A computation offloading method over big data for IoT-enabled cloud-edge computing

Xu, Xiaolong; Liu, Qingxiang; Luo, Yaping; Peng, Kai; Zhang, Xuyun; Meng, Shunmei; Qi, Lianyong

doi:10.1016/j.future.2018.12.055

Cited by 295 publications

(202 citation statements)

References 34 publications

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“…First of all, many previous works focused on minimizing the energy consumption with the assumption that devices equipped with a battery have limited power [2,[4][5][6][7][8]. Authors in [4] proposed the evolutionary algorithm to minimize the energy consumption for data offloading. In [2], Yan et al considered an energy efficient offloading method under unstable channels.…”

Section: Related Workmentioning

confidence: 99%

“…(2) and calculate the finishing time for Y′ based on Eqs. (3)(4)(5). We can also determine the number of gateways based on Eq.…”

Section: )mentioning

confidence: 99%

“…This allows mobile devices to offload computing tasks to an edge server, which contains higher processing abilities to decrease the computing time. On the other hand, in comparison to cloud computing, MEC requires edge servers with relatively moderate computational resources but closer to IoT than the cloud to reduce the communication latency [4]. This latency can be further reduced in 5G networks.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Mobile Edge Computing for Mobile Internet of Thing in 5G Networks

Chevalier

Wang

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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We study the off-line efficient mobile edge computing (EMEC) problem for a joint computing to process a task both locally and remotely with the objective of minimizing the finishing time. When computing remotely, the time will include the communication and computing time. We first describe the time model, formulate EMEC, prove NPcompleteness of EMEC, and show the lower bound. We then provide an integer linear programming (ILP) based algorithm to achieve the optimal solution and give results for small-scale cases. A fully polynomialtime approximation scheme (FPTAS), named Approximation Partition (AP), is provided through converting ILP to the subset sum problem. Numerical results show that both the total data length and the movement have great impact on the time for mobile edge computing. Numerical results also demonstrate that our AP algorithm obtain the finishing time, which is close to the optimal solution.

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Section: Related Workmentioning

confidence: 99%

“…(2) and calculate the finishing time for Y′ based on Eqs. (3)(4)(5). We can also determine the number of gateways based on Eq.…”

Section: )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Mobile Edge Computing for Mobile Internet of Thing in 5G Networks

Chevalier

Wang

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…With the development of deep learning algorithms in recent years, studies using these methodologies have been drawing attention to improve the estimation performance [22,23]. Deep learning is distinguished for the specialty in learning accurate relations from large and complex datasets, which is well suitable for the precipitation estimation task under elastic computing resources available in the cloud [24][25][26]. Among the deep learning algorithms, the artificial neural network is a novel method simulating human's thinking and memory based on the research of biological neural network.…”

Section: Introductionmentioning

confidence: 99%

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

Tian¹,

Lei²,

Liu³

et al. 2020

J Cloud Comp

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Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84% and 82.30% lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75% compared to that with BPNN.

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“…With the development of computer network, cloud computing, as well as wireless sensor network, mobile devices (MDs) have become an indispensable part of people's daily lives [1][2][3][4][5][6][7]. In addition, the development of cyberphysical-social systems and big data has further affected people's life [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

An energy- and cost-aware computation offloading method for workflow applications in mobile edge computing

Peng

Zhu

Zhang

et al. 2019

J Wireless Com Network

Self Cite

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Mobile edge computing is becoming a promising computing architecture to overcome the resource limitation of mobile devices and bandwidth bottleneck of the core networks in mobile cloud computing. Although offloading applications to the cloud can extend the performance for the mobile devices, it may also lead to greater processing latency. Usually, the mobile users have to pay for the cloudlet resource or cloud resource they used. In this paper, we bring a thorough study on the energy consumption, time consumption, and cost of using the resource of cloudlet and cloud for workflow applications in mobile edge computing. Based on theoretical analysis, a multi-objective optimization model is established. In addition, a corresponding multi-objective computation offloading method based on non-dominated sorting genetic algorithm II is proposed to find the optimal offloading strategy for all the workflow applications. Finally, extensive experimental evaluations are performed to show that our proposed method is effective and energy-and cost-aware for workflow applications in MEC.

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A computation offloading method over big data for IoT-enabled cloud-edge computing

Cited by 295 publications

References 34 publications

Efficient Mobile Edge Computing for Mobile Internet of Thing in 5G Networks

Efficient Mobile Edge Computing for Mobile Internet of Thing in 5G Networks

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

An energy- and cost-aware computation offloading method for workflow applications in mobile edge computing

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