Currently, data centers offer cloud computing platforms relying on virtualization technology and multi-tier architecture to handle an ever increasing scale and to provide elastic service. However, in order to achieve elasticity, efficient prediction is needed to control virtual machines. We present a new adaptive linear auto regressive technique for web server workload prediction with feedback loop control. We test the Adaptive-Feedback AR model with the Songkhla Rajabhat University’s academic web which has a similar daily pattern of workloads and the learning management system (LMS) web which has unpredictable workloads. For the 1-minute interval, the suitable result for controlling the AR orders is in the range of 2-8 and previous historical value is in range of 10-25. Our new prediction approach predicts both web workloads with a root mean square error (RMSE) below 0.6, of which quality is better, in terms of the prediction accuracy resulting in a better performance.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Adaptive Workload Prediction for Cloud-Based Server Infrastructures



    Erscheinungsdatum :

    2017-06-01


    Anmerkungen:

    Journal of Telecommunication, Electronic and Computer Engineering (JTEC); Vol 9, No 2-4: Circuit and System Advancement in Modern World III; 129-134 ; 2289-8131 ; 2180-1843


    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Web Server Workload Prediction: Fuzzy Markovian Approach

    Cheong, C. W. | British Library Online Contents | 2004


    Workload-based congestion prediction

    Sutton, Francis N. / Lindsay, Kenneth S. | IEEE | 2015


    Workload-based congestion prediction

    Lindsay, Kenneth S. | IEEE | 2015


    Pilot Workload Prediction

    Pepitone, David D. / Bortolussi, Michael R. / Shively, Robert J. | SAE Technical Papers | 1987


    Dynamic workload estimation - a step towards workload adaptive systems

    Schiessl,C. / Deutsche Forsch.Anst.f.Luft- und Raumfahrt,DLR,Braunschweig,DE | Kraftfahrwesen | 2008