Performance improvement of Data analysis of IoT applications using re-storm in big data stream computing platform

摘要

Big Data and Internet of Things (IoT) are two popular technical terms in current IT industry. The analysis of IoT data consumes more energy since it is huge in size. This paper proposes a methodology re-storm that addresses energy issues and response time of IoT applications data. It uses big data stream computing for re-storm against existing method storm. The storm failed to address dynamic scheduling but re-storm deals with energy-efficient traffic aware resource scheduling. This paper presents a model that different traffic arriving rate of streams re-storm at multiple traffic levels for high energy efficiency, low response time. It deals at three levels, firstly, a mathematical model for high energy efficiency, low response time. Secondly, allocation of resources bearing in mind DVFS (Dynamic Voltage and Frequency Scaling) methods and existing effective optimal consolidation methods. Thirdly, online task allocation using hot swapping technique, streaming graph optimizing. Finally, the experimental results show that re-storm has been improved the performance 30-40% against storm for real time data of IoT applications.