Predicting resource demand profiles by periodicity mining

Summary form only given. Scientific computing clusters, enterprise data centers and grid and utility environments utilize the majority of the world´s computing resources. Most of these resources are lightly utilized and offer a vast potential for resource sharing, an economically attractive and increasingly indispensable management option. A prerequisite for automating resource consolidation is modeling and prediction of demand characteristics. We present an approach for long-term demand characteristics prediction based on mining periodicities in historical demand data. In addition to characterizing the regularity of the past demand behavior (and so providing a measure of predictability) we propose a method for predicting probabilistic profiles which describe likely future behavior. The presented algorithms are change-adaptive in the sense that they automatically adjust to new regularities in demand patterns. A case study using data from an enterprise data center evaluates the effectiveness of the technique.