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Green Web Services: Improving Energy Efficiency in Data Centers via Workload Predictions

Green Web Services: Improving Energy Efficiency in Data Centers via Workload Predictions . Massimiliano Menarini, Filippo Seracini , Xiang Zhang, Tajana Rosing , Ingolf Krüger GREENS 2013. Our Message: A Top-down Approach.

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Green Web Services: Improving Energy Efficiency in Data Centers via Workload Predictions

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  1. Green Web Services: Improving Energy Efficiency in Data Centers via Workload Predictions Massimiliano Menarini, Filippo Seracini, Xiang Zhang, TajanaRosing, Ingolf Krüger GREENS 2013

  2. Our Message:A Top-down Approach • The application layer contains fundamental information on the execution of a workflow • There are useful correlations between service calls that we can leverage to optimize the overall behavior of the system • Leverage that information to predict future levels of workload and proactively allocate resources

  3. S.O.PR.A Methodology

  4. Faced Issues • Accuracy of the workload predictions • A small standard deviation of the time dependency is key to save more energy

  5. Open Questions for Further Discussion • Cross-layer monitoring, modeling and prediction • How the different layers (application, middleware, OS, VM, PM) affect resource usage? • How can we model those layers and their interactions so to take into account also resource contention? • How can we measure, and what to measure, at each layer without affecting performance?

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