Toggle Main Menu Toggle Search

Open Access padlockePrints

Optimising energy and overhead for large parameter space simulations

Lookup NU author(s): Alex Kell, Dr Matthew Forshaw, Dr Stephen McGough

Downloads


Licence

This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2019.

For re-use rights please refer to the publisher's terms and conditions.


Abstract

Many systems require optimisation over multiple objectives, where objectives are characteristics of the system such as energy consumed or increase in time to perform the work. Optimisation is performed by selecting the `best' set of input parameters to elicit the desired objectives. However, the parameter search space can often be far larger than can be searched in a reasonable time. Additionally, the objectives are often mutually exclusive -- leading to a decision being made as to which objective is more important or optimising over a combination of the objectives. This work is an application of a Genetic Algorithm to identify the Pareto frontier for finding the optimal parameter sets for all combinations of objectives. A Pareto frontier can be used to identify the sets of optimal parameters for which each is the `best' for a given combination of objectives -- thus allowing decisions to be made with full knowledge. We demonstrate this approach for the HTC-Sim simulation system in the case where a Reinforcement Learning scheduler is tuned for the two objectives of energy consumption and task overhead. Demonstrating that this approach can reduce the energy consumed by ~36% over previously published work without significantly increasing the overhead.


Publication metadata

Author(s): Kell AJM, Forshaw M, McGough AS

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: The Tenth International Green and Sustainable Computing Conference (IGSC)

Year of Conference: 2019

Pages: 8

Online publication date: 13/01/2020

Acceptance date: 06/10/2019

Date deposited: 06/10/2019

Publisher: IEEE

URL: https://doi.org/10.1109/IGSC48788.2019.8957205

DOI: 10.1109/IGSC48788.2019.8957205

Library holdings: Search Newcastle University Library for this item

ISBN: 9781728154176


Actions

Link to this publication


Share