A Novel 5G-NR Resources Partitioning Framework Through Real-Time User-Provider Traffic Demand Analysis

Maule, Massimiliano; Vardakas, John S.; Verikoukis, Christos

doi:10.1109/jsyst.2021.3115896

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…The set of service priorities for four levels is [0.8, 0.6, 0.4, 0.2], and the set of minimum expected constraints for four levels is [0.8, 0.7, 0.6, 0.5]. The simulation parameters are summarized in Table 1 [45], [46].…”

Section: Simulation Resultsmentioning

confidence: 99%

Cloud-Edge-Device Collaborative High Concurrency Access Management for Massive IoT Devices in Distribution Grid

LI,

YOU,

ZHANG

et al. 2024

IEICE Trans. Fundamentals

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Emerging data-intensive services in distribution grid impose requirements of high-concurrency access for massive internet of things (IoT) devices. However, the lack of effective high-concurrency access management results in severe performance degradation. To address this challenge, we propose a cloud-edge-device collaborative high-concurrency access management algorithm based on multi-timescale joint optimization of channel pre-allocation and load balancing degree. We formulate an optimization problem to minimize the weighted sum of edge-cloud load balancing degree and queuing delay under the constraint of access success rate. The problem is decomposed into a large-timescale channel pre-allocation subproblem solved by the device-edge collaborative access priority scoring mechanism, and a small-timescale data access control subproblem solved by the discounted empirical matching mechanism (DEM) with the perception of high-concurrency number and queue backlog. Particularly, information uncertainty caused by externalities is tackled by exploiting discounted empirical performance which accurately captures the performance influence of historical time points on present preference value. Simulation results demonstrate the effectiveness of the proposed algorithm in reducing edgecloud load balancing degree and queuing delay.

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Section: Simulation Resultsmentioning

confidence: 99%