Green and Energy Efficient Big Data Processing at the Software Level

With the explosive growth of big data applications, energy efficiency is at the forefront of evaluating the performance of a data center, to deliver green solutions in analyzing both structured information and unstructured big data. Focusing on handling the critical design constraints at the software level in a distributed system composed of huge numbers of power-hungry components, in this proposal, an optimized program design approach is introduced in order to achieve the best possible power performance in big data processing. Methodologies to model and evaluate large scale big data computer architectures with multi-core and GPU are introduced. The model allows obtaining design characteristic values at the early design stage, thus benefits programmers by providing the necessary environmental information for choosing the best power-efficient alternative. The energy efficiency improvements from the new designed approach have been validated by real measurements on a multiprocessing system.

Learning Objectives

Introducing energy efficient software design methodologies for big data processing, power performance metrics and measurements
Modeling and evaluating large scale computer architectures with multi-core and GPU
Global optimization for choosing the best power-efficient alternative based on data characteristics and quantitative performance analysis
Validation of the energy efficiency improvements from the new designed approach