge, in this paper, we systematically study a distributed computation offloading problem with delay constraints, where heterogeneous computational tasks require continually offloading to a set of edge servers via a limiting number of stochastic communication channels. The task offloading problem is formulated as a delay-constrained long-term stochastic optimization problem under unknown prior statistical knowledge. To solve this problem, we first provide a technical path to transform and decompose it into several slot-level sub-problems. Then, we devise a distributed online algorithm, namely TODG, to efficiently allocate resources and schedule offloading tasks. Further, we present a comprehensive analysis for TODG in terms of the optimality gap, the worst-case delay, and the impact of system parameters. Extensive simulation results demonstrate the effectiveness and efficiency of TODG.
科研通智能强力驱动
Strongly Powered by AbleSci AI