Dynamic resource allocation for shared data centers using online measurements
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Autonomic Virtualized Environments
ICAS '06 Proceedings of the International Conference on Autonomic and Autonomous Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Workload Analysis and Demand Prediction of Enterprise Data Center Applications
IISWC '07 Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization
Batch Job Profiling and Adaptive Profile Enforcement for Virtualized Environments
PDP '09 Proceedings of the 2009 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing
SLA-Driven Adaptive Resource Management for Web Applications on a Heterogeneous Compute Cloud
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
NCA '10 Proceedings of the 2010 Ninth IEEE International Symposium on Network Computing and Applications
Hierarchical Forecasting of Web Server Workload Using Sequential Monte Carlo Training
IEEE Transactions on Signal Processing
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Companies are currently turning to the use of web applications offered as Cloud services, expecting a certain QoS expressed by means of a maximum response time. Virtual Machines hosting these applications may vary their resource allotment as a consequence of a variation in the incoming workload intensity to guarantee the agreed response time. This allotment should be enough to avoid an under-provision that would lead to the violation of response time constraints, and low enough to avoid an over-provision that would lead to resource wasting. To anticipate the resource demands of web applications, we propose a Prediction System that combines statistical and Machine Learning techniques. This system is composed by the Immediate Predictor to anticipate the immediate CPU demand, useful to adapt pro-actively the resource allotments, and by the Capacity Predictor to forecast the CPU demand at a more distant future. The last prediction might be used to make an informed admission control by means of rejecting new applications that will not be able to fulfill their SLAs. Experiments show the accuracy achieved by the Prediction System and discuss its potential benefit to enhance the resource management process in a Cloud provider.