期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 71, 期 5, 页码 4584-4595出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3133586
关键词
Task analysis; Servers; 6G mobile communication; Costs; Optimization; Computer architecture; Stochastic processes; Mobile edge computing; cybertwin; task offloading; hybrid energy supply; stochastic optimization
资金
- National Natural Science Foundation of China [61902029, 61972414, 62072490, 61973161]
- Excellent Talents Projects of Beijing [9111923401]
- Scientific Research Project of Beijing Municipal Education Commission [KM202011232015]
- Beijing Nova Program [Z201100006820082]
- Beijing Natural Science Foundation [4202066]
- Fundamental Research Funds for Central Universities [2462018YJRC040]
- FDCT-MOST Joint Fund Project [0066/2019/AMJ]
- Macao Science and Technology Development Fund [0060/2019/A1, 0162/2019/A3]
- University of Macau [MYRG2018-00237-FST]
This paper focuses on a hybrid energy-powered multi-server MEC system with Cybertwin. Vehicles enabled by Cybertwin and edge servers send the current network status and unprocessed tasks to the macro base station to achieve better resource allocation. The efficient multi-vehicle task offloading algorithm optimizes the cost and guarantees the system performance.
With the development of 6G wireless communication technologies, various resource-intensive and delay-sensitive vehicle application tasks are generated. These application tasks can be offloaded to Mobile Edge Computing (MEC) which deploys computing resources at the edge of networks. Besides, the recent proposed Cybertwin, as the digital representation of the complicated physical end-systems, can help the terminals obtain the required services from networks. Vehicles enabled by Cybertwin can offload their tasks to MEC and achieve better performance. In this paper, we focus on the study of a hybrid energy-powered multi-server MEC system with Cybertwin. Vehicles enabled by Cybertwin and edge servers send the current network status and unprocessed vehicle application tasks to the macro base station (MBS) to achieve the better allocation of resources. Energy harvesting (EH) devices are deployed on edge servers to form a green energy-grid hybrid energy supply model. We formulate a stochastic offloading optimization problem, and the goal is to minimize the system cost. The stochastic optimization problem is decomposed into three sub-problems. Then, we design an efficient multi-vehicle task offloading (EMT) algorithm to achieve the trade-off between system cost and task queue length. Theoretical analysis shows that EMT algorithm can optimize the total cost of the MEC system and guarantee the system performance. According to experimental evaluation, we verify the performance of the EMT algorithm.
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