Computational offloading happens to be a prominent solution for leveraging the performance of handheld devices. It raises the feasibility of executing computation-intensive and latency-conscious tasks with the help of task migration to proximate cloud servers. However, longer Wide Area Network latencies of cloud and greater mobile data consumption paved the way to adopting opportunistic offloading instead of remote offloading. This proposed work uses an edge-based solution of Fog computing to handle such tasks and to provide the users with a high Quality of Experience. This paper presents a Capability-Aware Supply Chain Management Model (CASCM\(^2\)) as an extension of traditional Supply Chain Management (SCM) model. CASCM\(^2\) dynamically selects a crowd of competent mobile devices within a Foglet that is in proximity to the users and offloads the complex computational tasks to them. The proposed model aims at optimizing two parameters, such as communication overhead and conductance cost, as they possess a remarkable impact on offloading delay-sensitive tasks. Hence an overall objective optimization is achieved using a dual Lagrangian decomposition method, which subdivides and solves the optimization of parameters in parallel. Experimental analysis of the participator selection is performed for a single period as well as multiple periods. The performance results yield a considerable contribution that alleviates the issues in delay-sensitive applications deployed in the Fog framework.

计算分流恰好是利用手持设备性能的重要解决方案。通过将任务迁移到附近的云服务器，它提高了执行计算密集型和时延敏感型任务的可行性。但是，更长的云广域网延迟和更多的移动数据消耗为采用机会卸载而不是远程卸载铺平了道路。这项拟议的工作使用基于边缘的Fog计算解决方案来处理此类任务并为用户提供高质量的体验。本文提出了一种基于能力的供应链管理模型（CASCM \（^ 2 \）），作为对传统供应链管理（SCM）模型的扩展。CASCM \（^ 2 \）动态地在Foglet中选择了一个靠近用户的称职的移动设备，并将繁琐的计算任务分担给他们。该模型旨在优化两个参数，例如通信开销和电导成本，因为它们对卸载对延迟敏感的任务具有显着影响。因此，使用双重拉格朗日分解方法可实现总体目标优化，该方法可细分并并行求解参数的优化。对参与者选择的实验分析在单个周期以及多个周期中进行。性能结果产生了可观的贡献，减轻了在Fog框架中部署的对延迟敏感的应用程序中的问题。