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ElCore: Dynamic elastic resource management and discovery for future large-scale manycore enabled distributed systems

Zarrin, J., Aguiar, R. L. and Barraca, J. P. (2016). ElCore: Dynamic elastic resource management and discovery for future large-scale manycore enabled distributed systems. Microprocessors and Microsystems, 46(B), pp. 221-239. doi: 10.1016/j.micpro.2016.06.007


Large-scale computing environments (such as HPC Clusters, Grids and Clouds) provide a vast number of heterogeneous resources (such as computing, storage, data and network resources) for the users/machines with various types of accessibility (in terms of resource, data, service and application). Resource management is one of the most significant underlying challenges for efficient resource sharing and utilization in such computing environments. Designing a resource management model which can be applied and adjusted to the requirements of these different future complex computing environments is an extra challenge. This paper will address the problem of resource management for the future large-scale many-core enabled computing environments by focusing on resource allocation issues. It provides a fully decentralized generic resource management architecture which can be applied to such distributed environments. Simulation results prove that our resource management scheme is highly scalable and provides a high level of accuracy for resource allocation which has a significant impact on the overall system performance.

Publication Type: Article
Additional Information: © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: Resource allocation; Many-core; Many-Chip; HPC; Cluster; Grid; Cloud computing; Scheduling; Resource discovery; Resource utilization
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
Date available in CRO: 13 Oct 2017 07:35
Date deposited: 13 October 2017
Date of acceptance: 17 June 2016
Date of first online publication: 22 June 2016
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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