With the widespread use of Mobile Edge Computing (MEC) in smart manufacturing systems in Industrial Internet of Things (IIoT) and 5G networks, determining how to efficiently offload computing tasks has become a hot research area. The Role-Based Collaboration (RBC) Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is introduced to comprehensively manage MEC servers and user computation tasks in edge network environments, thereby improving the effectiveness and performance of task offloading in smart manufacturing systems. To begin with, latency and energy consumption are important indicators for evaluating the offloading effect. A pre-allocation algorithm based on user latency tolerance is proposed to dynamically adjust the latency-energy consumption weighting factor to optimize system resource allocation for real-time adjustment of offloading decisions. Second, the Group Role Assignment of Agent Role Conflicts (GRACAR) model based on E-CARGO is extended, along with a dynamic weighting of the GRACAR (GRACAR-DW) model and formal modeling. By introducing resource contention constraints, the resource contention caused by excessive task data offloading to the same MEC server is proactively mitigated. Finally, a Gurobi solution based on Mixed-Integer Linear Programming (MILP) is developed to help validate and synthesize the proposed model. Simulation results show that the strategy considerably enhances the MEC system's overall performance in terms of latency and energy consumption while also providing new ideas and technological support for offloading decisions in edge networks.

中文翻译：

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基于 E-CARGO 的动态减重策略，可缓解边缘网络的资源争用


随着移动边缘计算（MEC）在工业物联网（IIoT）和5G网络的智能制造系统中的广泛使用，确定如何有效卸载计算任务已成为热门研究领域。引入基于角色的协作（RBC）环境-类、代理、角色、组和对象（E-CARGO）模型，全面管理边缘网络环境中的MEC服务器和用户计算任务，从而提高任务的有效性和性能智能制造系统中的卸载。首先，时延和能耗是评估卸载效果的重要指标。提出一种基于用户延迟容忍度的预分配算法，动态调整延迟-能耗权重因子，优化系统资源分配，实时调整卸载决策。其次，扩展了基于E-CARGO的组角色分配代理角色冲突（GRACAR）模型，以及GRACAR（GRACAR-DW）模型的动态加权和形式化建模。通过引入资源争用约束，主动缓解由于过多任务数据卸载到同一 MEC 服务器而导致的资源争用。最后，开发了基于混合整数线性规划（MILP）的 Gurobi 解决方案，以帮助验证和综合所提出的模型。仿真结果表明，该策略显着提升了MEC系统在时延和能耗方面的整体性能，同时也为边缘网络的卸载决策提供了新的思路和技术支持。