Cluster-Based Multi-User Multi-Server Caching Mechanism in Beyond 5G/6G MEC

Samir, Rasha; El‐Hennawy, Hadia; El-Badawy, Hesham M.

doi:10.3390/s23020996

Cited by 4 publications

(5 citation statements)

References 44 publications

(75 reference statements)

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“…In the future, the following directions will be investigated: (1) In this study, only constant column bicluster is investigated. In real cases, due to the widespread noise, column nearly constant bicluster may be more frequently seen.…”

Section: Discussionmentioning

confidence: 99%

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Incrementally Mining Column Constant Biclusters with FVSFP Tree

2023

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Bicluster mining has been frequently studied in the data mining field. Because column constant biclusters (CCB) can be transformed to be discriminative rules, they have been widely applied in various fields. However, no research on incrementally mining CCB has been reported in the literature. In real situations, due to the limitation of computation resources (such as memory), it is impossible to mine biclusters from very large datasets. Therefore, in this study, we propose an incremental mining CCB method. CCB can be deemed as a special case of frequent pattern (FP). Currently the most frequently used method for incrementally mining frequent patterns is FP tree based method. In this study, we innovatively propose an incremental mining CCB method with modified FP tree data structure. The technical contributions lie in two aspects. The first aspect is that we propose a modified FP tree data structure, namely Feature Value Sorting Frequent Pattern (FVSFP) tree that can be easily maintained. The second aspect is that we innovatively design a method for mining CCB from FVSFP tree. To verify the performance of the proposed method, it is tested on several datasets. Experimental results demonstrated that the proposed method has good performance for incrementally handling a newly added dataset.

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Section: Discussionmentioning

confidence: 99%

“…Clustering is an important part of data mining [1]. Clustering can group samples into different clusters.…”

Section: Introductionmentioning

confidence: 99%

Incrementally Mining Column Constant Biclusters with FVSFP Tree

2023

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“…We allocate patients’ medical records to the SDN edge node for caching which is set up appropriate to the patient's life, close to the closest locations. Additionally, we proposed using the MFO algorithm [ 31 ] to cache multiple patients' medical records. Essentially, MFO-Edge Caching involves decision-making with respect to multiple criteria, and in operations research, it is considered.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, we proposed using the MFO algorithm [ 31 ] to cache multiple patients' medical records. Essentially, MFO-Edge Caching involves decision-making with respect to multiple criteria, and in operations research, it is considered.…”

Section: Methodsmentioning

confidence: 99%

“…[ 29 ] proposes a D2D fog network architecture based on federated learning and QoS to estimate user preferences for content caching decision-making, while [ 30 ] proposes MagNet, a decentralized and cooperative edge caching system that utilizes Automatic Content Congregating and Mutual Assistance Group mechanisms to improve hit ratio and workload balance. [ 31 ] Discusses the advantages of SDN and edge caching in mobile edge computing networks and presents a proposed system model and caching algorithm that outperforms existing models in terms of hit rate, time delay, and traffic reduction. [ 2 ] proposes a mechanism to determine optimal content placement in a distributed edge deployment with limited storage resources to minimize data retrieval latency, and [ 32 ] proposes a data prioritization-based approach that utilizes fog computing to improve cache efficiency in ICN for IoT data.…”

Section: Related Workmentioning

confidence: 99%

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Composition of caching and classification in edge computing based on quality optimization for SDN-based IoT healthcare solutions

et al. 2023

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This paper proposes a novel approach that uses a spectral clustering method to cluster patients with e-health IoT devices based on their similarity and distance and connect each cluster to an SDN edge node for efficient caching. The proposed MFO-Edge Caching algorithm is considered for selecting the near-optimal data options for caching based on considered criteria and improving QoS. Experimental results demonstrate that the proposed approach outperforms other methods in terms of performance, achieving decrease in average time between data retrieval delays and the cache hit rate of 76%. Emergency and on-demand requests are prioritized for caching response packets, while periodic requests have a lower cache hit ratio of 35%. The approach shows improvement in performance compared to other methods, highlighting the effectiveness of SDN-Edge caching and clustering for optimizing e-health network resources.

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Distribution of resources beyond 5G networks with heterogeneous parallel processing and graph optimization algorithms

Khadidos,

Manoharan,

Selvarajan

et al. 2024

Cluster Comput

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In this paper, a design model for resource allocation is formulated beyond 5G networks for effective data allocations in each network nodes. In all networks, data is transmitted only after allocating all resources, and an unrestrained approach is established because the examination of resources is not carried out in the usual manner. However, if data transmission needs to occur, some essential resources can be added to the network. Moreover, these resources can be shared using a parallel optimization approach, as outlined in the projected model. Further the designed model is tested and verified with four case studies by using resource allocator toolbox with parallax where the resources for power and end users are limited within the ranges of 1.4% and 6%. Furthermore, in the other two case studies, which involve coefficient determination and blockage factors, the outcomes of the proposed approach fall within the marginal error constraint of approximately 31% and 87%, respectively.

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Cluster-Based Multi-User Multi-Server Caching Mechanism in Beyond 5G/6G MEC

Cited by 4 publications

References 44 publications

Incrementally Mining Column Constant Biclusters with FVSFP Tree

Incrementally Mining Column Constant Biclusters with FVSFP Tree

Composition of caching and classification in edge computing based on quality optimization for SDN-based IoT healthcare solutions

Distribution of resources beyond 5G networks with heterogeneous parallel processing and graph optimization algorithms

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