Survey on Computation Offloading Strategies in Cellular Networks with Mobile Edge Computing

Kavyashree, S; Kumari, H. A. Chaya

doi:10.1007/978-981-16-6460-1_44

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Another challenge with the MEC paradigm is the resources management that achieves the highest energy, latency, and utilization efficiency [21][22][23]. MEC platform hosts end-user's services, and thus it requires an efficient offloading scheme to manage the process of data offloading based on the available resources and the service requirements [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Ateya¹,

Alhussan²,

Abdallah³

et al. 2023

Computer Systems Science and Engineering

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Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeterwave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration technique for complex edge computing structures. The developed migration scheme enables services to adapt to the required load on the radio channels. The proposed algorithm is analyzed for various use cases, and a test bench has been developed to emulate the operator's infrastructure. The obtained results are introduced and discussed.

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Section: Introductionmentioning

confidence: 99%

Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Ateya¹,

Alhussan²,

Abdallah³

et al. 2023

Computer Systems Science and Engineering

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“…Any latency in processing, analyzing, and collecting data in real-time is intolerable for IoV. Cloud computing, fog computing, 2 of 19 and Mobile Edge Computing (MEC) are a few intelligent computing platforms for big data analytics that can speed up data processing and effectively reduce latency [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Offloading Method for Mobile Edge Computing of Internet of Vehicles Based on Multi-Vehicle Users and Multi-MEC Servers

et al. 2022

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With the continuous development of intelligent transportation system technology, vehicle users have higher and higher requirements for low latency and high service quality of task computing. The computing offloading technology of mobile edge computing (MEC) has received extensive attention in the Internet of Vehicles (IoV) architecture. However, due to the limited resources of the MEC server, it cannot meet the task requests from multiple vehicle users simultaneously. For this reason, making correct and fast offloading decisions to provide users with a service with low latency, low energy consumption, and low cost is still a considerable challenge. Regarding the issue above, in the IoV environment where vehicle users race, this paper designs a three-layer system task offloading overhead model based on the Edge-Cloud collaboration of multiple vehicle users and multiple MEC servers. To solve the problem of minimizing the total cost of the system performing tasks, an Edge-Cloud collaborative, dynamic computation offloading method (ECDDPG) based on a deep deterministic policy gradient is designed. This method is deployed at the edge service layer to make fast offloading decisions for tasks generated by vehicle users. The simulation results show that the performance is better than the Deep Q-network (DQN) method and the Actor-Critic method regarding reward value and convergence. In the face of the change in wireless channel bandwidth and the number of vehicle users, compared with the basic method strategy, the proposed method has better performance in reducing the total computational cost, computing delay, and energy consumption. At the same time, the computational complexity of the system execution tasks is significantly reduced.

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Full-Decentralized Federated Learning-Based Edge Computing Peer Offloading Towards Industry 5.0

Chi,

Radwan

2023

2023 IEEE 21st International Conference on Industrial Informatics (INDIN)

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Survey on Computation Offloading Strategies in Cellular Networks with Mobile Edge Computing

Cited by 4 publications

References 12 publications

Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Dynamic Offloading Method for Mobile Edge Computing of Internet of Vehicles Based on Multi-Vehicle Users and Multi-MEC Servers

Full-Decentralized Federated Learning-Based Edge Computing Peer Offloading Towards Industry 5.0

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