RE-STORM: REAL-TIME ENERGY EFFICIENT DATA ANALYSIS ADAPTING STORM PLATFORM

Abstract

It is necessary to model an energy efficient and stream optimization towards achieve high energy efficiency for Streaming data without degrading response time in big data stream computing. This paper proposes an Energy Efficient Traffic aware resource scheduling and Re-Streaming Stream Structure to replace a default scheduling strategy of storm is entitled as re-storm. The model described in three parts; First, a mathematical relation among energy consumption, low response time and high traffic streams. Second, various approaches provided for reducing an energy without affecting response time and which provides high performance in overall stream computing in big data. Third, re-storm deployed energy efficient traffic aware scheduling on the storm platform. It allocates worker nodes online by using hot-swapping technique with task utilizing by energy consolidation through graph partitioning. Moreover, re-storm is achieved high energy efficiency, low response time in all types of data arriving speeds.it is suitable for allocation of worker nodes in a storm topology. Experiment results have been demonstrated the comparing existing strategies which are dealing with energy issues without affecting or reducing response time for a different data stream speed levels. Finally, it shows that the re-storm platform achieved high energy efficiency and low response time when compared to all existing approaches.