The optimization of large-scale data sets depends on the technologies and methods used. The MapReduce model, implemented on Apache Hadoop or Spark, allows splitting large data sets into a set of blocks distributed on several machines. Data compression reduces data size and transfer time between disks and memory but requires additional processing. Therefore, finding an optimal trade-off is a challenge, as a high compression factor may underload Input/Output but overload the processor. The project aims to present a system enabling the selection of the compression tools and tuning the compression factor to reach the best performance in Apache Hadoop and Spark infrastructures based on simulation analyses
In this project, a system enabling to find an optimal trade-off to reach optimal performance in Apache Hadoop and Spark frameworks will be developed. The method will be evaluated for diverse applications, including TestDFSIO, TeraSort, WordCount, LogAnalyzer, and K-means. It is planned to study the energy-efficient data transfers of Apache Hadoop and Spark using RDMA-capable networks like InfiniBand based on the developed methodology and techniques.