The weighted majority algorithm
Information and Computation
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Designing Energy-Efficient Software
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Active Energy-Aware Management of Business-Process Based Applications
ServiceWave '08 Proceedings of the 1st European Conference on Towards a Service-Based Internet
Workload Analysis and Demand Prediction of Enterprise Data Center Applications
IISWC '07 Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization
Toward energy-efficient computing
Communications of the ACM
Dynamic Workload Prediction for Soft Real-Time Applications
CIT '10 Proceedings of the 2010 10th IEEE International Conference on Computer and Information Technology
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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%.