在物联网环境中,大量的传感器产生了海量的数据.这些数据通常都需要写入到数据库中来实现数据的分析与应用.当这些物联网海量传感器数据插入到数据库中的时候,会在存储系统中产生严重的小数据同步写性能瓶颈.针对此问题,本文设计了一种高性能数据库磁盘缓冲队列DCQD(Disk Cache Queue for Database).DCQD在保证物联网采集数据同步写入磁盘,确保不丢失数据的基础上,可以显著优化海量数据插入到数据库中的性能.实验表明,DCQD在物联网应用环境中,可以显著地提高数据采集系统的性能.
The parallel computation capabilities of modern graphics processing units （GPUs） have attracted increasing attention from researchers and engineers who have been conducting high computational throughput studies. However, current single GPU based engineering solutions are often struggling to fulfill their real-time requirements. Thus, the multi-GPU-based approach has become a popular and cost-effective choice for tackling the demands. In those cases, the computational load balancing over multiple GPU ＂nodes＂ is often the key and bottleneck that affect the quality and performance of the real=time system. The existing load balancing approaches are mainly based on the assumption that all GPU nodes in the same computer framework are of equal computational performance, which is often not the case due to cluster design and other legacy issues. This paper presents a novel dynamic load balancing （DLB） model for rapid data division and allocation on heterogeneous GPU nodes based on an innovative fuzzy neural network （FNN）. In this research, a 5-state parameter feedback mechanism defining the overall cluster and node performance is proposed. The corresponding FNN-based DLB model will be capable of monitoring and predicting individual node performance under different workload scenarios. A real=time adaptive scheduler has been devised to reorganize the data inputs to each node when necessary to maintain their runtime computational performance. The devised model has been implemented on two dimensional （2D） discrete wavelet transform （DWT） applications for evaluation. Experiment results show that this DLB model enables a high computational throughput while ensuring real=time and precision requirements from complex computational tasks.
