Edge server placement in mobile edge computing

Wang, Shangguang; Zhao, Yuchao; Xu, Jinlinag; Yuan, Jie; Hsu, Ching‐Hsien

doi:10.1016/j.jpdc.2018.06.008

Cited by 394 publications

(218 citation statements)

References 22 publications

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“…The data comes from a meteorological observatory located in the center area of Taizhou in Zhejiang province. The Doppler radar transmits radar reflectivity factors (dBZ) as well as the corresponding longitude and latitude every six minutes with eleven different elevation angles, which are stored with a binary file format orderly in private cloud [37][38][39]. Meanwhile, there are four automatic weather stations recording the minutely precipitation ordered by date and time, which are considered as the authentic value to verify the accuracy of precipitation estimation.…”

Section: Data Preparationmentioning

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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Section: Data Preparationmentioning

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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“…MU can connect to edge server or cloud according to requirement of service. There are many kinds of edge servers [43]. Cloudlet is a type of edge server, which has been widely used in LAN and WMAN [25][26][27][44][45][46].…”

Section: Related Workmentioning

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

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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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“…services. An adaptive consistency approach in this environment should consider more criteria specifically replica and edge servers placement [69,70], client context-awareness [39], security and privacy [55]:…”

Section: Consistency In Emerging Computing Architecturesmentioning

confidence: 99%

A comparative analysis of adaptive consistency approaches in cloud storage

Khelaifa

Benharzallah

Kahloul

et al. 2019

Journal of Parallel and Distributed Computing

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NoSQL storage systems are used extensively by web applications and provide an attractive alternative to conventional databases due to their high security and availability with a low cost. High data availability is achieved by replicating data in different servers in order to reduce access time lag, network bandwidth consumption and system unreliability. Hence, the data consistency is a major challenge in distributed systems. In this context, strong consistency guarantees data freshness but affects directly the performance and availability of the system. In contrast, weaker consistency enhances availability and performance but increases data staleness. Therefore, an adaptive consistency strategy is needed to tune, during runtime, the consistency level depending on the criticality of the requests or data items. Although there is a rich literature on adaptive consistency approaches in cloud storage, there is a need to classify as well as regroup the approaches based on their strategies. This paper will establish a set of comparative criteria and then make a comparative analysis of existing adaptive consistency approaches. A survey of this kind not only provides the user/researcher with a comparative performance analysis of the approaches but also clarifies the suitability of these for candidate cloud systems.

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Edge server placement in mobile edge computing

Cited by 394 publications

References 22 publications

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

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

A comparative analysis of adaptive consistency approaches in cloud storage

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