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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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

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