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Cooperative agents-based approach for workflow scheduling on fog-cloud computing

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Abstract

Connected objects in the Internet of Things (IoT) domain are widespread everywhere. They interact with each other and cooperate with their neighbors to achieve a common goal. Most of these objects generate a huge amount of data, often requiring a process under strict time constraints. Being motivated by the question of optimizing the execution time of these IoT tasks, we remain aware of the sensitivity to latency and the volume of data generated. In this article, we propose a hybrid Cloud-Fog multi-agent approach to schedule a set of dependent IoT tasks modeled as a workflow. The major advantage of our approach is to allow to model IoT workflow planning as a multi-objetif optimization problem in order to create a compromise planning solution in terms of response time, cost and makespan. In addition to taking into account data communications between workflow tasks, during the planning process, our approach has two other advantages: (1) maximizing the use of Fog Computing in order to minimize response time, and (2) the use of elastic cloud computing resources at minimum cost. The implementation of the MAS-GA (Multi-Agent System based Genetic Algorithm), which we have proposed in this context; the series of experiments carried out on different corpora, as well as the analysis of the found results confirm the feasibility of our approach and its performance in terms of cost which represents an average gain of 21.38% compared to Fog and 25.49% compared to Cloud, makespan which represents a gain of 14.13% compared to Fog and a slight increase of 5.24% compared to Cloud and in response time which represents an average gain of 46.66% compared to Cloud with a slight increase of 6.66% compared to Fog, while strengthening the collaboration between Fog computing and Cloud computing.

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References

  • Aburukba RO, AliKarrar M, Landolsi T, El-Fakih K (2020) Scheduling internet of things requests to minimize latency in hybrid fog-cloud-computing. Future Gener Comput Syst 20:539–551

    Google Scholar 

  • Alaei M, Khorsand R, Ramezanpour M (2020) An adaptive fault detector strategy for scientific workflow scheduling based on improved differential evolution algorithm in cloud. Appl Soft Comput 20:20

    Google Scholar 

  • Ali IM, Sallam KM, Moustafa N, Chakraborty R, Ryan MJ, Choo KKR (2020) An automated task scheduling model using non-dominated sorting genetic algorithm ii for fog-cloud systems. IEEE Trans Cloud Comput 20:1–1

    Google Scholar 

  • Bellifemine F, Poggi A, Rimassa G (2000) Developing multi-agent systems with jade. In: International workshop on agent theories, architectures, and languages, pp 89–103

  • Bhatia M, Sood SK, Kaur S (2020) Quantumized approach of load scheduling in fog computing environment for IoT applications. Computing 20:1–19

    Google Scholar 

  • Binh HTT, Anh TT, Son DB, Duc PA, Nguyen BM (2018) An evolutionary algorithm for solving task scheduling problem in cloud-fog computing environment. In: Proceedings of the ninth international symposium on information and communication technology, pp 397–404

  • Bittencourt LF, Goldman A, Madeira ER, da Fonseca NL, Sakellariou R (2018) Scheduling in distributed systems: a cloud computing perspective. Comput Sci Rev 20:31–54

    Google Scholar 

  • Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pp 13–16

  • Chen W, Deelman E (2012) Workflowsim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th international conference on E-science, pp 1–8

  • De Maio V, Kimovski D (2020) Multi-objective scheduling of extreme data scientific workflows in fog. Future Gener Comput Syst 20:171–184

    Google Scholar 

  • Feki MA, Kawsar F, Boussard M, Trappeniers L (2013) The internet of things: the next technological revolution. Computer 20:24–25

    Google Scholar 

  • Fellir F, El Attar A, Nafil K, Chung L (2020) A multi-agent based model for task scheduling in cloud-fog computing platform. In: 2020 IEEE international conference on informatics, IoT, and enabling technologies (ICIoT), pp 377–382

  • Goderis A, De Roure D, Goble C, Bhagat J, Cruickshank D, Fisher P, Michaelides D, Tanoh F (2008) Discovering scientific workflows: the my experiment benchmarks. Commun ACM 20:1–10

    Google Scholar 

  • Goldberg DE (1994) Genetic and evolutionary algorithms come of age. Commun ACM 20:113–120

    Google Scholar 

  • Hajlaoui JE, Omri MN, Benslimane D (2017a) Multi-tenancy aware configurable service discovery approach in cloud computing. In: 2017 IEEE 26th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE), pp 232–237

  • Hajlaoui JE, Omri MN, Benslimane D, Barhamgi M (2017b) Qos based framework for configurable iaas cloud services discovery. In: 2017 IEEE international conference on web services (ICWS), pp 460–467

  • Helali L, Omri MN (2021) A survey of data center consolidation in cloud computing systems. Comput Sci Rev 20:39

    Google Scholar 

  • Holland JH (1992) Genetic algorithms. Sci Am 20:66–73

    Google Scholar 

  • Ismayilov G, Topcuoglu HR (2020) Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing. Future Gener Comput Syst 20:307–322

