Skip to content
2000
Volume 18, Issue 9
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Background

Integration of cloud components in a wireless mesh network of Wireless-Optical Broadband Access Network (WOBAN) contributes to enhancing network performance.

Aim

This study aims to deploy the minimum cloudlets at the optimum location and to offload excess traffic from overloaded cloudlets to underloaded ones.

Objective

The objective of this study is to optimize cloudlet positioning and traffic offloading for cost-effective deployment, better resource utilization, reduced blocking probability, and lower delays.

Methods

The proposed methodology introduces a Cluster-Based Heuristic Approach (CBHA) for efficient cloudlet placement along with a traffic offloading mechanism using a Customized Donkey-Smuggler Optimization (CDSO) to enhance the overall network performance.

Results

Simulations show the effectiveness of the proposed approach for resource utilization, blocking probability, delay, and cost.

Conclusion

The problem in position optimization of the cloudlet, along with traffic offloading, is solved using the proposed approaches to get better network performance in a cost-efficient manner.

Loading

Article metrics loading...

/content/journals/raeeng/10.2174/0123520965335968241120092223
2025-01-06
2026-02-07
Loading full text...

Full text loading...

References

  1. GhazisaidiN. MaierM. Fiber-wireless (FiWi) access networks: Challenges and opportunitiesNetwrk. Mag. of Global Internetwkg. 20113642
    [Google Scholar]
  2. SarkarS. DixitS. MukherjeeB. Hybrid wireless-optical broadband-access network (WOBAN): A review of relevant challenges.J. Lightwave Technol.200725113329334010.1109/JLT.2007.906804
    [Google Scholar]
  3. MaierM. Fiber-wireless (FiWi) Broadband access networks in an age of convergence: Past, present, and future.Adv. Optics2014201412310.1155/2014/945364
    [Google Scholar]
  4. MohammadaniK.H. ButtR.A. MemonK.A. PirzadoA.A. FaheemM. AbroA. AliB. AinN. A QoS provisioning architecture of fiber wireless network based on XGPON and IEEE 802.11ac.J. Opt. Commun.202444s1s1017s102210.1515/joc‑2020‑0230
    [Google Scholar]
  5. ChouhanN. BhattU.R. UpadhyayR. An optimization framework for FiWi access network: Comprehensive solution for green and survivable deployment.Opt. Fiber Technol.20195310200210.1016/j.yofte.2019.102002
    [Google Scholar]
  6. ChouhanN. BhattU.R. UpadhyayR. Performance evaluation of fiber wireless (FiWi) access network using position optimization of ONUs.Int. J. Sensors Wirel. Commun. Control20201010.2174/2210327910666200304131411
    [Google Scholar]
  7. ChouhanN. Rathore BhattU. UpadhyayR. BhatV. FiWi network planning for WiFi enabled gram panchayats of India: A frame work using component placement optimization.Opt. Fiber Technol.20237610324210.1016/j.yofte.2023.103242
    [Google Scholar]
  8. EmamiH. PashazadehS. Positioning multiple optical network units in fiber-wireless networks: An efficient hybrid K -harmonic means clustering approach.Opt. Fiber Technol.20248410375910.1016/j.yofte.2024.103759
    [Google Scholar]
  9. EmamiH. PashazadehS. BalafarM.A. A novel fuzzy-based algorithm for ONU placement in FiWi broadband access network.Opt. Fiber Technol.20238010341410.1016/j.yofte.2023.103414
    [Google Scholar]
  10. LimC. TianY. RanaweeraC. NirmalathasT.A. WongE. LeeK.L. Evolution of radio-over-fiber technology.J. Lightwave Technol.20193761647165610.1109/JLT.2018.2876722
    [Google Scholar]
  11. BhattU.R. ChouhanN. UpadhyayR. Hybrid algorithm: A cost efficient solution for ONU placement in Fiber-Wireless (FiWi) network.Opt. Fiber Technol.201522768310.1016/j.yofte.2015.01.010
    [Google Scholar]
  12. BhattU.R. ChouhanN. ONU placement in Fiber-Wireless (FiWi) Networks.2013 Nirma University International Conference on Engineering (NUiCONE), Ahmedabad, India, 28-30 November 2013, pp. 1-610.1109/NUiCONE.2013.6780115
    [Google Scholar]
  13. BarberaM.V. KostaS. MeiA. StefaJ. To offload or not to offload? The bandwidth and energy costs of mobile cloud computing.IEEE INFOCOM Turin, Italy, 2013, pp. 1285-129310.1109/INFCOM.2013.6566921
