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An efficient Swarm-Intelligence approach for task scheduling in cloud-based internet of things applications

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Abstract

In our rapidly-growing big-data area, often the big sensory data from Internet of Things (IoT) cannot be sent directly to the far data-center in an efficient way because of the limitation in the network infrastructure. Fog computing, which has increasingly gained popularity for real-time applications, offers the utilization of local mini data-centers near the sensors to release the burden from the main data-center, and to exploit the full potential of cloud-based IoT. In this paper, a high-performance approach based on the Max–Min Ant System (MMAS), which is an efficient variation in the family of ant colony optimization algorithms, is proposed to tackle the static task-graph scheduling in homogeneous multiprocessor environments, the predominant technology used as mini-servers in fog computing. The main duty of the proposed approach is to properly manipulate the priority values of tasks so that the most optimal task-order can be achieved. Leveraging background knowledge of the problem, as heuristic values, has made the proposed approach very robust and efficient. Different random task-graphs with different shape parameters have been utilized to evaluate the proposed approach, and the results show its efficiency and superiority versus traditional counterparts from the performance perspective.

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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

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

    Article  MATH  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

  • Botta A, De Donato W, Persico V, Pescapé A (2016) Integration of cloud computing and internet of things: a survey. Future Generation Computer Systems 56:684–700

    Article  Google Scholar 

  • Boveiri HR (2014) Assigning tasks to the processors for task-graph scheduling in parallel systems using learning and cellular learning automata. In Proc. 1st National Conf. on Computer Engineering and Information Technology, 2014, pp. 1–8 (in Farsi)

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

    Article  Google Scholar 

  • Boveiri HR (2015b) List-Scheduling Techniques in Homogeneous Multiprocessor Environments: A Survey. Int J Softw Eng Appl 9(4):123–132

    Google Scholar 

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

    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 Computat Intell Syst 9(5):800–811

    Article  Google Scholar 

  • Boveiri HR (2017) An incremental ant colony optimization based approach to task assignment to processors for multiprocessor scheduling. Front Inform Technol Electron Eng 18(4):498–510

    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, Khayami R (2017) Static homogeneous multiprocessor task graph scheduling using ant colony optimization. KSII Trans Int Inform Syst (TIIS) 11(6):3046–3070

    Google Scholar 

  • Cassales GW, Charão AS, Kirsch-Pinheiro M, Souveyet C, Steffenel LA (2016) Improving the performance of Apache Hadoop on pervasive environments through context-aware scheduling. J Ambient Intell Humaniz Comput 7(3):333–345

    Article  Google Scholar 

  • Chretienne P et al (1995) Scheduling Theory and Its Application. Wiley, New York

    Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1991) Positive feedback as a search strategy. Final report. Politecnico di Milano, Milan, pp. 91–016

    Google Scholar 

  • Dorigo M, Caro GD, Gambardella LM (1999) Ant algorithms for discrete optimization. Artif Life 5(2):137–172

    Article  Google Scholar 

  • Guerrero C, Lera I, Juiz C (2018) A lightweight decentralized service placement policy for performance optimization in fog computing. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0914-0

    Article  Google Scholar 

  • Hu P, Dhelim S, Ning H, Qiu T (2017) Survey on fog computing: architecture, key technologies, applications and open issues. J Netw Comput Appl 98:27–42. https://doi.org/10.1016/j.jnca.2017.09.002

    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  MATH  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  MATH  Google Scholar 

  • Kruatrachue B, Lewis TG. Duplication Scheduling Heuristics (DSH): A New Precedence Task Scheduler for Parallel Processor Systems. Final report. Oregon State University, Corvallis; 1987. Report No.: OR 97331

  • McCreary C, Gill H (1989) Automatic determination of grain size for efficient parallel processing. Commun ACM 32(9):1073–1078

    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 

  • Stutzle T, Hoos H (1997) MAX-MIN ant system and local search for the traveling salesman problem. In: IEEE International Conference on Evolutionary Computation, 1997, pp. 309–314

  • Stützle T, Hoos H (1998) Improvements on the ant-system: Introducing the MAX-MIN ant system. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna, 1998, pp. 245–249

    Chapter  Google Scholar 

  • Sze V, Chen YH, Yang TJ, Emer JS (2017) Efficient processing of deep neural networks: a tutorial and survey. Proc IEEE 105(12):2295–2329

    Article  Google Scholar 

  • Wang H, Wang Y (2018) Maximizing reliability and performance with reliability-driven task scheduling in heterogeneous distributed computing systems. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0926-9

    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 

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Authors and Affiliations

  1. Computer Engineering and IT Department, Shiraz University of Technology, Shiraz, Iran

    Hamid Reza Boveiri & Raouf Khayami

  2. Faculty of Computers and Information, Mansoura University, Mansoura, Egypt

    Mohamed Elhoseny

  3. VIT University, Vellore, India

    M. Gunasekaran

Authors
  1. Hamid Reza Boveiri
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  2. Raouf Khayami
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  3. Mohamed Elhoseny
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  4. M. Gunasekaran
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Correspondence to Hamid Reza Boveiri.

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Boveiri, H.R., Khayami, R., Elhoseny, M. et al. An efficient Swarm-Intelligence approach for task scheduling in cloud-based internet of things applications. J Ambient Intell Human Comput 10, 3469–3479 (2019). https://doi.org/10.1007/s12652-018-1071-1

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