Energy aware adaptive scheduling of workflows

Mehul Warade; Kevin Lee; Chathurika Ranaweera; Jean Guy Schneider

doi:10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00078

Energy aware adaptive scheduling of workflows

Mehul Warade
, Kevin Lee
, Chathurika Ranaweera
, Jean Guy Schneider

Information Technology Faculty Office

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

1 Citation (Scopus)

Abstract

Scientific workflows consist of multi-step compu-tational tasks executing in the form of data flow and task dependencies. These workflows are defined to be long running and fault tolerant. There is evidence of improving performance achieved through run-time adaptive changes made to the work-flow execution. The aim of the work presented in this paper is to highlight the benefits that adaptive scheduling of scientific workflows have on the energy consumption of the computation. In this paper, an architecture for the implementation of an energy-aware adaptive scheduler is presented. The monitoring, analysis, planning and execution (MAPE) model from autonomic computing is used to propose a set of run-time modifications that will be used by the scheduler to improve the performance and energy consumption of the workflow.

Original language	English
Title of host publication	Proceedings - The 20th IEEE International Symposium on Parallel and Distributed Processing with Applications
Editors	Haipeng Dai, Rajiv Ranjan, Massimo Cafaro
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	562-570
Number of pages	9
ISBN (Electronic)	9781665464970
ISBN (Print)	9781665464987
DOIs	https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00078
Publication status	Published - 2022
Event	International Symposium on Parallel and Distributed Processing with Applications 2022 - Melbourne, Australia Duration: 17 Dec 2022 → 19 Dec 2022 Conference number: 20th https://ieeexplore.ieee.org/xpl/conhome/10070589/proceeding (Proceedings) https://ispa2019.com/ (Website)

Conference

Conference	International Symposium on Parallel and Distributed Processing with Applications 2022
Abbreviated title	ISPA 2022
Country/Territory	Australia
City	Melbourne
Period	17/12/22 → 19/12/22
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10070589/proceeding (Proceedings) https://ispa2019.com/ (Website)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Adaptive
Cluster Computing
Energy-Aware
MAPE
Workflows

Access to Document

10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00078

Cite this

Warade, M., Lee, K., Ranaweera, C., & Schneider, J. G. (2022). Energy aware adaptive scheduling of workflows. In H. Dai, R. Ranjan, & M. Cafaro (Eds.), Proceedings - The 20th IEEE International Symposium on Parallel and Distributed Processing with Applications (pp. 562-570). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00078

@inproceedings{6dd8bb703ba5426882eeac7462adee67,

title = "Energy aware adaptive scheduling of workflows",

abstract = "Scientific workflows consist of multi-step compu-tational tasks executing in the form of data flow and task dependencies. These workflows are defined to be long running and fault tolerant. There is evidence of improving performance achieved through run-time adaptive changes made to the work-flow execution. The aim of the work presented in this paper is to highlight the benefits that adaptive scheduling of scientific workflows have on the energy consumption of the computation. In this paper, an architecture for the implementation of an energy-aware adaptive scheduler is presented. The monitoring, analysis, planning and execution (MAPE) model from autonomic computing is used to propose a set of run-time modifications that will be used by the scheduler to improve the performance and energy consumption of the workflow.",

keywords = "Adaptive, Cluster Computing, Energy-Aware, MAPE, Workflows",

author = "Mehul Warade and Kevin Lee and Chathurika Ranaweera and Schneider, \{Jean Guy\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; International Symposium on Parallel and Distributed Processing with Applications 2022, ISPA 2022 ; Conference date: 17-12-2022 Through 19-12-2022",

year = "2022",

doi = "10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00078",

language = "English",

isbn = "9781665464987",

pages = "562--570",

editor = "Haipeng Dai and Rajiv Ranjan and Massimo Cafaro",

booktitle = "Proceedings - The 20th IEEE International Symposium on Parallel and Distributed Processing with Applications",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

url = "https://ieeexplore.ieee.org/xpl/conhome/10070589/proceeding, https://ispa2019.com/",

}

Warade, M, Lee, K, Ranaweera, C & Schneider, JG 2022, Energy aware adaptive scheduling of workflows. in H Dai, R Ranjan & M Cafaro (eds), Proceedings - The 20th IEEE International Symposium on Parallel and Distributed Processing with Applications. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 562-570, International Symposium on Parallel and Distributed Processing with Applications 2022, Melbourne, Victoria, Australia, 17/12/22. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00078

Energy aware adaptive scheduling of workflows. / Warade, Mehul; Lee, Kevin; Ranaweera, Chathurika et al.
Proceedings - The 20th IEEE International Symposium on Parallel and Distributed Processing with Applications. ed. / Haipeng Dai; Rajiv Ranjan; Massimo Cafaro. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2022. p. 562-570.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

TY - GEN

T1 - Energy aware adaptive scheduling of workflows

AU - Warade, Mehul

AU - Lee, Kevin

AU - Ranaweera, Chathurika

AU - Schneider, Jean Guy

N1 - Conference code: 20th

PY - 2022

Y1 - 2022

N2 - Scientific workflows consist of multi-step compu-tational tasks executing in the form of data flow and task dependencies. These workflows are defined to be long running and fault tolerant. There is evidence of improving performance achieved through run-time adaptive changes made to the work-flow execution. The aim of the work presented in this paper is to highlight the benefits that adaptive scheduling of scientific workflows have on the energy consumption of the computation. In this paper, an architecture for the implementation of an energy-aware adaptive scheduler is presented. The monitoring, analysis, planning and execution (MAPE) model from autonomic computing is used to propose a set of run-time modifications that will be used by the scheduler to improve the performance and energy consumption of the workflow.

AB - Scientific workflows consist of multi-step compu-tational tasks executing in the form of data flow and task dependencies. These workflows are defined to be long running and fault tolerant. There is evidence of improving performance achieved through run-time adaptive changes made to the work-flow execution. The aim of the work presented in this paper is to highlight the benefits that adaptive scheduling of scientific workflows have on the energy consumption of the computation. In this paper, an architecture for the implementation of an energy-aware adaptive scheduler is presented. The monitoring, analysis, planning and execution (MAPE) model from autonomic computing is used to propose a set of run-time modifications that will be used by the scheduler to improve the performance and energy consumption of the workflow.

KW - Adaptive

KW - Cluster Computing

KW - Energy-Aware

KW - MAPE

KW - Workflows

UR - https://www.scopus.com/pages/publications/85151929838

U2 - 10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00078

DO - 10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00078

M3 - Conference Paper

AN - SCOPUS:85151929838

SN - 9781665464987

SP - 562

EP - 570

BT - Proceedings - The 20th IEEE International Symposium on Parallel and Distributed Processing with Applications

A2 - Dai, Haipeng

A2 - Ranjan, Rajiv

A2 - Cafaro, Massimo

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - International Symposium on Parallel and Distributed Processing with Applications 2022

Y2 - 17 December 2022 through 19 December 2022

ER -

Warade M, Lee K, Ranaweera C, Schneider JG. Energy aware adaptive scheduling of workflows. In Dai H, Ranjan R, Cafaro M, editors, Proceedings - The 20th IEEE International Symposium on Parallel and Distributed Processing with Applications. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2022. p. 562-570 doi: 10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00078

Energy aware adaptive scheduling of workflows

Abstract

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Cite this