    [Google Scholar]
  14. MuñozO. Pascual-IserteA. VidalJ. Optimization of radio and computational resources for energy efficiency in latency-constrained application offloading.IEEE Trans. Vehicular Technol.201564104738475510.1109/TVT.2014.2372852
    [Google Scholar]
  15. VermaM. BhattU.R. UpadhyayR. Building a cloud-integrated WOBAN with optimal coverage and deployment costAdvances in Computing and Network CommunicationsSpringer2021119131Singapore10.1007/978‑981‑33‑6977‑1_10
    [Google Scholar]
  16. ClinchS. HarkesJ. FridayA. DaviesN. SatyanarayananM. How close is close enough? Understanding the role of cloudlets in supporting display appropriation by mobile users.2012 IEEE International Conference on Pervasive Computing and Communications, Lugano, Switzerland, 19-23 March 2012, pp. 122-127.10.1109/PerCom.2012.6199858
    [Google Scholar]
  17. SatyanarayananM. BahlP. CaceresR. DaviesN. The case for VM-Based Cloudlets in mobile computing.IEEE Pervasive Comput.200984142310.1109/MPRV.2009.82
    [Google Scholar]
  18. VerbelenT. SimoensP. De TurckF. DhoedtB. Cloudlets: Bringing the cloud to the mobile user.Third ACM Workshop on Mobile Cloud Computing and Services New York, NY, USA, pp. 29–36. 10.1145/2307849.2307858
    [Google Scholar]
  19. VerbelenT. SimoensP. De TurckF. DhoedtB. Leveraging cloudlets for immersive collaborative applications.IEEE Pervasive Comput.2013124303810.1109/MPRV.2013.66
    [Google Scholar]
  20. CeselliA. PremoliM. SecciS. Mobile edge cloud network design optimization.IEEE/ACM Trans. Netw.20172531818183110.1109/TNET.2017.2652850
    [Google Scholar]
  21. ReazA. RamamurthiV. TornatoreM. MukherjeeB. Green provisioning of cloud services over wireless-optical broadband access networksIEEE Global Telecommunications Conference - GLOBECOM Houston, TX, USA, 2011, pp. 1-510.1109/GLOCOM.2011.6134394
    [Google Scholar]
  22. VermaM. BhattU.R. UpadhyayR. BhatV. Priority based task scheduling in cloud integrated WOBAN network.Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India, 18-19 February 2023, pp. 1-4.10.1109/SCEECS57921.2023.10063047
    [Google Scholar]
  23. BaoB. YangH. YaoQ. GuanL. ZhangJ. CherietM. Resource allocation with edge-cloud collaborative traffic prediction in integrated radio and optical networks.IEEE Access2023117067707710.1109/ACCESS.2023.3237257
    [Google Scholar]
  24. LiZ. ZhaoY. LiY. LiuM. ZengZ. XinX. WangF. LiX. ZhangJ. Self-optimizing optical network with cloud-edge collaboration: Architecture and application.IEEE Open J. Comput. Soc.2020122022910.1109/OJCS.2020.3030957
    [Google Scholar]
  25. PelleI. PaolucciF. SonkolyB. CuginiF. Latency-sensitive edge/cloud serverless dynamic deployment over telemetry-based packet-optical network.IEEE J. Sel. Areas Comm.20213992849286310.1109/JSAC.2021.3064655
    [Google Scholar]
  26. RimalB.P. VanD.P. MaierM. Mobile edge computing empowered fiber-wireless access networks in the 5G Era.IEEE Commun. Mag.201755219220010.1109/MCOM.2017.1600156CM
    [Google Scholar]
  27. ReazA.S. RamamurthiV. TornatoreM. MukherjeeB. Cloud-integrated WOBAN: An offloading-enabled architecture for service-oriented access networks.Comput. Netw.20146851910.1016/j.comnet.2013.12.003
    [Google Scholar]
  28. XuZ. LiangW. XuW. JiaM. GuoS. Capacitated cloudlet placements in wireless metropolitan area networksIEEE 40th Conference on Local Computer Networks (LCN), Clearwater Beach, FL, USA, 26-29 October 2015, pp. 570-578.10.1109/LCN.2015.7366372
    [Google Scholar]
  29. XuZ. LiangW. XuW. JiaM. GuoS. Efficient algorithms for capacitated cloudlet placements.IEEE Trans. Parallel Distrib. Syst.201627102866288010.1109/TPDS.2015.2510638
    [Google Scholar]
  30. ChenL. WuJ. ZhouG. MaL. QUICK: QoS-guaranteed efficient cloudlet placement in wireless metropolitan area networks.J. Supercomput.20187484037405910.1007/s11227‑018‑2412‑8
    [Google Scholar]
  31. MondalS. DasG. WongE. CCOMPASSION: A hybrid cloudlet placement framework over passive optical access networks. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, Honolulu, HI, USA, 16-19 April 2018, pp. 216-22410.1109/INFOCOM.2018.8485846
