Meta-learning based architectural and algorithmic optimization for achieving green-ness in predictive workload analytics
Nidhi Singh, Shrisha Rao · 2013
Predictive workload analytics for server systems has been the focus of recent research in energy-aware computing, with many algorithmic and architectural techniques proposed to analyze, predict, and optimize server workloads in order to build energy-aware IT systems. These techniques, though effective in optimizing server workloads, often ignore the green-ness aspect 'in' the technique itself and incur heavy computational costs in their operations. In this paper, we propose a meta-learning based architecture for building server workload prediction mechanism using which the computational cost of holistic predictive workload analytics can be optimized, and green-ness 'in' the analytics can be achieved. We also present an algorithmic optimization of the proposed meta-learning architecture for handling concept drift in server workloads, thereby also achieving improved greenness 'by' the analytics. Our experiments show that the proposed meta-learning based architecture substantially reduces the total computational cost of workload prediction mechanism, with a minor decrease of 0.5--1.3% in the accuracy, and the proposed algorithmic optimization significantly improves accuracy of the workload prediction mechanism in concept drift scenarios by 8.1%.