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Krzywda, Jakub [Verfasser:in]; Rezaie, Ali [Verfasser:in]; Papazachos, Zafeirios [Verfasser:in]; Hamilton-Bryce, Ryan [Verfasser:in]; Östberg, Per-Olov [Verfasser:in]; Ali-Eldin, Ahmed [Verfasser:in]; McCollum, Barry [Verfasser:in]; Domaschka, Jörg [Verfasser:in]

Extended optimization model: project deliverable D3.3

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  • Medientyp: Bericht; E-Book
  • Titel: Extended optimization model: project deliverable D3.3
  • Beteiligte: Krzywda, Jakub [Verfasser:in]; Rezaie, Ali [Verfasser:in]; Papazachos, Zafeirios [Verfasser:in]; Hamilton-Bryce, Ryan [Verfasser:in]; Östberg, Per-Olov [Verfasser:in]; Ali-Eldin, Ahmed [Verfasser:in]; McCollum, Barry [Verfasser:in]; Domaschka, Jörg [Verfasser:in]
  • Erschienen: Universität Ulm, 2017-04-20T13:13:47Z
  • Sprache: Englisch
  • DOI: https://doi.org/10.18725/OPARU-4307
  • Schlagwörter: DDC 004 / Data processing & computer science ; Cloud computing ; Electric network topology ; Datenmanagement ; Data management ; Cloud ; Simulation ; Cactos Projekt ; Application model ; Context-aware cloud topology ; Optimisation ; Analytics
  • Entstehung:
  • Anmerkungen: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Beschreibung: This deliverable describes an enhanced version of the optimization model that features predictive capabilities. The purpose of this deliverable is to demonstrate how the enhanced model and advanced optimization algorithms support the optimization of a data center configuration. Predictive optimization capabilities of CactoOpt mainly support three optimization activities that can be performed on the logical (software) level of data center management: initial placement of virtual machines, migration of virtual machines, and vertical scaling. To deliver against these capabilities two software components were implemented: Workload Analysis and Classification Tool (WAC) and Application Behaviour Predictor. WAC is a tool that enables a cloud provider to deploy multiple auto-scaling algorithms suitable for different workload types. The tool assigns a workload to an auto-scaler based on the type of the workload, i.e., some auto-scalers can be better for bursty workloads while other auto-scalers can be better for workloads with strong patterns. The application behavior predictor is a tool that utilizes the knowledge about how the workload and the dynamics of the applications changes over time to predict the future state of the application for optimization purposes, e.g., how long will a task run before terminating on a given hardware configuration.
  • Zugangsstatus: Freier Zugang
  • Rechte-/Nutzungshinweise: Namensnennung - Keine Bearbeitung (CC BY-ND)