Social Network Aware Dynamic Edge Server Placement for Next-Generation Cellular Networks

Manasvi, G.; Chakraborty, Abhishek; Manoj, B. S.

doi:10.1109/comsnets48256.2020.9027421

Cited by 10 publications

(8 citation statements)

References 9 publications

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“…[40], EC refers to a kind of computing paradigm in which the capabilities of processing, communication, and intelligence are pushed to the edge of network systems from which data originate. EC is also a kind of distributed computing mode [41] which can solve some of the main issues by transferring storage resources and computation from centralized points, which is, in other words, pushing data, all kinds of services and applications closer to the requests geographically [42] . In theory, EC is an extension of the content delivery network architecture.…”

Section: Edge Computingmentioning

confidence: 99%

Artificial intelligence for edge service optimization in Internet of Vehicles: A survey

et al. 2022

Tsinghua Sci. Technol.

140

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The Internet of Vehicles (IoV) plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information. Generally, collected information is transmitted to a centralized resourceintensive cloud platform for service implementation. Edge Computing (EC) that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users. Additionally, many measures are adopted to optimize the performance of EC-enabled IoV, but they hardly help make dynamic decisions according to real-time requests. Artificial Intelligence (AI) is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically. Although extensive research has employed AI to optimize EC performance, summaries with relative concepts or prospects are quite few. To address this gap, we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV. Firstly, we establish the general condition and relative concepts about IoV, EC, and AI. Secondly, we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading. Finally, we discuss a number of open issues in optimizing edge services with AI.

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Section: Edge Computingmentioning

confidence: 99%

Artificial intelligence for edge service optimization in Internet of Vehicles: A survey

et al. 2022

Tsinghua Sci. Technol.

140

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“…Most of the works consider the edge server placement as a placement problem by selecting K locations from N candidates with different objections. In this case, generally the edge servers are co-located with access points [8][9][10][11][12][13][14][15][16] or other network functions [17,18]. These works are marked as yes in the column of Co-located.…”

Section: B Physical Resources Placementmentioning

confidence: 99%

“…They formulate this problem as a capacitated location-allocation problem and evaluate it in edge computing and fog computing environments respectively. Based on the available information gathered from the overlay social network groups, an edge sever placement strategy is proposed in [13] to identify a number of critical BSs to place edge servers while supporting low latency communication.…”

Section: B Physical Resources Placementmentioning

confidence: 99%

“…Content may change prior to final publication. [9] no yes yes fixed limited minimized Y. Li et al [10] no no yes fixed limited threshold H. Yin et al [33] no no no unfixed limited minimized T. Lahderanta et al [11] share yes yes fixed limited minimized J. Meng et al [34] share no yes fixed limited minimized Y. Li et al [12] no no no fixed no threshold K. Xiao et al [13] no no yes fixed limited minimized G. Manasvi et al [14] share no yes fixed limited minimized S. Lee et al [17] no yes yes minimized limited threshold T. Chin et al [15] no yes yes fixed no not used X. Xu et al [18] no yes yes fixed limited minimized edge servers, as well as the allocation of server computing resources to provide services towards BSs. The problem is essentially driven by the stochastic BS resource demands, which brings difficulties in practically solving the problem with low complexity.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Among all these factors, the uncertain resource requirements rising from mobile end users (such as users' computing tasks) is the key driven factor for the distribution of edge servers, because the execution resources are deployed to fulfill these requirements. In the literature, a number of studies has dealt with deploying servers in the MEC infrastructure [8][9][10][11][12][13][14][15][16][17][18]. However, these existing studies assume that the resource requirements of end users are deterministic, which largely ignores the pragmatic complexity of the real dynamic world.…”

Section: Introductionmentioning

confidence: 99%

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A Learning Based Framework for MEC Server Planning With Uncertain BSs Demands

et al. 2020

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Mobile Edge Computing (MEC) architecture is composed of geographically distributed edge servers, in which computing capabilities are provisioned at the boundary of the network, which is in close proximity to the end users to provide network services with low latency. The planning of MEC edge servers at appropriate locations is the fundamental first step towards the deployment of the MEC system. In the literature, edge servers planning is based on deterministic resource requirements. This assumption largely neglects the pragmatic complexities imposed by the real dynamic world, in which base station (BS) resource demands are stochastic variables with arbitrary pattern. In view of this fact, we formulate the MEC planning problem as a joint optimization problem of MEC edge servers placement and resource allocation with uncertain BS demands through an uncertain programming formulation. Due to the complexity of this jointuncertain problem, a learning based framework is utilized to practically solve this problem, and the relevance of applying this mechanism in practical usage with sampled arbitrary BS demands data is also discussed. Finally, we conducted intensive real-data driven simulations to evaluate the performance of our proposed mechanism. The results show the effectiveness of our approach with arbitrary BS demands. INDEX TERMS Mobile edge computing, edge server planning, uncertain programming, learning-based framework, resource optimization

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Edge server placement problem in multi-access edge computing environment: models, techniques, and applications

2023

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Social Network Aware Dynamic Edge Server Placement for Next-Generation Cellular Networks

Cited by 10 publications

References 9 publications

Artificial intelligence for edge service optimization in Internet of Vehicles: A survey

Artificial intelligence for edge service optimization in Internet of Vehicles: A survey

A Learning Based Framework for MEC Server Planning With Uncertain BSs Demands

Edge server placement problem in multi-access edge computing environment: models, techniques, and applications

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