scholarly journals SimuLTE-MEC: Extending SimuLTE for Multi-Access Edge Computing

10.29007/7g1p ◽  
2018 ◽  
Author(s):  
Giovanni Nardini ◽  
Antonio Virdis ◽  
Giovanni Stea ◽  
Angelo Buono

Multi-access Edge Computing (MEC) is a novel paradigm to enrich current 4G and future 5G cellular networks by placing cloud-computing-based capabilities at the edge of the network. This will allow operators and service providers to endow the cellular network with enriched services. In this paper we describe the modeling and development of a MEC extension for the SimuLTE framework.

2019 ◽  
Vol 9 (11) ◽  
pp. 2308 ◽  
Author(s):  
Juyong Lee ◽  
Daeyoub Kim ◽  
Jihoon Lee

Recently, new mobile applications and services have appeared thanks to the rapid development of mobile devices and mobile network technology. Cloud computing has played an important role over the past decades, providing powerful computing capabilities and high-capacity storage space to efficiently deliver these mobile services to mobile users. Nevertheless, existing cloud computing delegates computing to a cloud server located at a relatively long distance, resulting in significant delays due to additional time to return processing results from a cloud server. These unnecessary delays are inconvenient for mobile users because they are not suitable for applications that require a real-time service environment. To cope with these problems, a new computing concept called Multi-Access Edge Computing (MEC) has emerged. Instead of sending all requests to the central cloud to handle mobile users’ requests, the MEC brings computing power and storage resources to the edge of the mobile network. It enables the mobile user device to run the real-time applications that are sensitive to latency to meet the strict requirements. However, there is a lack of research on the efficient utilization of computing resources and mobility support when mobile users move in the MEC environment. In this paper, we propose the MEC-based mobility management scheme that arranges MEC server (MECS) as the concept of Zone so that mobile users can continue to receive content and use server resources efficiently even when they move. The results show that the proposed scheme reduce the average service delay compared to the existing MEC scheme. In addition, the proposed scheme outperforms the existing MEC scheme because mobile users can continuously receive services, even when they move frequently.


2021 ◽  
Vol 2 (5) ◽  
pp. 1-12
Author(s):  
Benedetta Picano ◽  
Romano Fantacci ◽  
Tommaso Pecorella ◽  
Adnan Rashid

In accordance with the Internet of Everything (IoE) paradigm, millions of people and billions of devices are expected to be connected to each other, giving rise to an ever increasing demand for application services with a strict quality of service requirements. Therefore, service providers are dealing with the functional integration of the classical cloud computing architecture with edge computing networks. However, the intrinsic limited capacity of the edge computing nodes implies the need for proper virtual functions' allocations to improve user satisfaction and service fulfillment. In this sense, demand prediction is crucial in services management and exploitation. The main challenge here consists of the high variability of application requests that result in inaccurate forecasts. Federated learning has recently emerged as a solution to train mathematical learning models on the users' site. This paper investigates the application of federated learning to virtual functions demand prediction in IoE based edge cloud computing systems, to preserve the data security and maximise service provider revenue. Additionally, the paper proposes a virtual function placement based on the services demand prediction provided by the federated learning module. A matching based tasks allocation is proposed. Finally, numerical results validate the proposed approach, compared with a chaos theory prediction scheme.


Author(s):  
Haowei Lin ◽  
Xiaolong Xu ◽  
Juan Zhao ◽  
Xinheng Wang

Abstract The multi-access edge computing (MEC) has higher computing power and lower latency than user equipment and remote cloud computing, enabling the continuing emergence of new types of services and mobile application. However, the movement of users could induce service migration or interruption in the MEC network. Especially for highly mobile users, they accelerate the frequency of services’ migration and handover, impacting on the stability of the total MEC network. In this paper, we propose a hierarchical multi-access edge computing architecture, setting up the infrastructure for dynamic service migration in the ultra-dense MEC networks. Moreover, we propose a new mechanism for users with high mobility in the ultra-dense MEC network, efficiently arranging service migrations for users with high-mobility and ordinary users together. Then, we propose an algorithm for evaluating migrated services to contribute to choose the suitable MEC servers for migrated services. The results show that the proposed mechanism can efficiently arrange service migrations and more quickly restore the services even in the blockage. On the other hand, the proposed algorithm is able to make a supplement to the existing algorithms for selecting MEC servers because it can better reflect the capability of migrated services.


2020 ◽  
Vol 58 (4) ◽  
pp. 24-30
Author(s):  
Estefania Coronado ◽  
Zarrar Yousaf ◽  
Roberto Riggio

2020 ◽  
Vol 8 (1) ◽  
pp. 85-92
Author(s):  
A. Antony Franklin ◽  
Supriya Dilip Tambe

2020 ◽  
Author(s):  
Haowei Lin ◽  
Xiaolong Xu ◽  
Juan Zhao ◽  
Xinheng Wang

Abstract The Multi-Access Edge Computing (MEC) has higher computing power than user equipment and lower latency than remote cloud computing, making new types of services and mobile applications keep emerging. However, the movement of users could induce service migration or interruption in the MEC network. Especially for highly mobile users, they accelerate the frequency of services' migration and handover, impacting on the stability of the total MEC network. In this paper, we propose a hierarchical multi-access edge computing architecture, setting up the Infrastructure for dynamic service migration in the ultra-dense MEC networks. Moreover, we propose a new mechanism for users with high mobility in the ultra-dense MEC network, efficiently arranging service migrations for users with high mobility and ordinary users together. Then, we propose an algorithm for evaluating migrated services to contribute to choose the suitable MEC servers for migrated services. The results show that the proposed mechanism can efficiently arrange service migrations and more quickly restore the services even in the blockage. On the other hand, the proposed algorithm is able to make a supplement to the existing algorithms for selecting MEC servers because it can better reflect the capability of migrated services.


2020 ◽  
Vol 10 (7) ◽  
pp. 2478 ◽  
Author(s):  
Woosik Lee ◽  
Eun Suk Suh ◽  
Woo Young Kwak ◽  
Hoon Han

Mobile communication technology is evolving from 4G to 5G. Compared to previous generations, 5G has the capability to implement latency-critical services, such as autonomous driving, real-time AI on handheld devices and remote drone control. Multi-access Edge Computing is one of the key technologies of 5G in guaranteeing ultra-low latency aimed to support latency critical services by distributing centralized computing resources to networks edges closer to users. However, due to its high granularity of computing resources, Multi-access Edge Computing has an architectural vulnerability in that it can lead to the overloading of regional computing resources, a phenomenon called regional traffic explosion. This paper proposes an improved communication architecture called Hybrid Cloud Computing, which combines the advantages of both Centralized Cloud Computing and Multi-access Edge Computing. The performance of the proposed network architecture is evaluated by utilizing a discrete-event simulation model. Finally, the results, advantages, and disadvantages of various network architectures are discussed.


Author(s):  
Dario Sabella ◽  
Alex Reznik ◽  
Rui Frazao

2021 ◽  
Author(s):  
Pablo Fondo-Ferreiro ◽  
David Candal-Ventureira ◽  
Felipe Gil-Castineira ◽  
Francisco Javier Gonzalez-Castano ◽  
Diarmuid Collins

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