Designing efficient control mechanisms to meet strict performance requirements with respect tochanging workload demands without sacrificing resource efficiency remains a challenge in cloudinfrastructures. A popular approach is fine-grained resource provisioning via auto-scaling mechanisms that rely on either threshold-based adaptation rules or sophisticated queuing/control-theoretic models. While it is difficult at design time to specify optimal threshold rules, it is even more challenging inferring precise performance models for the multitude of services. Recently, reinforcement learning have been applied to address this challenge. However, such approaches require many learning trials to stabilize at the beginning and when operational conditions vary thereby limiting their application under dynamic workloads. To this end, we extend the standard reinforcement learning approach in two ways: a) we formulate the system state as a fuzzy space and b) exploit a set of cooperative agents to explore multiple fuzzy states in parallel to speed up learning. Through multiple experiments on a real virtualized testbed, we demonstrate that our approach converges quickly, meets performance targets at high efficiency without explicit service models. ; Cloud Control


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Adaptive Service Performance Control using Cooperative Fuzzy Reinforcement Learning in Virtualized Environments


    Beteiligte:

    Erscheinungsdatum :

    2017-01-01



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




    Adaptive fuzzy control of satellite attitude by reinforcement learning

    Buijtenen, W.M. van / Schram, G. / Babuska, R. et al. | Tema Archiv | 1998


    Decentralized cooperative driving automation: a reinforcement learning framework using genetic fuzzy systems

    Sathyan, Anoop / Ma, Jiaqi / Cohen, Kelly | Taylor & Francis Verlag | 2021


    An Integrated Cloud Service of Application Delivery in Virtualized Environments

    Shuen-Tai Wang / Yu-Ching Lin / Hsi-Ya Chang | BASE | 2016

    Freier Zugriff

    Adaptive fuzzy control for rendezvous and docking by reinforcement learning

    Mulder, J. / Bennis, R. / Chu, Q. et al. | AIAA | 2001