Cloud Computing. First International Conference, CloudComp 2009 Munich, Germany, October 19–21, 2009 Revised Selected Papers

Research Article

Dynamic Load Management of Virtual Machines in Cloud Architectures

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  • @INPROCEEDINGS{10.1007/978-3-642-12636-9_14,
        author={Mauro Andreolini and Sara Casolari and Michele Colajanni and Michele Messori},
        title={Dynamic Load Management of Virtual Machines in Cloud Architectures},
        proceedings={Cloud Computing. First International Conference, CloudComp 2009 Munich, Germany, October 19--21, 2009 Revised Selected Papers},
        proceedings_a={CLOUDCOMP},
        year={2012},
        month={5},
        keywords={},
        doi={10.1007/978-3-642-12636-9_14}
    }
    
  • Mauro Andreolini
    Sara Casolari
    Michele Colajanni
    Michele Messori
    Year: 2012
    Dynamic Load Management of Virtual Machines in Cloud Architectures
    CLOUDCOMP
    Springer
    DOI: 10.1007/978-3-642-12636-9_14
Mauro Andreolini1,*, Sara Casolari1,*, Michele Colajanni1,*, Michele Messori1,*
  • 1: University of Modena and Reggio Emilia
*Contact email: mauro.andreolini@unimore.it, sara.casolari@unimore.it, michele.colajanni@unimore.it, michele.messori@unimore.it

Abstract

Cloud infrastructures must accommodate changing demands for different types of processing with heterogeneous workloads and time constraints. In a similar context, dynamic management of virtualized application environments is becoming very important to exploit computing resources, especially with recent virtualization capabilities that allow live sessions to be moved transparently between servers. This paper proposes novel management algorithms to decide about reallocations of virtual machines in a cloud context characterized by large numbers of hosts. The novel algorithms identify just the real critical instances and take decisions without recurring to typical thresholds. Moreover, they consider load trend behavior of the resources instead of instantaneous or average measures. Experimental results show that proposed algorithms are truly selective and robust even in variable contexts, thus reducing system instability and limit migrations when really necessary.