Faculty of Computing and Telecommunications, Poznan University of Technology, 60-965 Poznań, Poland.
Sensors (Basel). 2023 Jan 15;23(2):997. doi: 10.3390/s23020997.
The well known cloud computing is being extended by the idea of fog with the computing nodes placed closer to end users to allow for task processing with tighter latency requirements. However, offloading of tasks (from end devices to either the cloud or to the fog nodes) should be designed taking energy consumption for both transmission and computation into account. The task allocation procedure can be challenging considering the high number of arriving tasks with various computational, communication and delay requirements, and the high number of computing nodes with various communication and computing capabilities. In this paper, we propose an optimal task allocation procedure, minimizing consumed energy for a set of users connected wirelessly to a network composed of FN located at AP and CN. We optimize the assignment of AP and computing nodes to offloaded tasks as well as the operating frequencies of FN. The considered problem is formulated as a Mixed-Integer Nonlinear Programming problem. The utilized energy consumption and delay models as well as their parameters, related to both the computation and communication costs, reflect the characteristics of real devices. The obtained results show that it is profitable to split the processing of tasks between multiple FNs and the cloud, often choosing different nodes for transmission and computation. The proposed algorithm manages to find the optimal allocations and outperforms all the considered alternative allocation strategies resulting in the lowest energy consumption and task rejection rate. Moreover, a heuristic algorithm that decouples the optimization of wireless transmission from implemented computations and wired transmission is proposed. It finds the optimal or close-to-optimal solutions for all of the studied scenarios.
云计算的概念正在通过雾计算得到扩展，雾计算将计算节点放置在更接近最终用户的位置，以允许在具有更严格延迟要求的情况下处理任务。然而，任务卸载（从终端设备到云或雾节点）的设计应该考虑到传输和计算的能量消耗。考虑到具有各种计算、通信和延迟要求的大量到达任务，以及具有各种通信和计算能力的大量计算节点，任务分配过程可能具有挑战性。在本文中，我们提出了一种最优的任务分配过程，该过程最小化了一组用户通过无线连接到由位于 AP 和 CN 的 FN 组成的网络所消耗的能量。我们优化了 AP 和计算节点到卸载任务的分配以及 FN 的工作频率。所考虑的问题被表述为一个混合整数非线性规划问题。所使用的能量消耗和延迟模型及其参数，与计算和通信成本有关，反映了实际设备的特性。获得的结果表明，将任务的处理分配给多个 FN 和云是有利的，通常为传输和计算选择不同的节点。所提出的算法能够找到最优的分配方案，并优于所有考虑的替代分配策略，从而实现最低的能量消耗和任务拒绝率。此外，还提出了一种将无线传输的优化与实施的计算和有线传输解耦的启发式算法。它为所有研究的场景找到了最优或接近最优的解决方案。
