Towards Workload Balancing in Fog Computing Empowered IoT
Towards Workload Balancing in Fog Computing Empowered IoT
复制标题
DOI：
10.1109/tnse.2018.2852762
复制
发表时间：
2020-01-01
影响因子：
6.6
通讯作者：
Ansari, Nirwan
中科院分区：
文献类型：
--
作者：
Fan, Qiang;Ansari, Nirwan
由于延迟是物联网应用程序的关键性能指标，因此与蜂窝基站共同置于的FOG节点可以将计算资源移动到IoT设备附近。因此，可以将物联网设备的数据流以其接近而不是远程云的靠近雾节的载荷进行处理。但是，物联网设备中数据流的延迟既包括通信延迟和计算延迟。由于物联网设备分布的空间和时间动力学，有些BS和雾节略微加载，而其他BS和雾节可能会超负荷。因此，基站（BSS）之间的流量负载分配和雾节之间的计算负载分配分别影响数据流的通信延迟和计算潜伏期。为了解决此问题，我们在雾网络中提出了一个工作负载平衡方案，以通过将IoT设备与合适的BSS关联到通信和处理过程中数据流的延迟。我们进一步证明了拟议的工作负载平衡方案的收敛性和最佳性。通过广泛的模拟，我们将提议的负载平衡方案的性能与其他方案进行了比较，并验证了其在雾网络上的优势。
As latency is the key performance metric for IoT applications, fog nodes co-located with cellular base stations can move the computing resources close to IoT devices. Therefore, data flows of IoT devices can be offloaded to fog nodes in their proximity, instead of the remote cloud, for processing. However, the latency of data flows in IoT devices consist of both the communications latency and computing latency. Owing to the spatial and temporal dynamics of IoT device distributions, some BSs and fog nodes are lightly loaded, while others, which may be overloaded, may incur congestion. Thus, the traffic load allocation among base stations (BSs) and computing load allocation among fog nodes affect the communications latency and computing latency of data flows, respectively. To solve this problem, we propose a workload balancing scheme in a fog network to minimize the latency of data flows in the communications and processing procedures by associating IoT devices to suitable BSs. We further prove the convergence and the optimality of the proposed workload balancing scheme. Through extensive simulations, we have compared the performance of the proposed load balancing scheme with other schemes and verified its advantages for fog networking.