期刊
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
卷 33, 期 6, 页码 1503-1519出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2021.3112604
关键词
Task analysis; Computer architecture; Optimization; Delays; Servers; Costs; Quality of experience; Computation offloading; end-edge-cloud computing (EECC); hierarchical EECC; horizontal EECC; potential game
资金
- National Outstanding Youth Science Program of National Natural Science Foundation of China [61625202]
- National Key Research and Development Program of China [2018YFB1701403]
- Programs of National Natural Science Foundation of China [61876061, 62072165, U19A2058, 61702170]
- Postgraduate Scientific Research Innovation Project of Hunan Province [CX20200435]
- Open Research Projects of Zhejiang Lab [2020KE0AB01]
This article investigates two computing architectures of end-edge-cloud computing (EECC), Hi-EECC and Ho-EECC, and develops potential game-based algorithms to optimize computation offloading strategies. Extensive experiments demonstrate the performance of the proposed algorithms and the scalability and applicability of the two computing architectures are comprehensively analyzed.
Integrating user ends (UEs), edge servers (ESs), and the cloud into end-edge-cloud computing (EECC) can enhance the utilization of resources and improve quality of experience (QoE). However, the performance of EECC is significantly affected by its architecture. In this article, we classify EECC into two computing architectures types according to the visibility and accessibility of the cloud to UEs, i.e., hierarchical end-edge-cloud computing (Hi-EECC) and horizontal end-edge-cloud computing (Ho-EECC). In Hi-EECC, UEs can offload their tasks only to ESs. When the resources of ESs are exhausted, the ESs request the cloud to provide resources to UEs. In Ho-EECC, UEs can offload their tasks directly to ESs and the cloud. In this article, we construct a potential game for the EECC environment, in which each UE selfishly minimizes its payoff, study the computation offloading strategy optimization problems, and develop two potential game-based algorithms in Hi-EECC and Ho-EECC. Extensive experiments with real-world data are conducted to demonstrate the performance of the proposed algorithms. Moreover, the scalability and applicability of the two computing architectures are comprehensively analyzed. The conclusions of our work can provide useful suggestions for choosing specific computing architectures under different application environments to improve the performance of EECC and QoE.
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