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An enhanced cuckoo optimization algorithm for task graph scheduling in cluster-computing systems

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Abstract

Optimized task scheduling is key to achieve high performance in the cluster-computing systems whose application is broad ranging from scientific to the military purposes. This combinatorial problem is NP-hard from the time complexity perspective, where applying newly proposed metaheuristics to it deserves further investigation based on the well-known no-free-lunch theorem. Accordingly, in this paper, an enhanced version of cuckoo optimization algorithm (COA) named E-COA is proposed to cope with the static task scheduling problem in the mesh topology cluster-computing environments. The proposed approach is equipped with an efficient adaptive semi-stochastic egg-laying strategy that significantly improves the local and global search potentiality of the basic COA. The experiments on a comprehensive set of randomly generated task graphs with different structural parameters reveal the efficiency of the proposed approach from the performance point of view, especially for the small-scale samples, and where the number of clusters in the machine is very restricted, i.e., we are in the lack of computational resource.

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Notes

  1. Highest level first with estimated time.

  2. Insertion scheduling heuristic.

  3. Which uses the cluster-like CLANs to partition the task graph.

  4. Localized allocation of static tasks.

  5. Earliest time first.

  6. Dynamic-level scheduling.

  7. Modified critical path.

References

  • Abu-Jamous B, Fa R, Nandi AK (2015) Integrative cluster analysis in bioinformatics. Wiley, New York

    Book  Google Scholar 

  • Adam TL, Chandy KM, Dickson JR (1974) A comparison of list schedules for parallel processing systems. Commun ACM 17(12):685–690

    Article  Google Scholar 

  • Akbari M, Rashidi H (2016) A multi-objectives scheduling algorithm based on cuckoo optimization for task allocation problem at compile time in heterogeneous systems. Expert Syst Appl 60:234–248

    Article  Google Scholar 

  • Bashir MB, Latiff MSBA, Coulibaly Y, Yousif A (2016) A survey of grid-based searching techniques for large scale distributed data. J Netw Comput Appl 60:170–179

    Article  Google Scholar 

  • Baxter J, Patel J (1989) The LAST algorithm—a heuristic-based static task allocation algorithm. In: 1989 International conference on parallel processing, University Park, PA

  • Bazgosha A, Ranjbar M, Jamili N (2017) Scheduling of loading and unloading operations in a multi stations transshipment terminal with release date and inventory constraints. Comput Ind Eng 106:20–31

    Article  Google Scholar 

  • Boveiri HR (2015a) List-scheduling techniques in homogeneous multiprocessor environments: a survey. Int J Softw Eng Appl 9(4):123–132

    Google Scholar 

  • Boveiri HR (2015b) An efficient task priority measurement for list-scheduling in multiprocessor environments. Int J Softw Eng Appl 9(5):233–246

    Google Scholar 

  • Boveiri HR (2015c) Multiprocessor task graph scheduling using a novel graph-like learning automata. Int J Grid Distrib Comput 8(1):41–54

    Article  Google Scholar 

  • Boveiri HR (2015d) Task assigning techniques for list-scheduling in homogeneous multiprocessor environments: a survey. Int J Softw Eng Appl 9(12):303–312

    Google Scholar 

  • Boveiri HR (2016) A novel ACO-based static task scheduling approach for multiprocessor environments. Int J Comput Intell Syst 9(5):800–811

    Article  Google Scholar 

  • Boveiri HR (2018) 125 random task-graphs for multiprocessor task scheduling. Mendeley Data. https://doi.org/10.17632/4fycv9td56.2

    Article  Google Scholar 

  • Boveiri HR, Elhoseny M (2018) A-COA: an adaptive cuckoo optimization algorithm. Neural Comput Appl. https://doi.org/10.1007/s0052

    Article  Google Scholar 

  • Boveiri HR, Khayami R (2017) Static homogeneous multiprocessor task graph scheduling using ant colony optimization. KSII Trans Internet Inf Syst 11(6):3046–3070

    Google Scholar 

  • Boveiri HR, Khayami R, Elhoseny M, Gunasekaran M (2019) An efficient Swarm-Intelligence approach for task scheduling in cloud-based internet of things applications. J Ambient Intell Humaniz Comput 10(9):3469–3479

    Article  Google Scholar 

  • Buyya R (1999) High performance cluster computing: architectures and systems, vol 1. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Cao J, Chan AT, Sun Y, Das SK, Guo M (2006) A taxonomy of application scheduling tools for high performance cluster computing. Cluster Comput 9(3):355–371

    Article  Google Scholar 

  • Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19(2):171–209

    Article  Google Scholar 

  • Elyasigomari V, Lee DA, Screen HR, Shaheed MH (2017) Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckoo optimization algorithm and harmony search for cancer classification. J Biomed Inform 67:11–20

    Article  Google Scholar 

  • Faradonbeh RS, Monjezi M (2017) Prediction and minimization of blast-induced ground vibration using two robust meta-heuristic algorithms. Eng Comput 33(4):835–851

    Article  Google Scholar 

  • Hwang JJ, Chow YC, Anger FD, Lee CY (1989) Scheduling precedence graphs in systems with interprocessor communication times. SIAM J Comput 18(2):244–257

    Article  MathSciNet  Google Scholar 

  • Hwang R, Gen M, Katayama H (2008) A comparison of multiprocessor task scheduling algorithms with communication costs. Comput Oper Res 35(3):976–993

    Article  MathSciNet  Google Scholar 

  • Kruatrachue B, Lewis TG (1987) Duplication scheduling heuristic: a new precedence task scheduler for parallel systems. Technical report 87-60-3

  • Kwok YK, Ahmad I (1999) Benchmarking and comparison of the task graph scheduling algorithms. J Parallel Distrib Comput 59(3):381–422

    Article  Google Scholar 

  • McCreary C, Gill H (1989) Automatic determination of grain size for efficient parallel processing. Commun ACM 32(9):1073–1079

    Article  Google Scholar 

  • Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput 11(8):5508–5518

    Article  Google Scholar 

  • Sih GC, Lee EA (1993) A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures. IEEE Trans Parallel Distrib Syst 4(2):175–187

    Article  Google Scholar 

  • Top 500 Supercomputer List (2017) http://www.top500.org/

  • Torus Interconnect (2019) https://en.wikipedia.org/wiki/Torus_interconnect

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  • Wu MY, Gajski DD (1990) Hypertool: a programming aid for message-passing systems. IEEE Trans Parallel Distrib Syst 1(3):330–343

    Article  Google Scholar 

  • Xiong Y, Wan S, She J, Wu M, He Y, Jiang K (2016) An energy-optimization-based method of task scheduling for a cloud video surveillance center. J Netw Comput Appl 59:63–73

    Article  Google Scholar 

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Acknowledgements

This work has been financially supported by the Department of Gotvand, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran, as a research project.

Funding

This study was funded by the Department of Gotvand, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran, as a research project.

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  1. Department of Gotvand, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran

    Hamid Reza Boveiri

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  1. Hamid Reza Boveiri
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Correspondence to Hamid Reza Boveiri.

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Communicated by V. Loia.

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Boveiri, H.R. An enhanced cuckoo optimization algorithm for task graph scheduling in cluster-computing systems. Soft Comput 24, 10075–10093 (2020). https://doi.org/10.1007/s00500-019-04520-3

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