Skip to main content
SpringerLink
Log in

A Mixed-integer programming model using particle swarm optimization algorithm for resource discovery in the cloudiot

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Cloud computing and the Internet of Things (IoT) are new platforms in the information and communication technology revolution. Selecting Cloud of Things (CloudIoT) in applications with fixed and mobile resources can provide many opportunities in different technologies, such as healthcare and transportation. Discovering fixed and mobile resources are one of the main concerns of the CloudIoT paradigm that requires a proper discovery mechanism. This paper proposes a mathematical optimization model to minimize response time, cost, and bandwidth of CloudIoT platforms by considering fixed and mobile resources in resource discovery. Moreover, a heuristic Single Resource Discovery algorithm is presented based on a Mathematical optimization model (SRDM). Furthermore, a heuristic Multi Resource Discovery algorithm is introduced based on a Mathematical optimization model (MRDM). In addition, this paper employs Particle Swarm Optimization (PSO) and Multi-Objective Particle Swarm Optimization (MOPSO) to solve the optimization problem. Finally, according to the simulation results, the proposed MOPSO-based algorithm significantly reduces the latency and improves the success ratio and availability compared to other algorithms.

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
Fig. 8
Fig. 9

Similar content being viewed by others

Availability of data and material

Not applicable.

References

  1. Nawaz F, Hussain O, Hussain FK, Janjua NK, Saberi M, Chang E (2019) Proactive management of SLA violations by capturing relevant external events in a Cloud of Things environment. Futur Gener Comput Syst 95:26–44

    Article  Google Scholar 

  2. Xavier TC, Santos IL, Delicato FC, Pires PF, Alves MP, Calmon TS, Amorim CL (2020) Collaborative resource allocation for Cloud of Things systems. J Netw Comput Appl 159:102592

    Article  Google Scholar 

  3. Tian Y, Kaleemullah MM, Rodhaan MA, Song B, Al-Dhelaan A, Ma T (2019) A privacy preserving location service for cloud-of-things system. J Parallel Distrib Comput 123:215–222

    Article  Google Scholar 

  4. Li Z, Yang Z, Xie S (2019) Computing resource trading for edge-cloud-assisted Internet of Things. IEEE Trans Industr Inf 15(6):3661–3669

    Article  Google Scholar 

  5. Elazhary H (2019) Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions. J Netw Comput Appl 128:105–140

    Article  Google Scholar 

  6. Darwish A, Hassanien AE, Elhoseny M, Sangaiah AK, Muhammad K (2019) The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. J Ambient Intell Humaniz Comput 10(10):4151–4166

    Article  Google Scholar 

  7. Munir A, Kansakar P, Khan SU (2017) IFCIoT: Integrated Fog Cloud IoT: A novel architectural paradigm for the future Internet of Things. IEEE Consum Electron Mag 6(3):74–82

    Article  Google Scholar 

  8. Wang SC, Tseng SC, Yan KQ, Tsai YT (2018) Reaching agreement in an integrated fog cloud IoT. IEEE Access 6:64515–64524

    Article  Google Scholar 

  9. Jiang Y, Huang Z, Tsang DH (2017) Challenges and solutions in fog computing orchestration. IEEE Network 32(3):122–129

    Article  Google Scholar 

  10. Kochar V, Sarkar A (2016) Real time resource allocation on a dynamic two level symbiotic fog architecture. In 2016 Sixth International Symposium on Embedded Computing and System Design (ISED) (pp. 49–55). IEEE

  11. Mseddi A, Jaafar W, Elbiaze H, Ajib W (2019) Joint container placement and task provisioning in dynamic fog computing. IEEE Internet Things J 6(6):10028–10040

    Article  Google Scholar 

  12. Zhang F, Ge J, Li Z, Li C, Huang Z, Kong L, Luo B (2017) Task Offloading for Scientific Workflow Application in Mobile Cloud. In IoTBDS (pp. 136–148)

  13. Kozyrev D, Ometov A, Moltchanov D, Rykov V, Efrosinin D, Milovanova T, Koucheryavy Y (2018) Mobility-centric analysis of communication offloading for heterogeneous Internet of Things devices. Wirel Commun Mob Comput

  14. Guo K, Yang M, Zhang Y, Cao J (2019) Joint computation offloading and bandwidth assignment in cloud-assisted edge computing. IEEE Trans Cloud Comput

  15. Abdel-Basset M, Abdle-Fatah L, Sangaiah AK (2019) An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Clust Comput 22(4):8319–8334

    Article  Google Scholar 

  16. Alli AA, Alam MM (2019) SecOFF-FCIoT: Machine learning based secure offloading in Fog-Cloud of things for smart city applications. Internet Things 7:100070

    Article  Google Scholar 

  17. Zhang Q, Liang H, Xing Y (2014) A parallel task scheduling algorithm based on fuzzy clustering in cloud computing environment. Int J Mach Learn Comput 4(5):437

    Article  Google Scholar 

  18. Lin W, Liang C, Wang JZ, Buyya R (2014) Bandwidth‐aware divisible task scheduling for cloud computing. Softw Pract Exp 44(2):163–174

  19. Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279

    Article  Google Scholar 

  20. Ding S, Chen C, Xin B, Pardalos PM (2018) A bi-objective load balancing model in a distributed simulation system using NSGA-II and MOPSO approaches. Appl Soft Comput 63:249–267

    Article  Google Scholar 

  21. Gill SS, Garraghan P, Buyya R (2019) ROUTER: Fog enabled cloud based intelligent resource management approach for smart home IoT devices. J Syst Softw 154:125–138

