Energy Efficient Spectrum Resource Allocation in Mobile Edge Computing

Li, Qing; Sun, Yanhua; Hao, Zhe; Zhang, Yanhua

doi:10.1109/iccc47050.2019.9064191

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Computation offloading strategy has been extensively studied in mobile edge computing (MEC) [11] and mobile cloud computing (MCC) [12]. Most studies focus on one [13][14][15] or two [16][17][18] aspects of energy consumption and latency. In [19], the author designed a deadline and priority-aware task offloading (DPTO) strategy to schedule and process offloaded tasks to suitable computing devices.…”

Section: A Related Workmentioning

confidence: 99%

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Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing

Bai

Han

et al. 2021

IEEE Access

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Fog computing is considered to be an effective method to solve the problem of high latency and high energy consumption of IoT devices. A suitable computation offloading strategy can provide a low offloading cost to the user device. Most researches on computation offloading in fog computing focus on one or two targets to improve system performance, however, the actual system needs to meet a comprehensive demand. Therefore, the joint optimization of multi-objective in multiple scenarios is a very meaningful problem. Inspired by this, the paper highlights the joint optimization research for fog computing, which proposes a Joint Computation offloading, Data compression, Energy harvesting, and Application scenarios (JCDEA) algorithm. The related mathematical model is constructed and the cost expressions of local computing, fog computing, and cloud computing are derived. Through the proposed algorithm, solving the computation offloading strategy is transformed into solving the minimum cost and is simplified by controlling strategy factors. Moreover, five simulation experiments are conducted and the meaningful conclusions are drawn, which contain that (1) the cost of fog computing is lower than that of local and cloud computing in most time slots and cloud computing can compensate for fog computing in complex environments; (2) the cost increases approximately linear with the amount of offloaded data; (3) the number of user devices and the compression ratio affect the fog-to-cloud ratio (FCR), while the FCR affects the cost; and (4) the related offloading strategy distribution and the cost are obtained for different scenarios. The JCDEA algorithm always outperforms than that of the random selection algorithm in all scenarios.

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Section: A Related Workmentioning

confidence: 99%

“…Here, d represents the offloading deadline which is equal to the length of the time slot. -17) indicates the computing time and transmission time cannot exceed a time slot (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18). indicates the offloading energy consumption cannot exceed the maximum instantaneous discharge threshold.…”

mentioning

confidence: 99%

Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing

Bai

Han

et al. 2021

IEEE Access

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“…Inspired by MEC technology, which could extend the computational capacity and process the task for DSA at edge cloud platform [28][29][30], the centralized training decentralized execu-tion (CTDE) scheme of MARL is addressed to achieve efficient DSA [31][32][33]. By this way, the distributed users offload the DSA task to the cloud platform in the training phase, so as to learn the action-taking strategies, and then work in a fully distributed manner in the practical implementation phase.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Dynamic Spectrum Allocation in MEC-Enabled Cognitive Networks: A Multiagent Reinforcement Learning Approach

Lei

Zhao

Zhou

et al. 2022

Wireless Communications and Mobile Computing

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Making effective use of scarce spectrum resources, along with efficient computational performance, is one of the key challenges for future wireless networks. To tackle this issue, in this paper, we focus on the intelligent dynamic spectrum allocation (DSA) in a mobile edge computing (MEC) enabled cognitive network. And our objective is to optimize the spectrum utilization and load balance among idle channels. Since users can only acquire part of environment information in a decentralized way, we model such a problem as decentralized partially observed Markov decision process (Dec-POMDP) and design the corresponding evaluating metric to encourage users sense and access spectrum properly. Then, we propose a QMIX-based DSA method with centralized training decentralized execution (CTDE) structure to tackle it. In the training phase, the users offload the computational tasks to the MEC server to obtain the optimal distributed DSA strategies, through which the users select the optimal channel locally in the execution phase. Simulation results show that, using the proposed algorithm, users can independently capture spectrum holes, and hence improve the spectrum utilization while balancing the load on available channels.

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Resource management and switch migration in SDN-based multi-access edge computing environments

Guo

Luo

2022

J Supercomput

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Energy Efficient Spectrum Resource Allocation in Mobile Edge Computing

Cited by 3 publications

References 14 publications

Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing

Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing

Intelligent Dynamic Spectrum Allocation in MEC-Enabled Cognitive Networks: A Multiagent Reinforcement Learning Approach

Resource management and switch migration in SDN-based multi-access edge computing environments

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