Skip to main content

Advertisement

SpringerLink
Log in

An algorithm with harmonious blending of distributed swarm intelligence and geometric Brownian motion for greener heterogeneous scheduling

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

This paper focuses on the design and implementation of green heterogeneous scheduling algorithm based on the theory and technology hotspot of energy saving and emission reduction in cloud computing, since intelligent scheduling algorithms often have defects such as insufficient dynamic optimization power or poor parallel framework design. Following that, a distributed swarm intelligence optimization algorithm for greener heterogeneous scheduling, is proposed, including ⓵ an optimal blending pattern of particle swarm intelligence and nonlinear geometric Brownian motion, based on the related physical sciences and stochastic mathematical analyses, and ⓶ a parallel fusion model that is suitable for the management server processor hybrid system, i.e., coarse-grained parallelism between nodes and CPU/GPU master-slave in a single node. Then, a large number of experimental results are given, whose evaluation indicators can be divided into two categories: static and dynamic optimization performance. Compared with most newly published scheduling algorithms, there are significant advantages of the proposed algorithm on the dynamic optimization performance for consistent or semiconsistent and large inconsistent scheduling instances, although with the lower improvement for the small inconsistent instances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Banos D, Nilssen T, Proske F (2020) Strong existence and higher order frechet differentiability of stochastic flows of fractional Brownian motion driven SDEs with singular drift. J Dyn Differ Eq 32(4):1819–1866. https://doi.org/10.1007/s10884-019-09789-4

  2. Betsakos D, Boudabra M, Markowsky G (2021) On the probability of fast exits and long stays of a planar Brownian motion in simply connected domains. J Math Anal Appl 493(1). https://doi.org/10.1016/j.jmaa.2020.124454

  3. Bohaienko V, Gladky A, Romashchenko M, Matiash T (2021) Identification of fractional water transport model with psi-Caputo derivatives using particle swarm optimization algorithm. Appl Math Comput 390. https://doi.org/10.1016/j.amc.2020.125665

  4. Bonyadi MR, Michalewicz Z (2017) Particle swarm optimization for single objective continuous space problems: A review. Evol Comput 25(1):1–54. https://doi.org/10.1162/EVCO_r_00180

  5. Chen X, Li K, Xu B, Yang Z (2020) Biogeography-based learning particle swarm optimization for combined heat and power economic dispatch problem. Knowl-Based Syst 208. https://doi.org/10.1016/j.knosys.2020.106463

  6. Darwish A, Ezzat D, Hassanien AE (2020) An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis. Swarm Evol Comput 52. https://doi.org/10.1016/j.swevo.2019.100616

  7. Das A, Dhar A, Santra I, Satpathi U, Sinha S (2020) Quantum Brownian motion: Drude and Ohmic baths as continuum limits of the Rubin model. Phys Rev E 102(6). https://doi.org/10.1103/PhysRevE.102.062130

  8. Hu D, Qiu X, Liu Y, Zhou X (2021) Probabilistic convergence analysis of the stochastic particle swarm optimization model without the stagnation assumption. Inf Sci 547:996–1007. https://doi.org/10.1016/j.ins.2020.08.072

  9. Peng S (2007) G-Expectation,G-Brownian Motion and Related Stochastic Calculus of Ito’s type. The Abel Symposium 2005, Abel Symposia. Springer, pp 541–567

  10. Peng S (2019) Nonlinear expectations and stochastic calculus under uncertainty. Springer

  11. Piotrowski AP, Napiorkowski JJ, Piotrowska AE (2020) Population size in particle swarm optimization. Swarm Evol Comput 58. https://doi.org/10.1016/j.swevo.2020.100718

  12. Ren Y, Sakthivel R, Sun G (2020) Robust stability and boundedness of stochastic differential equations with delay driven by G-Brownian motion. Int J Control 93(12):2886–2895. https://doi.org/10.1080/00207179.2019.1566645

  13. Santos R, Borges G, Santos A, Silva M, Sales C, Costa JCWA (2020) A rotationally invariant semi-autonomous particle swarm optimizer with directional diversity. Swarm Evol Comput 56. https://doi.org/10.1016/j.swevo.2020.100700

