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<p>Fog computing offers a promising solution to execute time-sensitive Internet of Things (IoT) applications. Nevertheless, inadequate resources available on fog devices pose challenges while using numerous applications. The major concern lies in the absence of promising techniques for analyzing and discovering the resources needed for several IoT applications, which makes the deployment process complex and may direct to inefficient resource allocation. Hence, developing new mechanisms for accurate resource estimation is significant for optimizing the reliability of IoT services within fog computing environments. A model named CAViaR Snow Leopard Optimization Algorithm <b>(</b>CaSLOA) is developed for Service placement strategy in fog computing is proposed. Initially, fog computing is simulated and then Service placement is done for the resource provisioning in fog environment by employing proposed CaSLOA by considering the multi-objective parameters, such as energy consumption, cost, service time, as well as makespan. Here, CaLSOA is the combination of Snow Leopard Optimization Algorithm (SLOA) and Conditional autoregressive Value at risk by Regression Quantiles (CAViaR). The performance metrics considered for CaSLOA are service cost, Makespan and service time that acquired 20.629, 0.526 and 3.540&#xa0;s.</p>

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CAViaR Snow Leopard Optimization Algorithm for Multi-Objective IoT Service Placement Strategy in Fog Computing

  • 期刊论文

  • 发表日期：

    2025年7月9日

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  • A. R. Gopinath,
  • R. Bhargava,
  • S. Swathi,
  • Abraham Rajan

摘要

Fog computing offers a promising solution to execute time-sensitive Internet of Things (IoT) applications. Nevertheless, inadequate resources available on fog devices pose challenges while using numerous applications. The major concern lies in the absence of promising techniques for analyzing and discovering the resources needed for several IoT applications, which makes the deployment process complex and may direct to inefficient resource allocation. Hence, developing new mechanisms for accurate resource estimation is significant for optimizing the reliability of IoT services within fog computing environments. A model named CAViaR Snow Leopard Optimization Algorithm (CaSLOA) is developed for Service placement strategy in fog computing is proposed. Initially, fog computing is simulated and then Service placement is done for the resource provisioning in fog environment by employing proposed CaSLOA by considering the multi-objective parameters, such as energy consumption, cost, service time, as well as makespan. Here, CaLSOA is the combination of Snow Leopard Optimization Algorithm (SLOA) and Conditional autoregressive Value at risk by Regression Quantiles (CAViaR). The performance metrics considered for CaSLOA are service cost, Makespan and service time that acquired 20.629, 0.526 and 3.540 s.