    Article  Google Scholar 

  22. Bharti M, Kumar R, Saxena S (2018) Clustering-based resource discovery on Internet-of-Things. Int J Commun Syst 31(5):e3501

    Article  Google Scholar 

  23. Ezugwu AE, Adewumi AO (2017) Soft sets based symbiotic organisms search algorithm for resource discovery in cloud computing environment. Futur Gener Comput Syst 76:33–50

    Article  Google Scholar 

  24. Panwar N, Negi S, Rauthan MMS, Vaisla KS (2019) TOPSIS–PSO inspired non-preemptive tasks scheduling algorithm in cloud environment. Clust Comput 22(4):1379–1396

    Article  Google Scholar 

  25. Reddy MPK, Babu MR (2019) Implementing self adaptiveness in whale optimization for cluster head section in Internet of Things. Clust Comput 22(1):1361–1372

    Article  Google Scholar 

  26. AlZubi A, Alarifi A, Al-Maitah M, Albasheer OA (2020) Location assisted delay-less service discovery method for IoT environments. Comput Commun 150:405–412

    Article  Google Scholar 

  27. Kalaiselvi S, Selvi CK (2020) Hybrid cloud resource provisioning (HCRP) algorithm for optimal resource allocation using MKFCM and bat algorithm. Wirel Pers Commun 111(2):1171–1185

    Article  Google Scholar 

  28. Skarlat O, Karagiannis V, Rausch T, Bachmann K, Schulte S (2018) A framework for optimization, service placement, and runtime operation in the fog. In 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC) (pp. 164–173). IEEE

  29. Nunes LH, Estrella JC, Perera C, Reiff-Marganiec S, Delbem AC (2018) The elimination-selection based algorithm for efficient resource discovery in Internet of Things environments. In 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC) (pp. 1–7). IEEE

  30. Md AQ, Varadarajan V, Mandal K (2019) Efficient algorithm for identification and cache based discovery of cloud services. Mob Netw Appl 24(4):1181–1197

    Article  Google Scholar 

  31. Abdi S, PourKarimi L, Ahmadi M, Zargari F (2017) Cost minimization for deadline-constrained bag-of-tasks applications in federated hybrid clouds. Futur Gener Comput Syst 71:113–128

    Article  Google Scholar 

  32. Kalantary S, Akbari Torkestani J, Shahidinejad A (2021) Resource discovery in the Internet of Things integrated with fog computing using Markov learning model. J Supercomput 1–22

  33. Bharti M, Jindal H (2021) Optimized clustering-based discovery framework on Internet of Things. J Supercomput 77(2):1739–1778

    Article  Google Scholar 

  34. Xu L, Zhou X, Tao Ye, Lei Liu XuYu, Kumar N (2021) Intelligent Security Performance Prediction for IoT-Enabled Healthcare Networks Using an Improved CNN. IEEE Trans Industr Inf 18(3):2063–2074

    Article  Google Scholar 

  35. Liu Y, Zhang W, Zhang Q, Norouzi M (2021) An optimized human resource management model for cloud-edge computing in the internet of things. Cluster Comput 1–13

  36. Murturi I, Dustdar S (2021) A decentralized approach for resource discovery using metadata replication in edge networks. IEEE Trans Serv Comput

  37. Chang CT (2011) Multi-choice goal programming with utility functions. Eur J Oper Res 215(2):439–445

    Article  MathSciNet  Google Scholar 

  38. Varshney P, Simmhan Y (2017) Demystifying fog computing: Characterizing architectures, applications and abstractions. In 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC) (pp. 115–124). IEEE

  39. Helsley M (2009) LXC: Linux container tools. IBM DevloperWorks Technical Library 11

  40. Merkel D (2014) Docker: lightweight linux containers for consistent development and deployment. Linux J 2014(239):2

    Google Scholar 

  41. Yannuzzi M, Milito R, Serral-Gracià R, Montero D, Nemirovsky M (2014) Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing. In 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) (pp. 325–329). IEEE

  42. Openstack. http://www.openstack.org. Accessed 13 Oct 2018

  43. OpenNebula.org. http://www.opennebula.org. Accessed 22 Jan 2021

  44. Eucalyptus. https://www.eucalyptus.cloud/. Accessed 17 Apr 2020

  45. Taguchi G, Chowdhury S, Wu Y (2005) Taguchi's quality engineering handbook. Wiley Publishing

  46. Zitzler E, Thiele L (1998) Multi-objective optimization using evolutionary algorithms—a comparative case study. In International conference on parallel problem solving from nature (pp. 292–301). Springer, Berlin, Heidelberg

  47. Schott JR (1995) Fault tolerant design using single and multicriteria genetic algorithm optimization (Doctoral dissertation, Massachusetts Institute of Technology)

  48. Zhang Q, Li H (2007) MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  Google Scholar 

  49. Yen GG, He Z (2013) Performance metric ensemble for multi-objective evolutionary algorithms. IEEE Trans Evol Comput 18(1):131–144

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran

    Parisa Goudarzi, Amir Masoud Rahmani & Mohammad Mosleh

  2. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan

    Amir Masoud Rahmani

Authors
  1. Parisa Goudarzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir Masoud Rahmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Mosleh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed equally to this manuscript.

Corresponding author

Correspondence to Amir Masoud Rahmani.

Ethics declarations

Competing interests

There is no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Goudarzi, P., Rahmani, A.M. & Mosleh, M. A Mixed-integer programming model using particle swarm optimization algorithm for resource discovery in the cloudiot. Peer-to-Peer Netw. Appl. 15, 2326–2346 (2022). https://doi.org/10.1007/s12083-022-01349-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-022-01349-w

Keywords

Search

Navigation