%0 Journal Article %1 HuKoAm2013-CCPE-WorkloadClassificationAndForecasting %A Herbst, Nikolas Roman %A Huber, Nikolaus %A Kounev, Samuel %A Amrehn, Erich %D 2014 %J Concurrency and Computation - Practice and Experience, John Wiley and Sons, Ltd. %K Application_quality_of_service_management Automated_model_learning Elasticity Instrumentation_profiling_and_workload_characterization Online_monitoring_and_forecasting Performance Prediction Resource_management Self-adaptive-systems Self-aware-computing Statistical_estimation_and_machine_learning Tool WCF descartes t_journalmagazine myown %N 12 %P 2053--2078 %T Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning %U http://dx.doi.org/10.1002/cpe.3224 %V 26 %X As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining in importance as a foundation for online capacity planning and resource management. Time series analysis covers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting Quality-of-Service (QoS) metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared to statically applied forecasting methods, e.g. in an exemplary scenario on average by 37%. In a case study, between 55% and 75% of the violations of a given service level agreement can be prevented by applying proactive resource provisioning based on the forecast results of our implementation.

@article{HuKoAm2013-CCPE-WorkloadClassificationAndForecasting, abstract = {As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining in importance as a foundation for online capacity planning and resource management. Time series analysis covers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting Quality-of-Service (QoS) metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared to statically applied forecasting methods, e.g. in an exemplary scenario on average by 37%. In a case study, between 55% and 75% of the violations of a given service level agreement can be prevented by applying proactive resource provisioning based on the forecast results of our implementation.}, added-at = {2020-04-06T11:20:28.000+0200}, author = {Herbst, Nikolas Roman and Huber, Nikolaus and Kounev, Samuel and Amrehn, Erich}, biburl = {https://www.bibsonomy.org/bibtex/2035f94d6f4acd0900ba003a5cdc7e7cb/samuel.kounev}, interhash = {2ca0ad7dc7f74e444cb7c0df06f335ed}, intrahash = {035f94d6f4acd0900ba003a5cdc7e7cb}, journal = {Concurrency and Computation - Practice and Experience, John Wiley and Sons, Ltd.}, keywords = {Application_quality_of_service_management Automated_model_learning Elasticity Instrumentation_profiling_and_workload_characterization Online_monitoring_and_forecasting Performance Prediction Resource_management Self-adaptive-systems Self-aware-computing Statistical_estimation_and_machine_learning Tool WCF descartes t_journalmagazine myown}, month = {March}, number = 12, pages = {2053--2078}, timestamp = {2020-10-21T04:00:42.000+0200}, title = {{Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning}}, url = {http://dx.doi.org/10.1002/cpe.3224}, volume = 26, year = 2014 }