By the same authors

Market-inspired Dynamic Resource Allocation in Many-core High Performance Computing Systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Market-inspired Dynamic Resource Allocation in Many-core High Performance Computing Systems. / Singh, Amit Kumar; Dziurzanski, Piotr; Soares Indrusiak, Leandro.

Proceedings of International Conference on High Performance Computing & Simulation. IEEE, 2015. p. 413-420.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Singh, AK, Dziurzanski, P & Soares Indrusiak, L 2015, Market-inspired Dynamic Resource Allocation in Many-core High Performance Computing Systems. in Proceedings of International Conference on High Performance Computing & Simulation. IEEE, pp. 413-420, 2015 International Conference on High Performance Computing & Simulation (HPCS 2015), Amsterdam, Netherlands, 20/07/15.

APA

Singh, A. K., Dziurzanski, P., & Soares Indrusiak, L. (2015). Market-inspired Dynamic Resource Allocation in Many-core High Performance Computing Systems. In Proceedings of International Conference on High Performance Computing & Simulation (pp. 413-420). IEEE.

Vancouver

Singh AK, Dziurzanski P, Soares Indrusiak L. Market-inspired Dynamic Resource Allocation in Many-core High Performance Computing Systems. In Proceedings of International Conference on High Performance Computing & Simulation. IEEE. 2015. p. 413-420

Author

Singh, Amit Kumar ; Dziurzanski, Piotr ; Soares Indrusiak, Leandro. / Market-inspired Dynamic Resource Allocation in Many-core High Performance Computing Systems. Proceedings of International Conference on High Performance Computing & Simulation. IEEE, 2015. pp. 413-420

Bibtex - Download

@inproceedings{c39fed05ccc14a7abaa8e3bf179043cb,
title = "Market-inspired Dynamic Resource Allocation in Many-core High Performance Computing Systems",
abstract = "Many-core systems are envisioned to fulfill the increased performance demands in several computing domains such as embedded and high performance computing (HPC). The HPC systems are often overloaded to execute a number of dynamically arriving jobs. In overload situations, market-inspired resource allocation heuristics have been found to provide better results in terms of overall profit (value) earned by completing the execution of a number of jobs when compared to various other heuristics. However, the conventional market-inspired heuristics lack the concept of holding low value executing jobs to free the occupied resources to be used by high value arrived jobs in order to maximize the overall profit. In this paper, we propose a market-inspired heuristic that accomplish the aforementioned concept and utilizes design-time profiling results of jobs to facilitate efficient allocation. Additionally, the remaining executions of the held jobs are performed on freed resources at later stages to make some profit out of them. The holding process identifies the appropriate jobs to be put on hold to free the resources and ensures that the loss incurred due to holding is lower than the profit achieved by high value arrived jobs by using the free resources. Experiments show that the proposed approach achieves 8{\%} higher savings when compared to existing approaches, which can be a significant amount when dealing in the order of millions of dollars.",
keywords = "Many-core, High Performance Computing, RESOURCE ALLOCATION, Profit, Value curves",
author = "Singh, {Amit Kumar} and Piotr Dziurzanski and {Soares Indrusiak}, Leandro",
note = "{\circledC} 2015, IEEE. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.",
year = "2015",
language = "English",
pages = "413--420",
booktitle = "Proceedings of International Conference on High Performance Computing & Simulation",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Market-inspired Dynamic Resource Allocation in Many-core High Performance Computing Systems

AU - Singh, Amit Kumar

AU - Dziurzanski, Piotr

AU - Soares Indrusiak, Leandro

N1 - © 2015, IEEE. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

PY - 2015

Y1 - 2015

N2 - Many-core systems are envisioned to fulfill the increased performance demands in several computing domains such as embedded and high performance computing (HPC). The HPC systems are often overloaded to execute a number of dynamically arriving jobs. In overload situations, market-inspired resource allocation heuristics have been found to provide better results in terms of overall profit (value) earned by completing the execution of a number of jobs when compared to various other heuristics. However, the conventional market-inspired heuristics lack the concept of holding low value executing jobs to free the occupied resources to be used by high value arrived jobs in order to maximize the overall profit. In this paper, we propose a market-inspired heuristic that accomplish the aforementioned concept and utilizes design-time profiling results of jobs to facilitate efficient allocation. Additionally, the remaining executions of the held jobs are performed on freed resources at later stages to make some profit out of them. The holding process identifies the appropriate jobs to be put on hold to free the resources and ensures that the loss incurred due to holding is lower than the profit achieved by high value arrived jobs by using the free resources. Experiments show that the proposed approach achieves 8% higher savings when compared to existing approaches, which can be a significant amount when dealing in the order of millions of dollars.

AB - Many-core systems are envisioned to fulfill the increased performance demands in several computing domains such as embedded and high performance computing (HPC). The HPC systems are often overloaded to execute a number of dynamically arriving jobs. In overload situations, market-inspired resource allocation heuristics have been found to provide better results in terms of overall profit (value) earned by completing the execution of a number of jobs when compared to various other heuristics. However, the conventional market-inspired heuristics lack the concept of holding low value executing jobs to free the occupied resources to be used by high value arrived jobs in order to maximize the overall profit. In this paper, we propose a market-inspired heuristic that accomplish the aforementioned concept and utilizes design-time profiling results of jobs to facilitate efficient allocation. Additionally, the remaining executions of the held jobs are performed on freed resources at later stages to make some profit out of them. The holding process identifies the appropriate jobs to be put on hold to free the resources and ensures that the loss incurred due to holding is lower than the profit achieved by high value arrived jobs by using the free resources. Experiments show that the proposed approach achieves 8% higher savings when compared to existing approaches, which can be a significant amount when dealing in the order of millions of dollars.

KW - Many-core

KW - High Performance Computing

KW - RESOURCE ALLOCATION

KW - Profit

KW - Value curves

M3 - Conference contribution

SP - 413

EP - 420

BT - Proceedings of International Conference on High Performance Computing & Simulation

PB - IEEE

ER -