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<p>Mobile edge computing (MEC) brings computation and data storage closer to mobile users, reducing latency and improving the quality of services by leveraging edge computing resources located at the mobile network’s edge. Development of lightweight and efficient algorithms are required for extension of cloud computing to the edge of the network and optimal offload scheduling of the terminal tasks. In this paper, we present a three-layer edge-fog-cloud architecture-based energy and latency efficient task offloading (ELETO) framework. Our design scheme distributes workloads across edge, fog, and cloud to reduce end-to-end delay and energy consumption at the device level. Our main contribution is an offloading algorithm that considers both computing resource availability and task deadlines. The simulation results demonstrate that the proposed framework outperform the traditional offloading schemes greatly, minimizing the overall energy consumption and latency. These findings establish that a coordinated edge-fog-cloud strategy is an effective means of balancing energy-efficient and low-latency computations in IoT systems. Performance of the proposed scheme is compared to the existing Bender’s decomposition-based cloudlet Placement and task Allocation (BDPA) approach. Based on the comparative analyses, the proposed ELETO scheme is better than the existing BDPA scheme as it provides 40% less energy consumption and around 50s less delay.</p>

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Energy efficient low latency three-layer framework for task offloading in edge-fog-cloud environment

  • 期刊论文

  • 发表日期：

    2025年10月25日

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  • Aysha Tabassum,
  • Monir Hossen,
  • Istiaq Ahmed Fahim

摘要

Mobile edge computing (MEC) brings computation and data storage closer to mobile users, reducing latency and improving the quality of services by leveraging edge computing resources located at the mobile network’s edge. Development of lightweight and efficient algorithms are required for extension of cloud computing to the edge of the network and optimal offload scheduling of the terminal tasks. In this paper, we present a three-layer edge-fog-cloud architecture-based energy and latency efficient task offloading (ELETO) framework. Our design scheme distributes workloads across edge, fog, and cloud to reduce end-to-end delay and energy consumption at the device level. Our main contribution is an offloading algorithm that considers both computing resource availability and task deadlines. The simulation results demonstrate that the proposed framework outperform the traditional offloading schemes greatly, minimizing the overall energy consumption and latency. These findings establish that a coordinated edge-fog-cloud strategy is an effective means of balancing energy-efficient and low-latency computations in IoT systems. Performance of the proposed scheme is compared to the existing Bender’s decomposition-based cloudlet Placement and task Allocation (BDPA) approach. Based on the comparative analyses, the proposed ELETO scheme is better than the existing BDPA scheme as it provides 40% less energy consumption and around 50s less delay.