Cooperative computation offloading and resource allocation for delay minimization in mobile edge computing
Mobile edge computing (MEC) is a promising paradigm, which brings computation resources in proximity to mobile devices and allows the tasks of mobile devices to be offloaded to MEC servers with low latency. The joint problem of cooperative computation task offloading and resource allocation is a cha...
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| Published in | Journal of systems architecture Vol. 118; p. 102167 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.09.2021
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Mobile edge computing (MEC) is a promising paradigm, which brings computation resources in proximity to mobile devices and allows the tasks of mobile devices to be offloaded to MEC servers with low latency. The joint problem of cooperative computation task offloading and resource allocation is a challenging issue. The joint problem of cooperative computation task offloading scheme and resource assignment in MEC is investigated in this paper, where the vertical cooperation among mobile devices, mobile edge server nodes and mobile cloud server nodes is considered, and the horizontal computation cooperation between edge nodes is considered as well. A computation offloading decision, cooperative selection, power allocation and CPU cycle frequency assignment problem is formulated. The objective is to minimize the latency while guaranteeing the constraint of transmission power, energy consumption and CPU cycle frequency. The formulated latency optimization problem is a nonconvex mixed-integer problem in general, which has binary variables and continuous variables. In order to solve the formulated problem. A joint iterative algorithm based on the Lagrangian dual decomposition, ShengJin Formula method, and monotonic optimization method is proposed. The CPU cycle frequence allocation is handled by the ShengJin Formula method due to the cubic equation of one variable about the CPU frequence allocation. The transmission power assignment is handled by the monotonic optimization method. In the algorithm convergence with different number of tasks, the proposed algorithm can quickly and effectively reach the convergence state and getting the minimum task execution delay. Numerical results demonstrate that the proposed algorithm outperforms the Full MEC, Full Local and Full Cloud three schemes in terms of execution latency. |
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| AbstractList | Mobile edge computing (MEC) is a promising paradigm, which brings computation resources in proximity to mobile devices and allows the tasks of mobile devices to be offloaded to MEC servers with low latency. The joint problem of cooperative computation task offloading and resource allocation is a challenging issue. The joint problem of cooperative computation task offloading scheme and resource assignment in MEC is investigated in this paper, where the vertical cooperation among mobile devices, mobile edge server nodes and mobile cloud server nodes is considered, and the horizontal computation cooperation between edge nodes is considered as well. A computation offloading decision, cooperative selection, power allocation and CPU cycle frequency assignment problem is formulated. The objective is to minimize the latency while guaranteeing the constraint of transmission power, energy consumption and CPU cycle frequency. The formulated latency optimization problem is a nonconvex mixed-integer problem in general, which has binary variables and continuous variables. In order to solve the formulated problem. A joint iterative algorithm based on the Lagrangian dual decomposition, ShengJin Formula method, and monotonic optimization method is proposed. The CPU cycle frequence allocation is handled by the ShengJin Formula method due to the cubic equation of one variable about the CPU frequence allocation. The transmission power assignment is handled by the monotonic optimization method. In the algorithm convergence with different number of tasks, the proposed algorithm can quickly and effectively reach the convergence state and getting the minimum task execution delay. Numerical results demonstrate that the proposed algorithm outperforms the Full MEC, Full Local and Full Cloud three schemes in terms of execution latency. |
| ArticleNumber | 102167 |
| Author | Kuang, Zhufang Deng, Xiaoheng Li, Zhe Ma, Zhihao |
| Author_xml | – sequence: 1 givenname: Zhufang orcidid: 0000-0002-1445-3217 surname: Kuang fullname: Kuang, Zhufang email: zfkuangcn@163.com organization: School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China – sequence: 2 givenname: Zhihao surname: Ma fullname: Ma, Zhihao email: 827272056@qq.com organization: School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China – sequence: 3 givenname: Zhe surname: Li fullname: Li, Zhe email: zhemao1992@qq.com organization: School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China – sequence: 4 givenname: Xiaoheng orcidid: 0000-0003-2740-8025 surname: Deng fullname: Deng, Xiaoheng email: dxh@csu.edu.cn organization: School of Computer Science and Engineering, Central South University, Changsha 410010, China |
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| Keywords | Cooperative computation offloading Delay minimization Mobile edge computing Resource allocation |
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