基于集群协作的云雾混合计算资源分配和负载均衡策略

针对物联网(IoT)中智能应用快速增长导致的移动网络数据拥塞问题，该文构建了一种基于雾集群协作的云雾混合计算模型，在考虑集群负载均衡的同时引入权重因子以平衡计算时延和能耗，最终实现系统时延能耗加权和最小。为了解决该混合整数非线性规划问题，将原问题分解后采用库恩塔克(KKT)条件和二分搜索迭代法对资源配置进行优化，提出一种基于分支定界的开销最小化卸载算法(BB-OMOA)获得最优卸载决策。仿真结果表明，集群协作模式显著提高了系统负载均衡度，且所提策略在不同参数条件下明显优于其他基准方案。

Resource Allocation and Load Balancing Strategy in Cloud-fog Hybrid Computing Based on Cluster-collaboration

Considering the problem of data congestion in mobile networks caused by the rapid growth of smart applications in Internet of Things (IoT), a cloud-fog hybrid computing model based on cluster-collaboration is constructed. The cluster load balancing is considered while introducing weighting factors to balance the computational latency and energy consumption, and finally the minimum weighted sum of system latency and energy consumption is achieved. In order to solve this mixed integer nonlinear programming problem, the original problem is decomposed to optimize the resource allocation using Karush-Kuhn-Tucker (KKT) condition and bisection search iterative method. Then an Overhead Minimization Offloading Algorithm based on Branch and Brand (BB-OMOA) is proposed to obtain the optimal offloading decision. Simulation results show that the cluster-collaboration model improves significantly the system load balancing degree and the proposed strategy outperforms significantly other benchmark schemes.