Capacity planning for Web performance: metrics, models, and methods
Capacity planning for Web performance: metrics, models, and methods
Assessing the Robustness of Self-Managing Computer Systems under Highly Variable Workloads
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Short term performance forecasting in enterprise systems
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Time Series Analysis and Its Applications (Springer Texts in Statistics)
Time Series Analysis and Its Applications (Springer Texts in Statistics)
Workload Analysis and Demand Prediction of Enterprise Data Center Applications
IISWC '07 Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization
Analysis of Energy Efficiency in Clouds
COMPUTATIONWORLD '09 Proceedings of the 2009 Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns
Designing and evaluating an energy efficient Cloud
The Journal of Supercomputing
Efficient resource provisioning in compute clouds via VM multiplexing
Proceedings of the 7th international conference on Autonomic computing
Temporal Data Mining
Towards Self-Aware Performance and Resource Management in Modern Service-Oriented Systems
SCC '10 Proceedings of the 2010 IEEE International Conference on Services Computing
Pattern Matching Based Forecast of Non-periodic Repetitive Behavior for Cloud Clients
Journal of Grid Computing
Model-based self-adaptive resource allocation in virtualized environments
Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
Kieker: a framework for application performance monitoring and dynamic software analysis
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
An Approach to Forecasting QoS Attributes of Web Services Based on ARIMA and GARCH Models
ICWS '12 Proceedings of the 2012 IEEE 19th International Conference on Web Services
Performance queries for architecture-level performance models
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
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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 offers 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.