Large-scale simulation of a self-organizing self-management cloud computing framework

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 maximizatio...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of supercomputing 2018-02, Vol.74 (2), p.530-550
Main Authors: Filelis-Papadopoulos, Christos K., Giannoutakis, Konstantinos M., Gravvanis, George A., Tzovaras, Dimitrios
Format: Article
Language:English
Subjects:
Accelerators
Cloud computing
Compilers
Computer Science
Computer simulation
Computing time
Energy consumption
Energy efficiency
Energy management
Hardware
Interpreters
Mathematical models
Power efficiency
Processor Architectures
Programming Languages
Resource allocation
Resource management
Simulation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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:0920-8542
1573-0484
DOI:10.1007/s11227-017-2143-2