Edge Computing in 5G: A Review

Hassan, Najmul; Yau, Kok‐Lim Alvin; Wu, Celimuge

doi:10.1109/access.2019.2938534

Cited by 286 publications

(131 citation statements)

References 79 publications

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“…The dynamic content request intensity and content popularity distribution are modeled using two independent Markov chains. The content requests process arrival to the EN is modeled as a Poisson process with intensity of λ (t) in interval t. Assume that request intensity is generated by an underlying Markov process with |L| states collected in the set L = λ (1) , . .…”

Section: A Formulation Of a Markov Decision Processmentioning

confidence: 99%

“…, λ (|L|) . Similarly, the content popularity distribution q(t) is generated by an underlying Markov process with |Q| states collected in the set Q = q (1) , . .…”

Section: A Formulation Of a Markov Decision Processmentioning

confidence: 99%

“…The demand for broadband multimedia applications has been rising exponentially, leading to a dramatic increase in network capital and operating expenditures. Edge computing is an emerging paradigm which stimulates the computing, caching and communication abilities of the network edge nodes (ENs), to satisfy the high quality of service requirements including low latency and high throughput [1]. However, the high expenditure of deployment and the location-centric network architecture restrict the efficiency and scalability of edge computing [2].…”

Section: Introductionmentioning

confidence: 99%

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Proactive Edge Caching in Content-Centric Networks With Massive Dynamic Content Requests

Feng

Shan

et al. 2020

IEEE Access

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Edge computing is a promising infrastructure evolution to reduce traffic loads and support low-latency communications. Furthermore, content-centric networks provide a natural solution to cache contents at edge nodes. However, it is a challenge for edge nodes to handle massive and highly dynamic content requests by users, and if without an efficient content caching strategy, the edge nodes will encounter high traffic load and latency due to increasing retrieval from content providers. This paper formulates a proactive edge caching problem to minimize the content retrieval cost at edge nodes. We exploit the inherent content caching and request aggregation mechanism in the content-centric networks to jointly minimize traffic load and content retrieval delay cost generated by the massive and dynamic content requests. We develop a Q-learning algorithm, which is an online optimal caching strategy, as it is adaptable to dynamic content popularity and content request intensity, and derive the long-term minimization of the content retrieval cost. Simulation results illustrate that the proposed algorithm can achieve a lower content retrieval cost compared with several baseline caching schemes. INDEX TERMS Content-centric networks, dynamic content requests, edge computing, proactive caching, Q-learning. XIAOGENG XU (Student Member, IEEE) received the B.S. degree in communications engineering from the Beijing University of Technology, China, in 2005, the M.S. degree in digital communication systems and technology from the Chalmers University of Technology, Sweden, in 2007. She is currently pursuing the Ph.D. degree in communication and information systems from the Beijing University of Posts and Telecommunications. Her current researches focus on caching performance analysis and optimization in content-centric networks. CHUNYAN FENG (Senior Member, IEEE) received the B.S. degree in communications engineering and the M.S. and Ph.D. degrees in communication and information systems from the Beijing University of Posts and Telecommunications (BUPT), Beijing, China. She is currently a Professor with the School of Information and Communication Engineering, BUPT. Her research interests are in the areas of broadband networks and wireless communication systems. Her current research focuses on cognitive radio and green wireless communications.

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Section: A Formulation Of a Markov Decision Processmentioning

confidence: 99%

“…, λ (|L|) . Similarly, the content popularity distribution q(t) is generated by an underlying Markov process with |Q| states collected in the set Q = q (1) , . .…”

Section: A Formulation Of a Markov Decision Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Proactive Edge Caching in Content-Centric Networks With Massive Dynamic Content Requests

Feng

Shan

et al. 2020

IEEE Access

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“…In [29], they used VRUs (Vulnerable Road users) to assist in information collecting about road environments, and tell the vehicles about this information and then send it using cellular network. As a result, depending on MEC (Multi-access Edge Computing) [59] bases instead of cloud based cellular infrastructure, they can lower the endto-end latency significantly compared to conventional methods. They proved their results using extensive simulation comparison.…”

Section: ) Mec-assisted End-to-end Latency Evaluations For C-v2x Commentioning

confidence: 99%

Minimizing E2e Delay in V2x Over Cellular Networks: Review and Challenges

Ali

Al-Hemairy

2020

Iraqi Journal of ICT

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V2X (Vehicle-to-Everything) evolution over cellular networks has been an excelling topic with the advances of high throughput and low latency LTE networks, and the introduction of 5G networks. According to recent researches, obtaining acceptable End-to-End (E2E) delay has been a challenging design process since data travels through several steps from the originating source to the data center and vice versa. V2X latency comprises mainly of three levels: source processing, cellular network, and data center processing. Delay reduction can be achieved on the three levels. However, many conventional solutions have not reached the required and acceptable range of latency to enable V2X communication over cellular networks. In this paper, a general review of challenges to make V2X feasible on cellular network has been discussed, and the proposed solutions in the literature has been introduced. As a conclusion, a various types of aiding tools to design and test V2X tools are given, so that a right path should be taken to consider challenges and improving design metrics.

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“…VANETs naturally inherit basic characteristics of Mobile Ad hoc NETworks (MANETs), but possess special features, such as the high mobility and varying network densities. The future fifth generation (5G) network [3,4] is supposed to provide high reliability and low latency communications. The arrival of 5G greatly promotes the development of Internet of Things (IoT) technology.…”

Section: Introductionmentioning

confidence: 99%

SDN-based Handover Scheme in Cellular/IEEE 802.11p Hybrid Vehicular Networks

Duo

Yoshinaga

et al. 2020

Sensors

Self Cite

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With the arrival of 5G, the wireless network will be provided with abundant spectrum resources, massive data transmissions and low latency communications, which makes Vehicle-to-Everything applications possible. However, VANETs always accompany with frequent network topology changes due to the highly mobile feature of vehicles. As a result, the network performance will be affected by the frequent handover. In this paper, a seamless handover scheme is proposed where the Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies are employed to adapt to the dynamic topology change in VANETs. The introduction of SDN provides a global view of network topology and centralized control, which enables a stable transmission layer connection when a handover takes place, so that the upper layer performance is not influenced by the network changes. By employing MEC server, the data are cached in advance before a handover happens, so that the vehicle can restore normal communication faster. In order to confirm the superiority of our proposal, computer simulations are conducted from different aspects. The results show that our proposal can significantly improve the network performance when a handover happens.

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Edge Computing in 5G: A Review

Cited by 286 publications

References 79 publications

Proactive Edge Caching in Content-Centric Networks With Massive Dynamic Content Requests

Proactive Edge Caching in Content-Centric Networks With Massive Dynamic Content Requests

Minimizing E2e Delay in V2x Over Cellular Networks: Review and Challenges

SDN-based Handover Scheme in Cellular/IEEE 802.11p Hybrid Vehicular Networks

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