A Computation Offloading and Resource Allocation Mechanism Based on Minimizing Devices Energy Consumption and System Delay

Meiling DAI; Zhoubin LIU; Shaoyong GUO; Sujie SHAO; Xuesong QIU

doi:10.11999/JEIT180970

Volume 41 Issue 11

Nov. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(11): 2684-2690

Meiling DAI, Zhoubin LIU, Shaoyong GUO, Sujie SHAO, Xuesong QIU. A Computation Offloading and Resource Allocation Mechanism Based on Minimizing Devices Energy Consumption and System Delay[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2684-2690. doi: 10.11999/JEIT180970

Citation:

Meiling DAI, Zhoubin LIU, Shaoyong GUO, Sujie SHAO, Xuesong QIU. A Computation Offloading and Resource Allocation Mechanism Based on Minimizing Devices Energy Consumption and System Delay[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2684-2690. doi: 10.11999/JEIT180970

Citation:

Meiling DAI, Zhoubin LIU, Shaoyong GUO, Sujie SHAO, Xuesong QIU. A Computation Offloading and Resource Allocation Mechanism Based on Minimizing Devices Energy Consumption and System Delay[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2684-2690. doi: 10.11999/JEIT180970

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A Computation Offloading and Resource Allocation Mechanism Based on Minimizing Devices Energy Consumption and System Delay

doi: 10.11999/JEIT180970

State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Funds: The State Grid Technology Project (52110118001H)

Received Date: 2018-10-17
Rev Recd Date: 2019-03-13

Available Online: 2019-04-01

Publish Date: 2019-11-01

Abstract

Abstract

To support the execution of computation-intensive, delay-sensitive computing task by moving down the computing and processing capability in mobile edge computing becomes the current trend. However, when serving a large number of mobile users, how to use effectively the edge nodes with limited computing resources to ensure Quality of service (QoS) of end-user has become a key issue. To solve this problem, the edge cloud and remote cloud are combined to build a layered edge cloud computing architecture. Based on this architecture, with the goal of minimizing mobile device energy consumption and task execution time, the problem which is proved to be convex is formulated to minimize the weight sum of energy and delay. A computation offloading and resource allocation mechanism based on multiplier method is proposed. Simulations are conducted to evaluate the proposed mechanism. Compared with local computing and computation offloading mechanism, the proposed mechanism can effectively reduce the energy consumption of mobile device and the delay of system by up to 60% and 10%, respectively.
- Edge computing,
- Computing offloading,
- Resource allocation,
- Energy consumption,
- Delay of system

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References(15)

References

CHEN T Y H, RAVINDRANATH L, DENG Shuo, et al. Glimpse: Continuous, real-time object recognition on mobile devices[C]. Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, Seoul, South Korea, 2015: 155–168.

LEE H S and LEE J W. Task offloading in heterogeneous mobile cloud computing: Modeling, analysis, and cloudlet deployment[J]. IEEE Access, 2018, 6: 14908–14925. doi: 10.1109/ACCESS.2018.2812144

VAN DEN BOSSCHE R, VANMECHELEN K, and BROECKHOVE J. Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workloads[C]. Proceedings of the IEEE 3rd International Conference on Cloud Computing, Miami, USA, 2010: 228–235.

TONG Liang, LI Yong, and GAO Wei. A hierarchical edge cloud architecture for mobile computing[C]. Proceedings of the IEEE INFOCOM 2016- the 35th Annual IEEE International Conference on Computer Communications, San Francisco, USA, 2016: 1–9.

DU Jianbo, ZHAO Liqiang, FENG Jie, et al. Computation offloading and resource allocation in mixed fog/cloud computing systems with Min-Max fairness guarantee[J]. IEEE Transactions on Communications, 2018, 66(4): 1594–1608. doi: 10.1109/TCOMM.2017.2787700

AHMAD A, PAUL A, KHAN M, et al. Energy efficient hierarchical resource management for mobile cloud computing[J]. IEEE Transactions on Sustainable Computing, 2017, 2(2): 100–112. doi: 10.1109/TSUSC.2017.2714344

KAO Y H, KRISHNAMACHARI B, RA M R, et al. Hermes: Latency optimal task assignment for resource-constrained mobile computing[J]. IEEE Transactions on Mobile Computing, 2017, 16(11): 3056–3069. doi: 10.1109/TMC.2017.2679712

WU Huaming, KNOTTENBELT W, WOLTER K, et al. An Optimal Offloading Partitioning Algorithm in Mobile Cloud Computing[M]. Cham, Springer, 2016: 311–328.

DINH T Q, TANG Jianhua, LA Q D, et al. Offloading in mobile edge computing: task allocation and computational frequency scaling[J]. IEEE Transactions on Communications, 2017, 65(8): 3571–3584. doi: 10.1109/TCOMM.2017.2699660

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

WANG Yanting, SHENG Min, WANG Xijun, et al. Mobile-edge computing: partial computation offloading using dynamic voltage scaling[J]. IEEE Transactions on Communications, 2016, 64(10): 4268–4282. doi: 10.1109/TCOMM.2016.2599530

CHEN Xu, JIAO Lei, LI Wenzhong, et al. Efficient multi-user computation offloading for mobile-edge cloud computing[J]. IEEE/ACM Transactions on Networking, 2016, 24(5): 2795–2808. doi: 10.1109/TNET.2015.2487344

CHEN Xu. Decentralized computation offloading game for mobile cloud computing[J]. IEEE Transactions on Parallel and Distributed Systems, 2015, 26(4): 974–983. doi: 10.1109/TPDS.2014.2316834

CARDELLINI V, DE NITTO PERSONÉ V, DI VALERIO V, et al. A game-theoretic approach to computation offloading in mobile cloud computing[J]. Mathematical Programming, 2016, 157(2): 421–449. doi: 10.1007/s10107-015-0881-6

CHEN Menghsi, DONG Min, and LIANG Ben. Joint offloading decision and resource allocation for mobile cloud with computing access point[C]. Proceedings of 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai, China, 2016: 3516–3520.

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