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A decentralized prediction-based workflow load balancing architecture for cloud/fog/IoT environments

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Abstract

Processing of data gathered from new communication devices, such as Internet of Things (IoT)-based technology, has grown dramatically in the past decade. Resource management plays a vital role in cloud/fog-based platforms’ efficiency. Alternatively, a deadline-based workflow scheduling mechanism is an approach to resource management that increases cloud/fog computing efficiency. However, most proposed methods may overload some resources and underload others. Consequently, adopting a proper load-balancing approach has a major impact on optimizing Quality of Service (QoS) and improving customer satisfaction. This paper presents a 4-layer software architecture for analyzing workflows and dynamic resources in cloud/fog/IoT environments to address such a problem. This approach also considers workload and presence prediction of IoT nodes as dynamic resources. Moreover, the 4 + 1 architectural view models represent architecture layers, components, and significant interactions. Architecture components are ultimately proposed to meet quality attributes such as availability, reliability, performance, and scalability. The proposed architecture evaluation is according to the Architecture Tradeoff Analysis Method (ATAM) as a scenario-based technique. Compared with previous works, various scenarios, and more quality attributes are discussed within this evaluation, in addition to analyzing and predicting workload and the presence prediction of dynamic resources.

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Data availability

The datasets analyzed during the current study are available online with open-source access in [48].

References

  1. Foster I et al (2008) Cloud computing and grid computing 360-degree compared. In: 2008 grid computing environments workshop

  2. Rodriguez MA, Buyya R (2017) Scientific workflow management system for clouds. In: Software architecture for big data and the cloud

  3. Zhao Y et al (2007) Swift: fast, reliable, loosely coupled parallel computation. In: 2007 IEEE Congress on Services (Services 2007)

  4. Zhao Y et al (2015) Enabling scalable scientific workflow management in the Cloud. Future Generation Computer Systems. 46

  5. Coutinho EF et al (2015) Elasticity in cloud computing: a survey. Annals of telecommunications-annales des télécommunications 70:280–309

    Google Scholar 

  6. Rimal BP, Maier M (2016) Workflow scheduling in multi-tenant cloud computing environments. IEEE Trans Parallel Distrib Syst 28(1):290–304

    Article  Google Scholar 

  7. Hu P et al (2017) Survey on fog computing: architecture, key technologies, applications and open issues. J Netw Comput Appl 98:27–42

    Article  Google Scholar 

  8. Deelman E et al (2016) Pegasus in the cloud: science automation through workflow technologies. IEEE Internet Comput 20(1):700–76

    Article  Google Scholar 

  9. Adhikari M et al (2019) Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud. J Netw Comput Appl 128:64–77

    Article  Google Scholar 

  10. Golchi MM et al (2019) A hybrid of firefly and improved particle swarm optimization algorithms for load balancing in cloud environments: performance evaluation. Comput Netw 162:106860

    Article  Google Scholar 

  11. Ningning S et al (2016) Fog computing dynamic load balancing mechanism based on graph repartitioning. China Commun 13(3):156–164

    Article  Google Scholar 

  12. Puthal D et al (2018) Secure and sustainable load balancing of edge data centers in fog computing. IEEE Commun Mag 56(5):60–65

    Article  Google Scholar 

  13. Xu X et al (2018) Dynamic resource allocation for load balancing in fog environment. Wirel Commun Mob Comput

  14. Ralha CG et al (2019) Multiagent system for dynamic resource provisioning in cloud computing platforms. Future Gen Comput Syst 94:80–96

    Article  Google Scholar 

  15. Zhan ZH et al (2015) Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput Surv (CSUR) 47(4):1–33

    Article  Google Scholar 

  16. Singh S, Chana I (2015) QoS-aware autonomic resource management in cloud computing: a systematic review. ACM Comput Surv (CSUR) 48(3):1–46

    Article  Google Scholar 

  17. Wen Z et al (2016) Dynamically partitioning workflow over federated clouds for optimising the monetary cost and handling run-time failures. IEEE Trans Cloud Comput 8(4):1093–1107

    Article  Google Scholar 

  18. Poola D et al (2017) A taxonomy and survey of fault-tolerant workflow management systems in cloud and distributed computing environments. Softw Arch Big Data Cloud. https://doi.org/10.1016/B978-0-12-805467-3.00015-6

    Article  Google Scholar 

  19. Qin J, Fahringer T (2012) Scientific workflows: programming, optimization, and synthesis with ASKALON and AWDL

  20. Balis B (2016) HyperFlow: a model of computation, programming approach and enactment engine for complex distributed workflows. Future Gen Comput Syst 55:147–162

    Article  Google Scholar 

  21. Deelman E et al (2019) The evolution of the pegasus workflow management software. Comput Sci Eng 21(4):22–36

    Article  Google Scholar 

  22. Altintas I et al (2004) Kepler: an extensible system for design and execution of scientific workflows. In: Proceedings of 16th international conference on scientific and statistical database management

