Δημοσιεύσεις

Project Acronym: ScaleSciComp
Title: Scale Scientific Computations
Affiliation: democritus university of thrace
Pi: George Gravvanis
Research Field: mathematics and computer sciences

Large-scale simulation of a self-organizing self-management cloud computing framework
by Filelis-Papadopoulos, Christos K. and Giannoutakis, Konstantinos M. and Gravvanis, George A. and Tzovaras, Dimitrios
Abstract:
A recently introduced cloud simulation framework is extended to support self-organizing and self-management local strategies in the cloud resource hierarchy. This dynamic hardware resource allocation system is evolving toward the goals defined by local strategies, which are determined as maximization of: energy efficiency of cloud infrastructures, task throughput, computational efficiency and resource management efficiency. Heterogeneous hardware resources are considered that are except from commodity CPU servers, hardware accelerators such as GPUs, MICs and FPGAs, thus forming a heterogeneous cloud infrastructure. Energy consumption and task execution models for the heterogeneous accelerators are also proposed, in order to demonstrate the energy efficiency of the proposed resource allocation system. Implementation details of the new functionalities on the parallel cloud simulation framework are discussed, while numerical results are given for the scalability and utilization of the cloud elements using the self-organization and self-management framework with two VM placement strategies.
Reference:
Large-scale simulation of a self-organizing self-management cloud computing framework (Filelis-Papadopoulos, Christos K. and Giannoutakis, Konstantinos M. and Gravvanis, George A. and Tzovaras, Dimitrios), In The Journal of Supercomputing, 2017.
Bibtex Entry:
@article{Filelis-Papadopoulos2017,
 author = {Filelis-Papadopoulos, Christos K.
		and Giannoutakis, Konstantinos M.
		and Gravvanis, George A.
		and Tzovaras, Dimitrios},
 title = {Large-scale simulation of a self-organizing self-management cloud computing framework},
 journal = {The Journal of Supercomputing},
 year = {2017},
 bibyear = {2017},
 month = {Sep},
 day = {13},
 abstract = {A recently introduced cloud simulation framework is extended to support self-organizing and self-management local strategies in the cloud resource hierarchy. This dynamic hardware resource allocation system is evolving toward the goals defined by local strategies, which are determined as maximization of: energy efficiency of cloud infrastructures, task throughput, computational efficiency and resource management efficiency. Heterogeneous hardware resources are considered that are except from commodity CPU servers, hardware accelerators such as GPUs, MICs and FPGAs, thus forming a heterogeneous cloud infrastructure. Energy consumption and task execution models for the heterogeneous accelerators are also proposed, in order to demonstrate the energy efficiency of the proposed resource allocation system. Implementation details of the new functionalities on the parallel cloud simulation framework are discussed, while numerical results are given for the scalability and utilization of the cloud elements using the self-organization and self-management framework with two VM placement strategies.},
 issn = {1573-0484},
 doi = {10.1007/s11227-017-2143-2},
 url = {https://doi.org/10.1007/s11227-017-2143-2},
}