IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v125y2017icp258-274.html
   My bibliography  Save this article

Energy-aware stochastic scheduler for batch of precedence-constrained jobs on heterogeneous computing system

Author

Listed:
  • Sajid, Mohammad
  • Raza, Zahid

Abstract

The problem of optimal scheduling of precedence-constrained jobs as well as finding the Pareto-optimal sets for multi objective scheduling problem have been proven to be nondeterministic polynomial time (NP)-complete. The growing consumption of energy has compelled the researchers to consider energy consumption as an important parameter along with other parameters in multi-objective scheduling problem. Accordingly, many energy-aware precedence-constraints scheduling algorithms have been reported in the literature. Most of the algorithms have a limitation of treating this problem as a single objective optimization problem modelling with deterministic execution times rather than stochastic execution times. This work proposes energy-aware stochastic scheduler to schedule the batch of precedence-constrained jobs on dynamic voltage frequency scaling-enabled processors in order to optimize the energy consumption and the turnaround time. The execution and inter-communication times are stochastic which are drawn from independent probability distributions. A novel encoding for batch of precedence-constrained jobs, stochastic turnaround time and energy models are also proposed. Experimental results show that, compared with other algorithms, the proposed scheduler offers reduced turnaround time and reduced energy consumption.

Suggested Citation

  • Sajid, Mohammad & Raza, Zahid, 2017. "Energy-aware stochastic scheduler for batch of precedence-constrained jobs on heterogeneous computing system," Energy, Elsevier, vol. 125(C), pages 258-274.
    • Handle: RePEc:eee:energy:v:125:y:2017:i:c:p:258-274
    DOI: 10.1016/j.energy.2017.02.069
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217302438
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.02.069?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fouda, M.E. & Elwakil, A.S. & Radwan, A.G. & Allagui, A., 2016. "Power and energy analysis of fractional-order electrical energy storage devices," Energy, Elsevier, vol. 111(C), pages 785-792.
    2. Charles E. Clark, 1961. "The Greatest of a Finite Set of Random Variables," Operations Research, INFORMS, vol. 9(2), pages 145-162, April.
    3. Rusin, Andrzej & Wojaczek, Adam, 2015. "Trends of changes in the power generation system structure and their impact on the system reliability," Energy, Elsevier, vol. 92(P1), pages 128-134.
    4. Esmaeili, Mobin & Sedighizadeh, Mostafa & Esmaili, Masoud, 2016. "Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty," Energy, Elsevier, vol. 103(C), pages 86-99.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    2. Hormozi, Elham & Hu, Shuwen & Ding, Zhe & Tian, Yu-Chu & Wang, You-Gan & Yu, Zu-Guo & Zhang, Weizhe, 2022. "Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation," Energy, Elsevier, vol. 252(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bolduc, Denis & Kaci, Mustapha, 1993. "Estimation des modèles probit polytomiques : un survol des techniques," L'Actualité Economique, Société Canadienne de Science Economique, vol. 69(3), pages 161-191, septembre.
    2. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    3. David Bergman & Carlos Cardonha & Jason Imbrogno & Leonardo Lozano, 2023. "Optimizing the Expected Maximum of Two Linear Functions Defined on a Multivariate Gaussian Distribution," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 304-317, March.
    4. Lof, Matthijs & van Bommel, Jos, 2023. "Asymmetric information and the distribution of trading volume," Journal of Corporate Finance, Elsevier, vol. 82(C).
    5. Nasreddine Belbachir & Mohamed Zellagui & Samir Settoul & Claude Ziad El-Bayeh & Ragab A. El-Sehiemy, 2023. "Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Al," Energies, MDPI, vol. 16(4), pages 1-24, February.
    6. Xu, Ying & Ren, Li & Zhang, Zhongping & Tang, Yuejin & Shi, Jing & Xu, Chen & Li, Jingdong & Pu, Dongsheng & Wang, Zhuang & Liu, Huajun & Chen, Lei, 2018. "Analysis of the loss and thermal characteristics of a SMES (Superconducting Magnetic Energy Storage) magnet with three practical operating conditions," Energy, Elsevier, vol. 143(C), pages 372-384.
    7. Dong, Lijun & Kang, Xiaojun & Pan, Mengqi & Zhao, Man & Zhang, Feng & Yao, Hong, 2020. "B-matching-based optimization model for energy allocation in sea surface monitoring," Energy, Elsevier, vol. 192(C).
    8. Elmaghraby, Salah E., 2000. "On criticality and sensitivity in activity networks," European Journal of Operational Research, Elsevier, vol. 127(2), pages 220-238, December.
    9. Hernández-Balaguera, Enrique, 2023. "Fractional model of the chemical inductor," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    10. Elmaghraby, S. E. & Fathi, Y. & Taner, M. R., 1999. "On the sensitivity of project variability to activity mean duration," International Journal of Production Economics, Elsevier, vol. 62(3), pages 219-232, September.
    11. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441, Elsevier.
    12. Martyna Tomala & Andrzej Rusin, 2022. "Risk-Based Operation and Maintenance Planning of Steam Turbine with the Long In-Service Time," Energies, MDPI, vol. 15(14), pages 1-17, July.
    13. Martinetti, Davide & Geniaux, Ghislain, 2017. "Approximate likelihood estimation of spatial probit models," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 30-45.
    14. Bhat, Chandra R., 2018. "New matrix-based methods for the analytic evaluation of the multivariate cumulative normal distribution function," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 238-256.
    15. D'Amato, Rebecca M. (Rebecca Marie) & D'Aquila, Richard T. & Wein, Lawrence M., 1998. "Management of antiretroviral therapy for HIV infection : analyzing when to change therapy," Working papers WP 4043-98., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    16. Shariatkhah, Mohammad-Hossein & Haghifam, Mahmoud-Reza & Chicco, Gianfranco & Parsa-Moghaddam, Mohsen, 2016. "Adequacy modeling and evaluation of multi-carrier energy systems to supply energy services from different infrastructures," Energy, Elsevier, vol. 109(C), pages 1095-1106.
    17. Federica Bomboi & Christoph Buchheim & Jonas Pruente, 2022. "On the stochastic vehicle routing problem with time windows, correlated travel times, and time dependency," 4OR, Springer, vol. 20(2), pages 217-239, June.
    18. Kenneth Train, "undated". "Simulation Methods for Probit and Related Models Based on Convenient Error Partitioning," Working Papers _009, University of California at Berkeley, Econometrics Laboratory Software Archive.
    19. Borsch-Supan, Axel & Hajivassiliou, Vassilis A., 1993. "Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models," Journal of Econometrics, Elsevier, vol. 58(3), pages 347-368, August.
    20. Kingsley E. Haynes & Stewart Fotheringham, 1990. "The Impact of Space on the Application Of Discrete Choice Models," The Review of Regional Studies, Southern Regional Science Association, vol. 20(2), pages 39-49, Spring.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:125:y:2017:i:c:p:258-274. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.