| Citation: | Lun TANG, Jiao XIAO, Yannan WEI, Guofan ZHAO, Qianbin CHEN. Joint Resource Allocation Algorithms Based on Mixed Cloud/Fog Computing in Vehicular Network[J]. Journal of Electronics & Information Technology, 2020, 42(8): 1926-1933. doi: 10.11999/JEIT190306
|
For the problems of low delay, low power requirement and access congestion caused by computational unloading of mass devices, a Joint Offloading Decision and Resource Allocation Algorithm (JODRAA) is proposed based on cloud-fog hybrid network architecture. Firstly, the algorithm considers the combination of cloud and fog computing, and establishes a resource optimization model to minimize system energy consumption and resource cost with maximum delay as constraint. Secondly, the original problem is transformed into a standard Quadratically Constrained Quadratic Program (QCQP) problem, and a low-complexity joint unloading decision-making and computational resource allocation algorithm is designed. Furthermore, considering the access congestion problem caused by massive computing of unloading devices, an estimation model of the overflow probability of unloading user access request queue is established, and an on-line measurement based time-frequency resource allocation algorithm for fog nodes is proposed. Finally, the iterative bandwidth and power allocation strategy is obtained by using fractional programming theory and Lagrange dual decomposition method. The simulation results show that the proposed algorithm can minimize the system energy consumption and resource cost on the premise of time delay.
|
MEBREK A, MERGHEM-BOULAHIA L, and ESSEGHIR M. Efficient green solution for a balanced energy consumption and delay in the IoT-Fog-Cloud computing[C]. The 16th IEEE International Symposium on Network Computing and Applications, Cambridge, USA, 2017: 1–4. doi: 10.1109/NCA.2017.8171359.
|
|
BACCARELLI E, NARANJO P G V, SCARPINITI M, et al. Fog of everything: Energy-efficient networked computing architectures, research challenges, and a case study[J]. IEEE Access, 2017, 5: 9882–9910. doi: 10.1109/ACCESS.2017.2702013
|
|
LIU Kaiyang, PENG Jun, ZHANG Xiaoyong, et al. A combinatorial optimization for energy-efficient mobile cloud offloading over cellular networks[C]. 2016 IEEE Global Communications Conference, Washington, USA, 2016: 1–6. doi: 10.1109/GLOCOM.2016.7841488.
|
|
YANG Lei, CAO Jiannong, TANG Shaojie, et al. A framework for partitioning and execution of data stream applications in mobile cloud computing[C]. The 5th IEEE International Conference on Cloud Computing, Honolulu, USA, 2012: 794–802. doi: 10.1109/CLOUD.2012.97.
|
|
LIU Mengyu and LIU Yuan. Price-based distributed offloading for mobile-edge computing with computation capacity constraints[J]. IEEE Wireless Communications Letters, 2018, 7(3): 420–423. doi: 10.1109/LWC.2017.2780128
|
|
CAO Xiaowen, WANG Feng, XU Jie, et al. Joint computation and communication cooperation for energy-efficient mobile edge computing[J]. IEEE Internet of Things Journal, 2019, 6(3): 4188–4200. doi: 10.1109/JIOT.2018.2875246
|
|
MENG Xianling, WANG Wei, and ZHANG Zhaoyang. Delay-constrained hybrid computation offloading with cloud and fog computing[J]. IEEE Access, 2017, 5: 21355–21367. doi: 10.1109/ACCESS.2017.2748140
|
|
GU H Y, YANG C Y, and FONG B. Low-complexity centralized joint power and admission control in cognitive radio networks[J]. IEEE Communications Letters, 2009, 13(6): 420–422. doi: 10.1109/LCOMM.2009.082173
|
|
JIANG Menglan, CONDOLUCI M, and MAHMOODI T. Network slicing management & prioritization in 5G mobile systems[C]. The 22th European Wireless Conference, Oulu, Finland, 2016: 1–6.
|
|
YAQOOB S, ULLAH A, AKBAR M, et al. Fog-assisted congestion avoidance scheme for internet of vehicles[C]. The 14th International Wireless Communications & Mobile Computing Conference, Limassol, Cyprus, 2018: 618–622. doi: 10.1109/IWCMC.2018.8450402.
|
|
LI Jian, PENG Mugen, YU Yuling, et al. Energy-efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks[J]. IEEE Transactions on Vehicular Technology, 2016, 65(12): 9873–9887. doi: 10.1109/TVT.2016.2531184
|
|
LIU Yiming, YU F R, LI Xi, et al. Distributed resource allocation and computation offloading in fog and cloud networks with non-orthogonal multiple access[J]. IEEE Transactions on Vehicular Technology, 2018, 67(12): 12137–12151. doi: 10.1109/TVT.2018.2872912
|
|
LI Qiuping, ZHAO Junhui, GONG Yi, et al. Energy-efficient computation offloading and resource allocation in fog computing for internet of everything[J]. China Communications, 2019, 16(3): 32–41.
|
|
SHAHZADI R, NIAZ A, ALI M, et al. Three tier fog networks: Enabling IoT/5G for latency sensitive applications[J]. China Communications, 2019, 16(3): 1–11.
|
|
SOOKHAK M, YU F R, HE Ying, et al. Fog vehicular computing: Augmentation of fog computing using vehicular cloud computing[J]. IEEE Vehicular Technology Magazine, 2017, 12(3): 55–64. doi: 10.1109/MVT.2017.2667499
|
|
LI Di, KAR S, and CUI Shuguang. Distributed quickest detection in sensor networks via two-layer large deviation analysis[J]. IEEE Internet of Things Journal, 2018, 5(2): 930–942. doi: 10.1109/JIOT.2018.2810825
|
Figures(6) / Tables(4)
DownLoad:
