Adaptive Edge-Centric Cloud Content Placement for Responsive Smart Cities

Sinky, Hassan; Khalfi, Bassem; Hamdaoui, Bechir; Rayes, Ammar

doi:10.1109/mnet.2019.1800137

Cited by 22 publications

(25 citation statements)

References 12 publications

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“…Indeed, various studies in literature do exist for dealing with these challenges. Similar to this, the authors in References [ 12 , 13 ] proposed use of small distributed data centers in entire network to reduce burden at core cloud network, and some works [ 14 , 15 , 16 ] offer prefetching of content at edge nodes to alleviate and control back-haul traffic. Although the concept of collaborative filtering was utilized by various online business applications, websites and live streaming services for generating recommendations for user’s preferences, yet, found its application in networking domain after the proposal presented in Reference [ 17 ].…”

Section: Introductionmentioning

confidence: 81%

Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications

Gupta

Rani

Ahmed³

et al. 2021

Sensors

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The substantial advancements offered by the edge computing has indicated serious evolutionary improvements for the internet of things (IoT) technology. The rigid design philosophy of the traditional network architecture limits its scope to meet future demands. However, information centric networking (ICN) is envisioned as a promising architecture to bridge the huge gaps and maintain IoT networks, mostly referred as ICN-IoT. The edge-enabled ICN-IoT architecture always demands efficient in-network caching techniques for supporting better user’s quality of experience (QoE). In this paper, we propose an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture. This collaborative filtering-based content caching strategy would intelligently cache content on edge nodes for traffic management at cloud databases. The evaluations has been conducted to check the performance of the proposed strategy over various benchmark strategies, such as LCE, LCD, CL4M, and ProbCache. The analytical results demonstrate the better performance of our proposed strategy with average gain of 15% for cache hit ratio, 12% reduction in content retrieval delay, and 28% reduced average hop count in comparison to best considered LCD. We believe that the proposed strategy will contribute an effective solution to the related studies in this domain.

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Section: Introductionmentioning

confidence: 81%

Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications

Gupta

Rani

Ahmed³

et al. 2021

Sensors

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“…More specifically, the problem is modeled as a 0-1 integer programming model and makes decisions through a variant of intelligent swarm optimization based on dependencies between data and the reliability of the storage hardware, among others. A content centric approach is the focus of [128] where a scheme is described around four complementary to each other algorithms for data caching at the edge. Each of these algorithms focuses on one of the following features to make latency minimum: data popularity, data heterogeneity, user mobility, and resource availability.…”

Section: Replica Placementmentioning

confidence: 99%

On the Use of Intelligent Models towards Meeting the Challenges of the Edge Mesh

et al. 2021

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Nowadays, we are witnessing the advent of the Internet of Things (IoT) with numerous devices performing interactions between them or with their environment. The huge number of devices leads to huge volumes of data that demand the appropriate processing. The “legacy” approach is to rely on Cloud where increased computational resources can realize any desired processing. However, the need for supporting real-time applications requires a reduced latency in the provision of outcomes. Edge Computing (EC) comes as the “solver” of the latency problem. Various processing activities can be performed at EC nodes having direct connection with IoT devices. A number of challenges should be met before we conclude a fully automated ecosystem where nodes can cooperate or understand their status to efficiently serve applications. In this article, we perform a survey of the relevant research activities towards the vision of Edge Mesh (EM), i.e., a “cover” of intelligence upon the EC. We present the necessary hardware and discuss research outcomes in every aspect of EC/EM nodes functioning. We present technologies and theories adopted for data, tasks, and resource management while discussing how machine learning and optimization can be adopted in the domain.

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“…Sometimes it is necessary to determine what is the number of servers that would produce a best trade-off between the budget and QoS. To explore this trade-off, some previous work [17], [18] evaluate the average user latency as a function of the number of edge servers.…”

Section: Related Workmentioning

confidence: 99%

“…For example, in the study by Wand et al [17] a fixed number of edge servers are placed by minimizing the geospatial distances while concurrently seeking for a balanced workload distribution. In [18] a hierarchical treelike structures are used to locate fixed number of edge servers without capacity limits. Yin et al [19] propose a heuristic decision-support management system for server placement that enables the discovery of unforeseen server locations.…”

Section: Related Workmentioning

confidence: 99%

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Scaling up an Edge Server Deployment

Lovén

Lähderanta

Ruha

et al. 2020

2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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In this article, we study the scaling up of edge computing deployments. In edge computing, deployments are scaled up by adding more computational capacity atop the initial deployment, as deployment budgets allow. However, without careful consideration, adding new servers may not improve proximity to the mobile users, crucial for the Quality of Experience of users and the Quality of Service of the network operators. In this paper, we propose a novel method for scaling up an edge computing deployment by selecting the optimal number of new edge servers and their placement, and re-allocating access points optimally to the old and new edge servers. The algorithm is evaluated with two scenarios, using data on a real-world large-scale wireless network deployment. The evaluation shows that the proposed method is stable on a real city-scale deployment, resulting in optimized Quality of Service for the network operator.

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Adaptive Edge-Centric Cloud Content Placement for Responsive Smart Cities

Cited by 22 publications

References 12 publications

Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications

Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications

On the Use of Intelligent Models towards Meeting the Challenges of the Edge Mesh

Scaling up an Edge Server Deployment

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