    Google Scholar 

  • Jiang YC, Wang JF (2007) Temporal partitioning data flow graphs for dynamically reconfigurable computing. IEEE Trans Very Large Scale Integrat Syst 20:1351–1361

    Google Scholar 

  • Lobo FG, Goldberg DE, Pelikan M (2000) Time complexity of genetic algorithms on exponentially scaled problems. In: Proceedings of the 2nd annual conference on genetic and evolutionary computation, pp 151–158

  • Mohammadzadeh A, Masdari M, Gharehchopogh FS, Jafarian A (2020) A hybrid multi-objective metaheuristic optimization algorithm for scientific workflow scheduling. Cluster Comput 20:1–25

    Google Scholar 

  • Mutlag AA, Khanapi Abd Ghani M, Mohammed MA, Maashi MS, Mohd O, Mostafa SA, Abdulkareem KH, Marques G, de la Torre Díez I (2020) Mafc: multi-agent fog computing model for healthcare critical tasks management. Sensors 20:1853

    Google Scholar 

  • Pham XQ, Huh EN (2016) Towards task scheduling in a cloud-fog computing system. In: 2016 18th Asia-Pacific network operations and management symposium (APNOMS), pp 1–4

  • Pham XQ, Man ND, Tri NDT, Thai NQ, Huh EN (2017) A cost-and performance-effective approach for task scheduling based on collaboration between cloud and fog computing. Int J Distrib Sens Netw 20:1550147717742073

    Google Scholar 

  • Rasheed S, Javaid N, Rehman S, Hassan K, Zafar F, Naeem M (2018) A cloud-fog based smart grid model using max-min scheduling algorithm for efficient resource allocation. In: International conference on network-based information systems, pp 273–285

  • Robusto CC (1957) The cosine-haversine formula. Am Math Mon 20:38–40

    MathSciNet  Google Scholar 

  • Saeedi S, Khorsand R, Bidgoli SG, Ramezanpour M (2020) Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing. Comput Ind Eng 20:106649

    Google Scholar 

  • Sharma C, Rashid M (2020) Scheduling of scientific workflow in distributed cloud environment using hybrid PSO algorithm. Trends Cloud Based IoT IEEE 20:113–123

    Google Scholar 

  • Stavrinides GL, Karatza HD (2019) A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments. Multimed Tools Appl 20:24639–24655

    Google Scholar 

  • Tychalas D, Karatza H (2020) A scheduling algorithm for a fog computing system with bag-of-tasks jobs: simulation and performance evaluation. Simul Model Pract Theory 20:101982

    Google Scholar 

  • Wang X, Yeo CS, Buyya R, Su J (2011) Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm. Future Gener Comput Syst 20:1124–1134

    Google Scholar 

  • Wang Y, Liu H, Zheng W, Xia Y, Li Y, Chen P, Guo K, Xie H (2019) Multi-objective workflow scheduling with deep-q-network-based multi-agent reinforcement learning. IEEE Access 20:39974–39982

    Google Scholar 

  • Wang Y, Guo Y, Guo Z, Baker T, Liu W (2020) Closure: a cloud scientific workflow scheduling algorithm based on attack-defense game model. Future Gener Comput Syst 20:460–474

    Google Scholar 

  • Wei X (2020) Task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing. J Ambient Intell Human Comput 20:1–12

    Google Scholar 

  • Xu X, Cao H, Geng Q, Liu X, Dai F, Wang C (2020) Dynamic resource provisioning for workflow scheduling under uncertainty in edge computing environment. Concurre Comput Pract Exp 20:e5674

    Google Scholar 

  • Yassa S (2014) Allocation optimale multicontraintes des workflows aux ressources d’un environnement cloud computing. PhD thesis, Cergy-Pontoise

  • Yassa S, Chelouah R, Kadima H, Granado B (2013a) Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. Sci World J 20:20

    MATH  Google Scholar 

  • Yassa S, Sublime J, Chelouah R, Kadima H, Jo GS, Granado B (2013b) A genetic algorithm for multi-objective optimisation in workflow scheduling with hard constraints. Int J Metaheurist 20:415–433

    MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. MARS Laboratory University of Sousse, Sousse, Tunisia

    Marwa Mokni, Jalel Eddine Hajlaoui & Mohamed Nazih Omri

  2. ETIS Laboratory, CNRS, UMR8051, University of CY Cergy, Paris, France

    Marwa Mokni & Rachid Chelouah

  3. University of  CY Cergy, Paris, France

    Sonia Yassa

Authors
  1. Marwa Mokni
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  2. Sonia Yassa
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  3. Jalel Eddine Hajlaoui
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  4. Rachid Chelouah
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  5. Mohamed Nazih Omri
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Correspondence to Marwa Mokni.

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Mokni, M., Yassa, S., Hajlaoui, J.E. et al. Cooperative agents-based approach for workflow scheduling on fog-cloud computing. J Ambient Intell Human Comput 13, 4719–4738 (2022). https://doi.org/10.1007/s12652-021-03187-9

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  • DOI: https://doi.org/10.1007/s12652-021-03187-9

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