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摘要: 为了构建绿色且长生命周期的物联网，本文提出了一种雾辅助的公平节能物联网计算迁移方案。首先，基于雾节点计算能力、带宽资源以及融合雾节点能耗公平性的迁移决策的联合考量，构建了一个最小化所有任务完成总能耗的优化问题。其次，提出了基于动量梯度和坐标协同下降的公平性能耗最小化算法用于解决上述混合整数非线性规划问题。该算法基于雾节点的历史平均能耗、距离、计算能力以及剩余能量值设计了公平性指标以获得对于雾节点能耗公平性最优的迁移决策；通过提出的动量梯度与坐标协同下降法，联合优化雾节点分配给各个任务的计算及带宽资源占比，达到最小化任务处理总能耗。最后，仿真结果表明本文方案能够取得较快的收敛速度，且与随机选择和贪婪任务迁移方案两种基准方案相比，本文方案的总能耗最低，雾节点的能耗公平性最高，且网络寿命分别平均提高了23.6%和31.2%。进一步地，该方案在不同雾节点数量以及不同任务大小的环境下仍然能够保持性能优势，体现了方案鲁棒性高的特点。Abstract: As an extension of the cloud computing paradigm, fog computing has attracted wide attention due to its advantages of low energy consumption, short time delay, and high bandwidth saving. Meanwhile, the fog computing-based computation offloading mechanism provides strong support for alleviating the pressure of data processing, realizing low delay service, and prolonging the network lifetime. To construct a green and long lifetime Internet of Things (IoT), this paper proposes a fairness and energy co-aware computation offloading scheme for fog-assisted IoT. Based on the joint optimization consideration of the fog node’s computing capacity, bandwidth resource, and offloading decision with energy consumption fairness, an optimization problem is first formulated to minimize the total energy consumption of all computation tasks. Second, a momentum gradient and coordinate collaboration descent-based fair energy minimization algorithm are proposed to solve the above mixed integer nonlinear programming problem. In this algorithm, based on the historical average energy consumption, distance, computing capacity, and residual energy of the fog node, a fair index is designed to obtain the offloading decision with the optimal energy consumption fairness. Minimization of the total energy consumption for processing all tasks can be achieved by jointly optimizing the occupation ratios of computing and bandwidth resources with the developed momentum gradient and coordinate collaboration descent method. Finally, simulation results show that the proposed scheme can achieve a faster convergence speed. Meanwhile, the total energy consumption of this scheme is the lowest compared to the random selection and greedy task offloading (GTO) schemes, the energy consumption fairness of the fog node is the highest, and the network lifetime is enhanced by 23.6% and 31.2% on average, respectively. Furthermore, this scheme can still maintain its performance advantage under different numbers of fog nodes and different task sizes, indicating the high robustness of the proposed scheme.
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图 2 动量梯度下降法和传统梯度下降法的总能耗对比
Figure 2. Comparison of the total energy consumption between the momentum gradient descent and gradient descent
图 3 Jain’s公平指数三种方案对比
Figure 3. Comparison of the Jain’s fairness index for the three different schemes
图 4 总能耗的三种方案对比
Figure 4. Comparison of the total energy consumption for the three different schemes
图 5 雾节点总历史平均能耗的三种方案对比
Figure 5. Comparison of the total historical average energy consumption for the three different schemes
图 6 总能耗关于平均距离以及平均任务大小的对比
Figure 6. Comparison of the total energy consumption versus the average distance and average task size
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