5G Network Data Migration Service Based on Edge Computing

Li, Furong

doi:10.3390/sym13112134

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…When nodes have a high load or do not own data requested by the incoming tasks, those tasks could be offloaded to peers or Cloud [6,17]. Other mitigation actions that nodes can adopt to increase the efficiency of the requested processing are the migration of services [5] or data [18,19]. Both activities try to fill gaps in the capabilities of nodes to avoid any malfunctions or delays in the provision of responses.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Proactive Data Management in Support of Pervasive Edge Applications

Kolomvatsos,

Anagnostopoulos

2023

Preprint

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Section: Related Workmentioning

confidence: 99%

Autonomous Proactive Data Management in Support of Pervasive Edge Applications

Kolomvatsos,

Anagnostopoulos

2023

Preprint

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“…The continuous cost of infrastructure is exemplified in managing the infrastructural components such as compute, storage and network by inhibiting several cost parameters such as the controller, network devices and rackspace as represented in Equation (4). The service provisioning cost is exemplified by calculating the time taken for a service to be reconfigured in a particular year and the service connection establishment time being represented in hours as shown in Equation (6).…”

Section: Operational Expenditure Cost (Opex)mentioning

confidence: 99%

“…In real time, the operators have to program their networks for the introduction of new services rather than manual configuration [5]. This adaptability is a key factor for the end users where there is variation in demands from providing a consistent state transition for storagebased distributed systems [6]. Globally, the data traffic has grown at a faster rate with the rapid deployment of 5G/6G networks.…”

Section: Introductionmentioning

confidence: 99%

Optimization of 5G/6G Telecommunication Infrastructure through an NFV-Based Element Management System

et al. 2022

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Network Function Virtualization (NFV) is an enabling technology that brings together automated network service management and corresponding virtualized network functions that use an NFV Infrastructure (NFVI) framework. The Virtual Network Function Manager (VNFM) placement in a large-scale distributed NFV deployment is therefore a challenging task due to the potential negative impact on performance and operating expense cost. The VNFM assigns Virtual Network Functions (VNFs) and operates efficiently based on network demands with resilient performance through efficient placement techniques. The degradation in performance and a tremendous increase in capital expenditure and operating expenses indicated this chaotic problem. This article proposed a method for VNFM placement using information on the resources of each nodes’ Element Manager (EM), which is an efficient method to assign VNFs to each node of element management systems. In addition, this paper proposed an Optimized Element Manager (OEM) method for looking at appropriate EMs for the placement of VNF through periodic information on available resources. It also overcomes challenges such as delays and variations in VNFs workload for edge computing and distributed cloud regions. The performance is measured based on computations performed on various optimization algorithms such as linear programming and tabu search algorithms. The advent of the new service provisioning model of BGP-EVPN for VXLAN is materialized by integrating VTS with OpenStack. The numerical analysis shows that the proposed OEM algorithm gives an optimal solution with an average gap of 8%.

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“…MEC mitigates the end-to-end latency of service delivery by moving the cloud computing platform to the edge of the network. Furthermore, it can hold various business scenarios such as smart device applications, health monitoring, connected vehicles, 5G network data migration service [5][6][7], and even a satellite-terrestrial network [8]. Therefore, the MEC has attracted extensive attention from the industry since its birth.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation on Blockchain Enabled Mobile Edge Computing System

et al. 2022

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Currently, the concept of Mobile Edge Computing (MEC) has been applied as a solution against the plethora of demands for high-quality computing services. It comprises several essential processes, such as resource allocation, data transmission, and task processing. Furthermore, researchers applied blockchain technology, aiming to enhance the robustness of the MEC system. At present, resource allocation in the MEC system is a very hot field, but there are still some problems in the resource allocation process under the traditional MEC architecture, such as privacy disclosure and so on. Moreover, the resource allocation problem in a blockchain-enabled MEC system will be more complicated, while the mining process may have an impact on resource allocation policy. To address this issue, this paper investigates the resource allocation problem with blockchain-based MEC system architecture. A brand new consensus mechanism: proof of learning (PoL), is applied to the system, which does not waste the computing resources of edge computing servers. Based on this, we modeled the system mathematically, focusing on server processing latency, mining latency, rewards under the new consensus, and total cost. The asynchronous advantage Actor-Critic (A3C) algorithm is used to optimize resource allocation policy. To better capture the long-time trend of the system, the temporal convolutional network (TCN) is implemented to represent the policy function and state-value function in the reinforcement learning model. The results show that the A3C algorithm based on TCN not only converges faster but also is more stable.

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5G Network Data Migration Service Based on Edge Computing

Cited by 7 publications

References 24 publications

Autonomous Proactive Data Management in Support of Pervasive Edge Applications

Autonomous Proactive Data Management in Support of Pervasive Edge Applications

Optimization of 5G/6G Telecommunication Infrastructure through an NFV-Based Element Management System

Resource Allocation on Blockchain Enabled Mobile Edge Computing System

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