Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Optimal Cloudlet Selection in Edge Computing for Resource Allocation

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Mobile and Edge Computing devices have limited resources to perform computationally intensive jobs, and hence, there is a need for task offloading. In Mobile Cloud Computing, cloud servers are placed far from the user devices; as a consequence, many challenges are faced, such as security, limited bandwidth, network latency, and storage. Whereas edge servers are placed near the user devices in Edge Cloud Computing; however, issues of Cloud computing are also faced in Edge computing due to the huge number of devices, which also generates a significant load on edge servers. Some resource optimization approaches help in achieving optimal Cloudlet selection at the edge servers. When users access edge resources, such as CPU, memory, and hard disk, load balancing helps in distributing tasks among edge servers and achieving efficient results. The user devices communicate either within a Cloudlet or between Cloudlets using resource sharing, in which one of the main issues is optimal Cloudlet selection. This paper presents an optimal Cloudlet selection algorithm in which, first of all, an index value for each resource is calculated using parameters like weight, cluster of Cloudlets, availability, and total resource usage. Thereafter, the resource level and available resources of this level are calculated for each Cloudlet. Finally, an algorithm is proposed to help in finding the optimal Cloudlet for the cloud broker. The proposed approach is implemented in Cloud-Sim. The simulation results have shown efficiency of the proposed approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Radouane B, Lyamine G, Ahmed K, Kamel B. Scalable mobile computing: from cloud computing to mobile edge computing. In: 2022 5th international conference on networking, information systems and security: envisage intelligent systems in 5g//6G-based interconnected digital worlds (NISS), 2022. pp. 1–6. https://doi.org/10.1109/NISS55057.2022.10085600.

  2. Liu F, Tong J, Mao J, Bohn R, Messina J, Badger L, Leaf D, et al. Nist cloud computing reference architecture. NIST Spec Publ. 2011;500(2011):1–28.

    Google Scholar 

  3. Patidar S, Rane D, Jain P. A survey paper on cloud computing. In: 2012 second international conference on advanced computing & communication technologies, 2012. pp. 394–398. https://doi.org/10.1109/ACCT.2012.15.

  4. Nayyer MZ, Raza I, Hussain SA. Revisiting vm performance and optimization challenges for big data. In: Advances in computers, vol. 114. Elsevier; 2019. pp. 71–112.

  5. Nayyer MZ, Raza I, Hussain SA. A survey of cloudlet-based mobile augmentation approaches for resource optimization. ACM Comput Surv (CSUR). 2018;51(5):1–28.

    Article  Google Scholar 

  6. Dolui K, Datta SK. Comparison of edge computing implementations: fog computing, cloudlet and mobile edge computing. In: 2017 global internet of things summit (GIoTS), 2017. IEEE. pp. 1–6.

  7. Mahmoudi C, Mourlin F, Battou A. Formal definition of edge computing: an emphasis on mobile cloud and iot composition. In: 2018 third international conference on fog and mobile edge computing (FMEC), 2018. IEEE. pp. 34–42.

  8. Voorsluys W, Broberg J, Venugopal S, Buyya R. Cost of virtual machine live migration in clouds: a performance evaluation. In: Cloud computing: first international conference, CloudCom 2009, Beijing, China, December 1–4, 2009. Proceedings 1, 2009. Springer. pp. 254–65.

  9. Gritto D, Muthulakshmi P. Scheduling cloudlets in a cloud computing environment: a priority-based cloudlet scheduling algorithm (pbcsa). In: 2022 11th international conference on system modeling & advancement in research trends (SMART), 2022. pp. 80–86. https://doi.org/10.1109/SMART55829.2022.10047622.

  10. Khan KA, Wang Q, Grecos C, Luo C, Wang X. Meshcloud: integrated cloudlet and wireless mesh network for real-time applications. In: 2013 IEEE 20th international conference on electronics, circuits, and systems (ICECS), 2013. IEEE. pp. 317–20.

  11. Rawadi J, Artail H, Safa H. Providing local cloud services to mobile devices with inter-cloudlet communication. In: MELECON 2014–2014 17th IEEE Mediterranean electrotechnical conference, 2014. IEEE. pp. 134–38.

  12. Baktir AC, Ozgovde A, Ersoy C. How can edge computing benefit from software-defined networking: a survey, use cases, and future directions. IEEE Commun Surv Tutor. 2017;19(4):2359–91. https://doi.org/10.1109/COMST.2017.2717482.

    Article  Google Scholar 

  13. Jararweh Y, Ababneh F, Khreishah A, Dosari F, et al. Scalable cloudlet-based mobile computing model. Procedia Comput Sci. 2014;34:434–41.

    Article  Google Scholar 

  14. Verma A, Pattanaik K. Failure detector of perfect p class for synchronous hierarchical distributed systems. Int J Distrib Syst Technol. 2016;7(2):57–74.

    Article  Google Scholar 

  15. Chaurasia B, Verma A. A comprehensive study on failure detectors of distributed systems. J Sci Res. 2020;64(2):250–60.

    Google Scholar 

  16. Verma A, Singh M, Pattanaik KK. Failure detectors of strong s and perfect p classes for time synchronous hierarchical distributed systems. In: Applying integration techniques and methods in distributed systems and technologies, 2019. IGI Global. pp. 246–80.

  17. Chaurasia B, Verma A, Verma P. Heartbeat-based failure detector of perfect p class for synchronous hierarchical distributed systems. In: Research advances in intelligent computing, 2023. CRC Press. pp. 293–310.

