Arcas Gabriel Ioan, Cioara Tudor, Anghel Ionut
Bosch Engineering Center, 400158 Cluj-Napoca, Romania.
Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania.
Biomimetics (Basel). 2024 May 18;9(5):302. doi: 10.3390/biomimetics9050302.
As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services' computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time.
随着物联网计量设备越来越普遍，智能能源电网面临着与大量数据传输相关的挑战，这些挑战影响着控制服务的延迟和能源的安全输送。将计算工作卸载到边缘是一种可行的选择；然而，由于搜索空间巨大，在有限的时间内难以确定最优决策，因此有效地协调边缘节点上的服务执行面临重大挑战。在本研究论文中，我们利用鲸鱼优化算法来决定和选择执行服务计算任务的最优边缘节点。我们采用有向无环图来对计算节点、数据网络链路、智能电网能源资产和能源网络组织之间的依赖关系进行建模，从而在决策空间内实现更高效的导航以确定最优解。卸载决策变量表示为一个二进制向量，使用考虑往返时间和边缘任务计算资源之间相关性的适应度函数进行评估。为了有效地探索卸载策略并防止收敛到次优解，我们调整了反馈机制、惯性权重系数和非线性收敛因子。评估结果很有前景，表明所提出的解决方案能够有效地兼顾能源和数据网络约束，同时实现更快的优化决策，响应时间有显著改善，每次迭代的平均执行时间约为0.03秒。此外，在所建模的复杂计算基础设施上，我们的解决方案在多样性、适应度进化和执行时间方面表现出强大的特性。