  23. Goecks J et al (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol 11:1–13

    Article  Google Scholar 

  24. Dong B et al (2008) Web service-oriented manufacturing resource applications for networked product development. Adv Eng Inf 22(3):282–295

    Article  Google Scholar 

  25. Wolstencroft K et al (2013) The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucleic Acids Res 41(W1):W557–W561

    Article  Google Scholar 

  26. Kayabay K et al (2018) [WiP] A workflow and cloud based service-oriented architecture for distributed manufacturing in industry 4.0 context. In: 2018 IEEE 11th Conference on service-oriented computing and applications (SOCA)

  27. Pierce M et al (2014) Apache Airavata: design and directions of a science gateway framework. In: 2014 6th international workshop on science gateways, pp 48–54

  28. Merchant N et al (2016) The iPlant collaborative: cyberinfrastructure for enabling data to discovery for the life sciences. PLoS Biol 14(1):e1002342

    Article  Google Scholar 

  29. Atkinson M et al (2017) Scientific workflows: past, present and future. Future Gen Comput Syst 75:216–227

    Article  Google Scholar 

  30. Kaur M et al (2021) Focalb: fog computing architecture of load balancing for scientific workflow applications. J Grid Comput 19(4):40

    Article  Google Scholar 

  31. Davami F et al (2022) Fog-based architecture for scheduling multiple workflows with high availability requirement. Computing

  32. Javadpour A et al (2023) An energy-optimized embedded load balancing using DVFS computing in cloud data centers. Comput Commun 197:255–266

    Article  Google Scholar 

  33. Neema G et al (2023) Multi-objective load balancing in cloud infrastructure through fuzzy based decision making and genetic algorithm based optimization. IAES Int J Artif Intell 12(2):678

    Google Scholar 

  34. Kruchten PB (1995) The 4+ 1 view model of architecture. IEEE Softw 12(6):42–50

    Article  Google Scholar 

  35. Barbacci MR (2003) Software quality attributes and architecture tradeoffs. Carnegie Mellon University, Software Engineering Institute: Pittsburgh

  36. Berander P et al (2005) Software quality attributes and trade-offs. Blekinge Inst Technol 97(98):19

    Google Scholar 

  37. Meng S et al (2010) The “4+ 1 “view model on safe home system architecture. In: 2010 IEEE international conference on software engineering and service sciences, pp 352–355

  38. White SA, Miers D (2008) BPMN modeling and reference guide: understanding and using BPMN

  39. Ghasemi F (2019) Structural and behavioral reference model for IoT-based elderly health-care systems in smart home. Int J Commun Syst 32(12):e4002

    Article  Google Scholar 

  40. Bass L et al (2003) Software architecture in practice

  41. Maheshwari P et al (2005) Supporting ATAM with a collaborative Web-based software architecture evaluation tool. Sci Comput Program 57(1):109–128

    Article  MathSciNet  Google Scholar 

  42. Kazman R et al (2000) ATAM: method for architecture evaluation

  43. Lee J, et al (2009) Analysis of VAN-core system architecture-a case study of applying the ATAM. In: 2009 10th ACIS international conference

  44. Velociraptor simulator https://github.com/simulatie-oplossingen/Velociraptor

  45. Batista E et al (2022) Load balancing between fog and cloud in fog of things based platforms through software-defined networking. J King Saud Univ Comput Inf Sci 34(9):711–725

    Google Scholar 

  46. Kanbar AB et al (2022) Region aware dynamic task scheduling and resource virtualization for load balancing in IoT-fog multi-cloud environment. Future Gen Comput Syst 137:70–86

    Article  Google Scholar 

  47. Hajvali M et al (2023) Decentralized and scalable hybrid scheduling-clustering method for real-time applications in volatile and dynamic fog-cloud environments. J Cloud Comput 12(1):66

    Article  Google Scholar 

  48. Rezaee et al (2022) IoT nodes movement and job requests (Version 1) [Data set]. Zenodo

  49. Juve G et al (2013) Characterizing and profiling scientific workflows. Future Gen Comput Syst 29(3):682–692

    Article  Google Scholar 

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Funding

No funds, grants, or other support was received.

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Author notes

  1. Zari Shamsa, Sahar Adabi and Amir Masoud Rahmani have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Zari Shamsa, Ali Rezaee & Amir Masoud Rahmani

  2. Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran

    Sahar Adabi

Authors
  1. Zari Shamsa
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  2. Ali Rezaee
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  3. Sahar Adabi
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  4. Amir Masoud Rahmani
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The authors contributed equally to this work.

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Correspondence to Ali Rezaee.

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Shamsa, Z., Rezaee, A., Adabi, S. et al. A decentralized prediction-based workflow load balancing architecture for cloud/fog/IoT environments. Computing 106, 201–239 (2024). https://doi.org/10.1007/s00607-023-01216-3

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  • DOI: https://doi.org/10.1007/s00607-023-01216-3

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