On building next generation data centers: energy flow in the information technology stack
COMPUTE '08 Proceedings of the 1st Bangalore Annual Compute Conference
Sustainable operation and management of data center chillers using temporal data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-resolution techniques for visual exploration of large time-series data
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
Exploiting Generalized Additive Models for Diagnosing Abnormal Energy Use in Buildings
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
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Time series prediction methods are used on a daily basis by analysts for making important decisions. Most of these methods use some variant of moving averages to reduce the number of data points before prediction. However, to reach a good prediction in certain applications (e.g., power consumption time series in data centers) it is important to preserve peaks and their patterns. In this paper, we introduce automated peak-preserving smoothing and prediction algorithms, enabling a reliable long term prediction for seasonal data, and combine them with an advanced visual interface: (1) using high resolution cell-based time series to explore seasonal patterns, (2) adding new visual interaction techniques (multi-scaling, slider, and brushing & linking) to incorporate human expert knowledge, and (3) providing both new visual accuracy color indicators for validating the predicted results and certainty bands communicating the uncertainty of the prediction. We have integrated these techniques into a wellfitted solution to support the prediction process, and applied and evaluated the approach to predict both power consumption and server utilization in data centers with 70-80% accuracy.