  14. Sedighizadeh D, Masehian E, Sedighizadeh M, Akbaripour H (2021) GEPSO: A new generalized particle swarm optimization algorithm. Math Comput Simul 179:194–212. https://doi.org/10.1016/j.matcom.2020.08.013

  15. Shahnazi-Pour A, Moghaddam BP, Babaei A (2021) Numerical simulation of the Hurst index of solutions of fractional stochastic dynamical systems driven by fractional Brownian motion. J Comput Appl Math 386. https://doi.org/10.1016/j.cam.2020.113210

  16. Song W, Cattani C, Chi CH (2020) Multifractional Brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting: An integrated approach. Energy 194. https://doi.org/10.1016/j.energy.2019.116847

  17. Braun TD, Siegel HJ, Beck N (2001) A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J Parallel Distrib Comput 61(6):810–837

  18. Tirkolaee EB, Aydin NS, Ranjbar-Bourani M, Weber GW (2020a) A robust bi-objective mathematical model for disaster rescue units allocation and scheduling with learning effect. Comput Ind Eng 149. https://doi.org/10.1016/j.cie.2020.106790

  19. Tirkolaee EB, Goli A, Faridnia A, Soltani M, Weber GW (2020b) Multi-objective optimization for the reliable pollution-routing problem with cross-dock selection using Pareto-based algorithms. J Clean Prod 276. https://doi.org/10.1016/j.jclepro.2020.122927

  20. Tirkolaee EB, Goli A, Weber GW (2020c) Fuzzy Mathematical Programming and Self-Adaptive Artificial Fish Swarm Algorithm for Just-in-Time Energy-Aware Flow Shop Scheduling Problem With Outsourcing Option. IEEE Trans Fuzzy Syst 28(11):2772–2783. https://doi.org/10.1109/TFUZZ.2020.2998174

  21. Tirkolaee EB, Mardani A, Dashtian Z, Soltani M, Weber G W (2020d) A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design. J Clean Prod 250 https://doi.org/10.1016/j.jclepro.2019.119517

  22. Wei B, Xia X, Yu F, Zhang Y, Xu X, Wu H, Gui L, He G (2020) Multiple adaptive strategies based particle swarm optimization algorithm. Swarm Evol Comput 57. https://doi.org/10.1016/j.swevo.2020.100731

  23. Yang Z, Yang H, Yao Z (2021) Strong convergence analysis for Volterra integro-differential equations with fractional Brownian motions. J Comput Appl Math 383. https://doi.org/10.1016/j.cam.2020.113156

Download references

Funding

This study was funded by National High Technology Research and Development Program of China(863 Program) (No.2012AA01A306), National Science Foundation for Young Scholars of China (No.61702248) and Talent Introduction Project of Ludong University (No.LB2016015).

Author information

Authors and Affiliations

  1. School of Information and Electrical Engineering, Ludong University, Yantai, China

    Jinglian Wang

  2. School of Computer Science and Technology, Shandong University, Jinan, China

    Bin Gong

  3. School of Information Science and Technology, Shandong Normal University, Jinan, China

    Hong Liu & Shaohui Li

Authors
  1. Jinglian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaohui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinglian Wang.

Ethics declarations

Ethical Approval

All procedure performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Conflict of Interest

Jinglian Wang declares that she has no conflict of interest. Bin Gong declares that he has no conflict of interest. Hong Liu declares that she has no conflict of interest. Shaohui Li declares that he has no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

National High Technology Research and Development Program of China(863 Program) (No.2006AA01A113 and No.2012AA01A306) and National Natural Science Foundation of China (No.61702248) and Talent Introduction Project of Ludong University (No.LB2016015)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Gong, B., Liu, H. et al. An algorithm with harmonious blending of distributed swarm intelligence and geometric Brownian motion for greener heterogeneous scheduling. Appl Intell 52, 18210–18225 (2022). https://doi.org/10.1007/s10489-021-03074-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-03074-y

Keywords

Search

Navigation