Energy Efficient Scale-In Clusters with In-Storage Processing for Big-Data Analytics
I. Stephen Choi, Yang-Suk Kee · 2015
Big data drives a computing paradigm shift. Due to enormous data volumes, data-intensive programming frameworks are pervasive and scale-out clusters are widespread. As a result, data-movement energy dominates overall energy consumption and this will get worse with a technology scaling. We propose scale-in clusters with In-Storage Processing (ISP) devices that would enable energy efficient computing for big-data analytics. ISP devices eliminate/reduce data movements towards CPUs and execute tasks more energy-efficiently. Thus, with energy efficient computing near data and higher throughput enabled, clusters with ISP can achieve more than quadruple energy efficiency with fewer number of nodes as compared to the energy efficiency of similarly performing its counter-part scale-out clusters.