论文标题
在具有潜伏期限制的雾计算网络中的能源优化的任务分配
Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints
论文作者
论文摘要
雾网络提供与最终用户不同距离不同能力的计算资源。与云相比，靠近网络边缘的FOG节点（FN）可能具有强大的计算资源，但是在FN限制长距离传输中的计算任务的处理。如何在雾和云节点之间分布任务？我们制定了通用的非凸层混合构成非线性编程（MINLP）问题，最大程度地限制任务传输和与处理相关的能量，并具有延迟约束以回答这个问题。它通过连续的凸近似（SCA）转换，并使用原始和双重分解技术进行分解。提出了两种称为节能资源分配（EEFFRA）和低复杂性（LC）-EEFFRA的实用算法。在各种情况下，它们允许在FNS和云之间成功分发网络请求，从而大大降低了平均能源成本，并减少了具有未完成延迟要求的计算请求数量。
Fog networks offer computing resources with varying capacities at different distances from end users. A Fog Node (FN) closer to the network edge may have less powerful computing resources compared to the cloud, but processing of computational tasks in an FN limits long-distance transmission. How should the tasks be distributed between fog and cloud nodes? We formulate a universal non-convex Mixed-Integer Nonlinear Programming (MINLP) problem minimizing task transmission- and processing-related energy with delay constraints to answer this question. It is transformed with Successive Convex Approximation (SCA) and decomposed using the primal and dual decomposition techniques. Two practical algorithms called Energy-EFFicient Resource Allocation (EEFFRA) and Low-Complexity (LC)-EEFFRA are proposed. They allow for successful distribution of network requests between FNs and the cloud in various scenarios significantly reducing the average energy cost and decreasing the number of computational requests with unmet delay requirements.
