With the emergence of big data era, most of the current performance optimization strategies are mainly used in a distributed computing framework with disks as the underlying storage. They may solve the problems in traditional disk-based distribution, but they are hard to transplant and are not well suitable for performance optimization especially for an in-memory computing framework on account of different underlying storage and computation architecture. In this paper, we first give the definition of the resource allocation model, parallelism degree model, and allocation fitness model on the basis of the theoretical analysis of Spark architecture. Second, based on the model presented, we propose a strategy embedded in the evaluation model which is easy to perform. The optimization strategy selects the worker with a lower load that satisfies requirements to assign the latter tasks, and the worker with a higher load may not be assigned tasks. The experiments consisting of four variance jobs are conducted to verify the effectiveness of the presented strategy.

Landslide geological hazard may cause great injuries and lost due to its crypticity and destroy. Advanced internet of things and wireless broadband communication techniques were adopted to realize remote wireless geological hazard monitoring and warning system and replace traditional manual work based monitoring method. Frames of hardware and software of the system were presented. Demonstration system was implemented at trouble spots in Wuxi. The results show that the monitoring system could collect real-time data of the monitored spots remotely, which provided necessary information for the geological hazard evaluation and warning.