  18. Krishna Nayak R, Srinivasarao G. A greedy load balancing strategy with optimal constraints for edge computing in industrial cloud environment. In: Innovations in computer science and engineering: Proceedings of the ninth ICICSE, 2021, 2022. Springer. pp. 31–8.

  19. Moon S, Lim Y. Task migration with partitioning for load balancing in collaborative edge computing. Appl Sci. 2022;12(3):1168.

    Article  Google Scholar 

  20. Satyanarayanan M, Bahl P, Caceres R, Davies N. The case for vm-based cloudlets in mobile computing. IEEE Pervas Comput. 2009;8(4):14–23.

    Article  Google Scholar 

  21. Lin Z, Liu J, Xiao J, Zi S. A survey: resource allocation technology based on edge computing in iiot. In: 2020 international conference on communications, computing, cybersecurity, and informatics (CCCI), 2020. pp. 1–5. https://doi.org/10.1109/CCCI49893.2020.9256663.

  22. Agarwal DA, Jain S. Efficient optimal algorithm of task scheduling in cloud computing environment. 2014. arXiv:1404.2076.

  23. Mukherjee A, De D, Roy DG. A power and latency aware cloudlet selection strategy for multi-cloudlet environment. IEEE Trans Cloud Comput. 2019;7(1):141–54. https://doi.org/10.1109/TCC.2016.2586061.

    Article  Google Scholar 

  24. Kaur S, Verma A. An efficient approach to genetic algorithm for task scheduling in cloud computing environment. Int J Inf Technol Comput Sci. 2012;4(10):74–9.

    Google Scholar 

  25. Sadiku MN, Musa SM, Momoh OD. Cloud computing: opportunities and challenges. IEEE Potent. 2014;33(1):34–6.

    Article  Google Scholar 

  26. Mahajan K, Dahiya D. Cloudanalyzer: a cloud based deployment framework for service broker and vm load balancing policies

  27. Mishra M, Das A, Kulkarni P, Sahoo A. Dynamic resource management using virtual machine migrations. IEEE Commun Mag. 2012;50(9):34–40.

    Article  Google Scholar 

  28. Parmar D, Kumar AS, Nivangune A, Joshi P, Rao UP. Discovery and selection mechanism of cloudlets in a decentralized mcc environment. In: 2016 IEEE/ACM international conference on mobile software engineering and systems (MOBILESoft), 2016. pp. 15–6. https://doi.org/10.1145/2897073.2897114.

  29. Rudra B, Verma A, Verma S, Shrestha B. Futuristic research trends and applications of internet of things. Boca Raton: CRC Press; 2022.

    Book  Google Scholar 

  30. Srivastava S, Verma A, Verma P. Fundamentals of internet of things. In: Futuristic research trends and applications of internet of things. CRC Press; 2022. pp. 1–30.

  31. Verma A, Verma P, Farhaoui Y, Lv Z. Emerging real-world applications of internet of things. Boca Raton: CRC Press; 2022.

    Book  Google Scholar 

  32. Liu H, Li S, Sun W. Resource allocation for edge computing without using cloud center in the smart home environment: a pricing approach. Sensors. 2020;20(22):6545.

    Article  Google Scholar 

  33. Zhu A, Wen Y. An efficient resource management optimization scheme for internet of vehicles in edge computing environment. Comput Intell Neurosci. 2022;2022.

  34. Verma A, Srivastava DA. Integrated routing protocol for opportunistic networks. 2012. arXiv:1204.1658.

  35. Verma A, Pattanaik K, Ingavale A. Context-based routing protocols for oppnets. Routing in opportunistic networks. New York: Springer; 2013. p. 69–97.

    Book  Google Scholar 

  36. Verma A, Verma P, Dhurandher SK, Woungang I. Opportunistic networks: fundamentals, applications and emerging trends. New York: CRC Press; 2021.

    Book  Google Scholar 

  37. Verma A, Singh M, Pattanaik K, Singh B. Future networks inspired by opportunistic networks. In: Opportunistic networks. Chapman and Hall; 2018. pp. 230–46.

  38. Verma A, Pattanaik K. Routing protocols in opportunistic networks. In: Opportunistic networking. Boca Raton: CRC Press; 2017. pp. 123–66.

  39. Singh M, Verma P, Verma A. Security in opportunistic networks. In: Opportunistic networks. Boca Raton: CRC Press; 2021. pp. 299–312.

  40. Singh M, Verma A, Verma P. Empirical analysis of the performance of routing protocols in opportunistic networks. In: Research advances in network technologies. Boca Raton: CRC Press; 2023. pp. 257–72.

  41. Gond MK, Singh M, Verma A, Verma P. Average time based prophet routing protocol for opportunistic networks. In: International conference on advanced network technologies and intelligent computing. Berlin: Springer; 2022. pp. 400–12.

Download references

Funding

This research work is funded by ‘Seed Grant to Faculty Members under IoE Scheme (under Dev. Scheme No. 6031)”.

Author information

Authors and Affiliations

  1. Department of Computer Science, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India

    Bablu Kumar, Mohini Singh & Anshul Verma

  2. TIH, Indian Institute of Technology, Patna, Bihar, 801106, India

    Pradeepika Verma

Authors
  1. Bablu Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohini Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anshul Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pradeepika Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bablu Kumar.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

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

This article is part of the topical collection “Machine Intelligence and Smart Systems” guest edited by Manish Gupta and Shikha Agrawal.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, B., Singh, M., Verma, A. et al. Optimal Cloudlet Selection in Edge Computing for Resource Allocation. SN COMPUT. SCI. 4, 745 (2023). https://doi.org/10.1007/s42979-023-02187-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02187-0

Keywords

Search

Navigation