CSR: Small: Collaborative Research: Comprehensive Algorithmic Resilience (CAR) for Big Data Analytics

Louisiana State University, Baton Rouge LA

Investigators

Abstract

Big Data analytics is the process of mining useful knowledge in very large data sets, critical to the advancement of many research and application fields. With manufacturing technology downscaling of processor size coupled with increasing power densities, modern computer systems suffer from potential hardware and software failures, which can manifest themselves as errors. The errors happen when long-running Big Data analytics are executed on the systems, causing system crashes or worse, returning undetected incorrect results. While numerous hardware-based resilience methods exist, they often come at the cost of excessive power efficiency reduction and substantial design complexity enlargement among others. The project aims to reinforce popular Big Data analytics by embracing a host of comprehensive algorithmic resilience (CAR) software techniques that include concurrent error detection, coordinated checkpointing, and execution recovery, for high execution resilience. Upon detecting potential hardware and software errors concurrently during analytics, CAR enables execution recovery from detected errors without lofty overhead common to hardware-based resilience methods. Research activities of the project aim to achieve six main objectives that focus on addressing several technical challenges to realize CAR, based on investigators' encouraging preliminary results and prior work. The success of this project can benefit wide scientific and industrial applications due to its better support of Big Data analytics and processing. Research advances from this research are to be incorporated into undergraduate and graduate education, to be disseminated and shared broadly through technical presentations and by a website, and to inspire high school students for their STEM interest.