Multi-Criteria Application Placement in Fog Computing

Dankovtsev, V. I.; Toutova, Natalia V.; Vorozhtsov, Anatoly S.; Andreev, Ilya

doi:10.1109/tirved56496.2022.9965545

Cited by 1 publication

(2 citation statements)

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“…To address resource allocation challenges, [11] explored multi-criteria methods for cost-based drone selection, focusing on optimizing selection criteria based on cost. In [12], the emphasis shifted to deploying fog applications for optimizing the quality of experience (QoE), utilizing a multi-criteria approach that considered both time and cost. Proposing BE-HAVE in [30], the authors targeted efficient utility optimization in multi-tier wireless networks, specifically allocating edge resources to heterogeneous IoT devices.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, an effective method is required to optimize resource utilization to improve network performance by considering several criteria and the combination of different communication protocols and technologies. Effective techniques for multi-criteria decision-making in [11] and compute-intensive application placement strategies in [12], may provide valuable insights to tackle challenges related to efficient resource management for applications that require high computation.…”

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confidence: 99%

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Framework for Optimized Resource Allocation in Multi-User, Multi-Service, Multi-Device Aerial Networks

Mushtaq,

Chughtai,

Zhang

et al. 2024

IEEE Access

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With the increasing prevalence of multi-user, multi-service, and heterogeneous multi-device environments, there is a need to address the imperative for efficient resource allocation in contemporary wireless networks, such as those involving unmanned aerial vehicles (UAVs) or drones. In this regard, this work addresses the challenges within a five-dimensional heterogeneous wireless network model, focusing on diverse services such as Big Data Analytics, Video Rendering, and Computer-Aided Design, and the allocation of resources among heterogeneous devices, including UAVs, tethered balloons, and multi-rotors. The resource allocation is facilitated through multiple interfaces like LTE, Wifi, LoRa, and Sigfox, catering to the diverse needs of users operating in aerial Networks. Additionally, this work introduces a novel Intelligent Relaxation using the Penalty Function (IRPF) approach for resource allocation, treating it as an integer programming problem to balance user needs while ensuring affordability. A comparative analysis is conducted between the proposed approach and the traditional branch-and-bound algorithm. In scenarios requiring resource allocation for numerous services based on user demand and device capabilities, the proposed work presents a penalty-based integrality gap solution adept at managing fractional values. The resulting optimization framework is meticulously designed to minimize activation and operating costs while optimizing utility. Additionally, the computing efficiency of the proposed approach is demonstrated by extensive simulations that prove its superiority over the traditional algorithm. Consequently, this research emphasizes the essential role of the proposed model in navigating the intricate challenges of resource allocation in modern drone-centric wireless networks.

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Section: Literature Reviewmentioning

confidence: 99%

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confidence: 99%