Host load prediction using linear models
Cluster Computing
Predicting Behavior Patterns Using Adaptive Workload Models
MASCOTS '99 Proceedings of the 7th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
The Fastest Fourier Transform in the West
The Fastest Fourier Transform in the West
Time series data mining: identifying temporal patterns for characterization and prediction of time series events
Modeling Multiple Time Series for Anomaly Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Load prediction models in web-based systems
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
Workload Analysis and Demand Prediction of Enterprise Data Center Applications
IISWC '07 Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization
Q-clouds: managing performance interference effects for QoS-aware clouds
Proceedings of the 5th European conference on Computer systems
An empirical comparison of methods to support QoS-aware service selection
Proceedings of the 2nd International Workshop on Principles of Engineering Service-Oriented Systems
Improving the scalability of data center networks with traffic-aware virtual machine placement
INFOCOM'10 Proceedings of the 29th conference on Information communications
An Analysis of Traces from a Production MapReduce Cluster
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Decomposition Based Algorithm for State Prediction in Large Scale Distributed Systems
ISPDC '10 Proceedings of the 2010 Ninth International Symposium on Parallel and Distributed Computing
Predicting and optimizing system utilization and performance via statistical machine learning
Predicting and optimizing system utilization and performance via statistical machine learning
Integrated estimation and tracking of performance model parameters with autoregressive trends
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Exploiting Resource Usage Patterns for Better Utilization Prediction
ICDCSW '11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems Workshops
Projecting disk usage based on historical trends in a cloud environment
Proceedings of the 3rd workshop on Scientific Cloud Computing Date
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
Resource utilization prediction: a proposal for information technology research
Proceedings of the 1st Annual conference on Research in information technology
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Predicting future behavior reliably and efficiently is vital for systems that manage virtual services. Such systems must be able to balance loads within a cloud environment to ensure that service level agreements (SLAs) are met at a reasonable expense. These virtual services while often comparatively idle are occasionally heavily utilized. Standard approaches to modeling system behavior (by analyzing the totality of the observed data, such as regression based approaches) tend to predict average rather than exceptional system behavior and may ignore important patterns of change over time. Consequently, such approaches are of limited use in providing warnings of future peak utilization within a cloud environment. Skewing predictions to better fit peak utilizations, results in poor fitting to low utilizations, which compromises the ability to accurately predict peak utilizations, due to false positives. In this paper, we present an adaptive approach that estimates, at run time, the best prediction value based on the performance of the previously seen predictions. This algorithm has wide applicability. We applied this adaptive technique to two large-scale real world case studies. In both studies, the results show that the adaptive approach is able to predict low, medium, and high utilizations more accurately than the other proposed approaches, at low cost, by adapting to changing patterns within the input time series. This facilitates better proactive management and placement of systems running within a cloud.