Starfish: A Self-Tuning System for Big Data Analytics by Herodotos Herodotou, Harold Lim, Gang Luo, Nedyalko Borisov, Liang Dong, Fatma Bilgen Cetin, and Shivnath Babu, of Duke University.
Abstract:
Timely and cost-effective analytics over “Big Data” is now a key ingredient for success in many businesses, scientific and engineering disciplines, and government endeavors. The Hadoop software stack—which consists of an extensible MapReduce execution engine, pluggable distributed storage engines, and a range of procedural to declarative interfaces—is a popular choice for big data analytics. Most practitioners of big data analytics—like computational scientists, systems researchers, and business analysts—lack the expertise to tune the system to get good performance. Unfortunately, Hadoop’s performance out of the box leaves much to be desired, leading to suboptimal use of resources, time, and money (in payas- you-go clouds). We introduce Starfish, a self-tuning system for big data analytics. Starfish builds on Hadoop while adapting to user needs and system workloads to provide good performance automatically, without any need for users to understand and manipulate the many tuning knobs in Hadoop. While Starfish’s system architecture is guided by work on self-tuning database systems, we discuss how new analysis practices over big data pose new challenges; leading us to different design choices in Starfish
Accepts that usability is, at least for this project, more important than peak performance. That is the goal is to open up use of Hadoop with reasonable performance to a large number of non-expert users. That will probably do as much if not more than the native performance of Hadoop to spread its use in a number of sectors.
Makes me wonder what acceptance of usability over precision would look like for topic maps? Suggestions?