Hierarchical MEC Servers Deployment and User-MEC Server Association in C-RANs over WDM Ring Networks

Liu, Zhen; Zhang, Jiawei; Li, Yanan

doi:10.3390/s20051282

Cited by 14 publications

(7 citation statements)

References 30 publications

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“…Equation ( 19) represents the server placement strategy to be solved and Equation (20) represents four objective functions that need to be optimized during placement strategy search. Equation (22) guarantees that only one edge server can be placed at each selected location.…”

Section: Problem Formulationmentioning

confidence: 99%

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Edge server placement for offloading real‐time statistics tasks of bus passenger

Guo

Shang

et al. 2022

Trans Emerging Tel Tech

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With the continuous development of smart bus systems, higher requirements are expected for the accuracy and timeliness of the real‐time statistics of bus passengers. Although the statistical method of image and video processing based on deep learning has higher accuracy, it has higher requirements on the computing power and hardware equipment of the computer. A traditional solution is cloud computing, but cloud computing cannot meet real‐time requirements due to long‐distance transmission. In order to meet the real‐time demand, it can be offloaded to the edge of the network and processed by edge servers. In edge computing, the location of the edge server will have a great impact on the access delay and the traffic load in the edge network. Currently, few people optimize the traffic load in the edge network during the placement process. In view of this, an edge server placement algorithm for task offloading, named ESPTO, is designed to balance the average delay and traffic load under the control of each edge server while minimizing the average delay and traffic load in the edge network. First, a decomposition‐based multi‐objective evolutionary algorithm (MOEA/D) is used to find a better set of placement strategies, and then the optimal placement strategy is obtained through TOPSIS. Experimental results based on the Hangzhou bus station dataset prove the effectiveness of ESPTO.

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Section: Problem Formulationmentioning

confidence: 99%

“…The cost is also an issue to be considered during the deployment of edge servers. Liu et al 22 studied hierarchical deployment of MEC servers and user allocation problems and formulated the problem as a mixed integer nonlinear programming model to minimize the deployment cost and average latency.…”

Section: Related Workmentioning

confidence: 99%

Edge server placement for offloading real‐time statistics tasks of bus passenger

Guo

Shang

et al. 2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…The user-MEH association problem can consider different aspects, such as i) the enhancement of the user allocation rate, server hiring cost, and energy consumption by means of an association scheme able to consider that edge users' resource demands arrive and depart dynamically [189], ii) the dynamic balancing of the computation load for neighboring MEHs [190]. Other approaches jointly consider the MEH placement and association problem with the goal of minimizing the deployment cost [191] or number of MEH [192] while guaranteeing certain end-to-end service latency.…”

Section: Yesmentioning

confidence: 99%

5G Multi-access Edge Computing: Security, Dependability, and Performance

Nencioni,

Garroppo,

Olimid

2021

Preprint

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The main innovation of the Fifth Generation (5G) of mobile networks is the ability to provide novel services with new and stricter requirements. One of the technologies that enable the new 5G services is the Multi-access Edge Computing (MEC). MEC is a system composed of multiple devices with computing and storage capabilities that are deployed at the edge of the network, i.e., close to the end users. MEC reduces latency and enables contextual information and real-time awareness of the local environment. MEC also allows cloud offloading and the reduction of traffic congestion. Performance is not the only requirement that the new 5G services have. New missioncritical applications also require high security and dependability. These three aspects (security, dependability, and performance) are rarely addressed together. This survey fills this gap and presents 5G MEC by addressing all these three aspects. First, we overview the background knowledge on MEC by referring to the current standardization efforts. Second, we individually present each aspect by introducing the related taxonomy (important for the not expert on the aspect), the state of the art, and the challenges on 5G MEC. Finally, we discuss the challenges of jointly addressing the three aspects.

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“…When it comes to multilayer networks, the problem becomes even more complicated, because the simple superposition of optimal strategies in a single layer usually cannot obtain the optimal strategy in multi-layer networks. Liu et al [49] studied the hierarchical deployment of mobile edge computing servers and the user allocation problem. Their hierarchical deployment is relative to specific 5G entities, and no upper limit to access delay was considered.…”

Section: Name-resolution Server Placement In Information-centric Netw...mentioning

confidence: 99%

Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking

Sheng

Deng

2020

Applied Sciences

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Information-centric networking (ICN) is an emerging network architecture that has the potential to address demands related to transmission latency and reliability in fifth-generation (5G) communication technology and the Internet of Things (IoT). As an essential component of ICN, name resolution provides the capability to translate identifiers into locators. Applications have different demands on name-resolution latency. To meet the demands, deploying name-resolution servers at the edge of the network by dividing it into multilayer overlay networks is effective. Moreover, optimization of the deployment of distributed name-resolution servers in such networks to minimize deployment costs is significant. In this paper, we first study the placement problem of the name-resolution server in ICN. Then, two algorithms called IIT-DOWN and IIT-UP are developed based on the heuristic ideas of inter-layer information transfer (IIT) and server reuse. They transfer server placement information and latency information between adjacent layers from different directions. Finally, experiments are conducted on both simulation networks and a real-world dataset. The experimental results reveal that the proposed algorithms outperform state-of-the-art algorithms such as the latency-aware hierarchical elastic area partitioning (LHP) algorithm in finding more cost-efficient solutions with a shorter execution time.

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Hierarchical MEC Servers Deployment and User-MEC Server Association in C-RANs over WDM Ring Networks

Cited by 14 publications

References 30 publications

Edge server placement for offloading real‐time statistics tasks of bus passenger

Edge server placement for offloading real‐time statistics tasks of bus passenger

5G Multi-access Edge Computing: Security, Dependability, and Performance

Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking

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