Platform Support for Mobile Edge Computing

Lee, Jun Ho; Lee, Hochul; Lee, Young Choon; Han, Hyuck; Kang, Sooyong

doi:10.1109/cloud.2017.84

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…It uses the capabilities of personal computers along with cloud services, with more reliability . It has two essential features: (1) independence, which means that the local devices provide services without a continuous connection to the Internet; and (2) collaboration, which means that the application is connected to the cloud service and synchronizes the data at the proper time . Cloud is far from the ground, fog is closer to the ground, and dew is on the ground.…”

Section: Related Workmentioning

confidence: 99%

“…30 It has two essential features: (1) independence, which means that the local devices provide services without a continuous connection to the Internet; and (2) collaboration, which means that the application is connected to the cloud service and synchronizes the data at the proper time. 31,32 Cloud is far from the ground, fog is closer to the ground, and dew is on the ground. The cloud computing is far from the user, fog computing is beside the user, and dew computing is at the user.…”

Section: Dew Computingmentioning

confidence: 99%

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A proposed computation, which benefits from the cooperation of dew, edge, and cloud computations

Abdoos

Taheri

Taeezadeh

2019

Trans Emerging Tel Tech

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We propose a computation that compensates the weakness of the processing power and storage capacity of the dew and edge servers. In the proposed computation, the nodes near to each other, which can be the dew, edge, or cloud servers, form a computational grid. The dew servers can be mobile devices, laptops, or computers. Their processing power and storage capacity can be used when they are idle. Our goal is to achieve load balancing of different nodes (servers). A server of the grid with proper processing power, storage capacity, remained energy, and low task load is selected as the grid head (each server of grid shares its specification with the other servers of grid periodically). The grid head (server) is responsible for grid communications. The grid head shares the specification of the grid, with the neighbor nodes (we suppose that the end device is aware of the specification of the nearest dew, edge, and cloud grid heads to itself). The end device decides about sending its created tasks to the grid head of a dew or edge or cloud grid, with attention to the distance between itself and the grid head, and the free processing power and storage capacity of the grid. The scheduling algorithm of a grid is the combination of first‐come–first‐served and priority scheduling algorithms. The simulation results show that the proposed computation has less delay and energy consumption and more efficiency than the cloud and edge computations.

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Section: Related Workmentioning

confidence: 99%

Section: Dew Computingmentioning

confidence: 99%

A proposed computation, which benefits from the cooperation of dew, edge, and cloud computations

Abdoos

Taheri

Taeezadeh

2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…Masip-Bruin et al [14] introduced a layered F2C architecture with benefits and strengths and provided the real need for their coordinated management. To adapt to rapid increment of mobile resources, Lee et al [15] proposed collaborate platform solution, which greatly simplified the development mechanism of mobile collaborative applications and reduced the delay and energy consumption. Due to the problem that the mobile users are far away from the cloud servers and generate great transmission delay, Intharawijitr et al [16] relaxed the delay constraint system and selected the edge servers to reduce the system delay effectively.…”

Section: Related Workmentioning

confidence: 99%

Data Processing Delay Optimization in Mobile Edge Computing

Wang

et al. 2018

Wireless Communications and Mobile Computing

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With the development of Internet of Things (IoT), the number of mobile terminal devices is increasing rapidly. Because of high transmission delay and limited bandwidth, in this paper, we propose a novel three-layer network architecture model which combines cloud computing and edge computing (abbreviated as CENAM). In edge computing layer, we propose a computational scheme of mutual cooperation between the edge devices and use the Kruskal algorithm to compute the minimum spanning tree of weighted undirected graph consisting of edge nodes, so as to reduce the communication delay between them. Then we divide and assign the tasks based on the constrained optimization problem and solve the computation delay of edge nodes by using the Lagrange multiplier method. In cloud computing layer, we focus on the balanced transmission method to solve the data transmission delay from edge devices to cloud servers and obtain an optimal allocation matrix, which reduces the data communication delay. Finally, according to the characteristics of cloud servers, we solve the computation delay of cloud computing layer. Simulation shows that the CENAM has better performance in data processing delay than traditional cloud computing.

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CollaboRoid: Mobile platform support for collaborative applications

Lee

et al. 2019

Pervasive and Mobile Computing

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Platform Support for Mobile Edge Computing

Cited by 8 publications

References 19 publications

A proposed computation, which benefits from the cooperation of dew, edge, and cloud computations

A proposed computation, which benefits from the cooperation of dew, edge, and cloud computations

Data Processing Delay Optimization in Mobile Edge Computing

CollaboRoid: Mobile platform support for collaborative applications

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