For the home and office, many life-log analysis applications for transferring data from cameras and sensors to the cloud and analyzing the data have been developed. However, because of limitation of the resources of the cloud and the network bandwidth between the sensor and the cloud, it is difficult to execute large load processing, such as video streaming analysis, in real time on the cloud. Moreover, taking into account the execution environment from the sensor to the cloud, it is necessary to set an appropriate degree of parallelism in the processing from the pre-processing, such as feature extraction, to the analysis on the information. In this paper, we propose a video streaming analysis application framework for load balancing between sensors and the cloud, and investigate the performance of the application in a cluster environment that simulates the sensor and the cloud. From the experiments, we show that the processing performance is improved by increasing the number of processing threads, and we demonstrate the effectiveness of load balancing between the sensors and the cloud.