    [Google Scholar]
  32. MondalS. DasG. WongE. Efficient cost-optimization frameworks for hybrid cloudlet placement over fiber-wireless networks.J. Opt. Commun. Netw.201911843745110.1364/JOCN.11.000437
    [Google Scholar]
  33. SunX. AnsariN. Latency aware workload offloading in the cloudlet network.IEEE Commun. Lett.20172171481148410.1109/LCOMM.2017.2690678
    [Google Scholar]
  34. WangZ. GaoF. JinX. Optimal deployment of cloudlets based on cost and latency in internet of things networks.Wirel. Netw.20202686077609310.1007/s11276‑020‑02418‑9
    [Google Scholar]
  35. MahesarA.R. LakhanA. SajnaniD.K. JamaliI.A. Hybrid delay optimization and workload assignment in mobile edge cloud networks.OAlib20185911210.4236/oalib.1104854
    [Google Scholar]
  36. RimalB.P. Pham VanD. MaierM. Cloudlet enhanced fiber-wireless access networks for mobile-edge computing.IEEE Trans. Wirel. Commun.20171663601361810.1109/TWC.2017.2685578
    [Google Scholar]
  37. GuoH. LiuJ. Collaborative computation offloading for multiaccess edge computing over fiber-wireless networks.IEEE Trans. Vehicular Technol.20186754514452610.1109/TVT.2018.2790421
    [Google Scholar]
  38. LiH. LiuL. DuanX. LiH. ZhengP. TangL. Energy-efficient offloading based on hybrid bio-inspired algorithm for edge–cloud integrated computation.Sustain. Comput.20244210097210.1016/j.suscom.2024.100972
    [Google Scholar]
  39. JiaM. LiangW. XuZ. HuangM. MaY. QoS-aware cloudlet load balancing in wireless metropolitan area networks.IEEE Trans. Cloud Comput.20208262363410.1109/TCC.2017.2786738
    [Google Scholar]
  40. JiaM. CaoJ. LiangW. Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks.IEEE Trans. Cloud Comput.20175472573710.1109/TCC.2015.2449834
    [Google Scholar]
  41. MondalS. DasG. WongE. Computation offloading in optical access cloudlet networks: A game-theoretic approach.IEEE Commun. Lett.20182281564156710.1109/LCOMM.2018.2843327
    [Google Scholar]
  42. DaiQ. QianJ. QinG. LiJ. ZhaoJ. A latency-aware offloading strategy over fiber-wireless (FiWi) infrastructures for tactile internet services.Appl. Sci.20221213641710.3390/app12136417
    [Google Scholar]
  43. ChenB. LiuL. FanY. ShaoW. GaoM. ChenH. JuW. HoP-H. JueJ.P. ShenG. Low-latency partial resource offloading in cloud-edge elastic optical networks.J. Opt. Commun. Netw.202416214215810.1364/JOCN.500117
    [Google Scholar]
  44. MaqsoodT. uz ZamanS.K. QayyumA. RehmanF. MustafaS. ShujaJ. Adaptive thresholds for improved load balancing in mobile edge computing using K-means clustering.Telecomm. Syst.202486351953210.1007/s11235‑024‑01134‑5
    [Google Scholar]
  45. JiangW. Graph-based deep learning for communication networks: A survey.Comput. Commun.2022185405410.1016/j.comcom.2021.12.015
    [Google Scholar]
  46. JianpingW. GuangqiuQ. ChunmingW. WeiweiJ. JiaheJ. Federated learning for network attack detection using attention-based graph neural networks.Sci. Rep.20241411908810.1038/s41598‑024‑70032‑239154072
    [Google Scholar]
  47. FesehayeD. GaoY. NahrstedtK. WangG. Impact of cloudlets on interactive mobile cloud applicationsEEE 16th International Enterprise Distributed Object Computing Conference Beijing, China, 10-14 September 2012, pp. 123-132,10.1109/EDOC.2012.23
    [Google Scholar]
  48. NgC. H. Boon-HeeS. Queueing Modelling Fundamentals: With Applications in Communication NetworksWiley200810.1002/9780470994672
    [Google Scholar]
  49. ShamsaldinA.S. RashidT.A. Al-Rashid AghaR.A. Al-SalihiN.K. MohammadiM. Donkey and smuggler optimization algorithm: A collaborative working approach to path finding.J. Comput. Des. Eng.20196456258310.1016/j.jcde.2019.04.004
    [Google Scholar]
  50. Sai KalyanC.N. Srikanth GoudB. KishanH. RamineniP. KumarB.P. Anil KumarT. Donkey and smuggler optimization algorithm-based degree of freedom controller for stability of two area power system with AC-DC Links.IEEE 7th International Conference on Recent Advances and Innovations in Engineering (ICRAIE), MANGALORE, India, 01-03 December 2022, pp. 461-466.10.1109/ICRAIE56454.2022.10054318
    [Google Scholar]
  51. HasanN.M. RashidT.A. AlsadoonA. QosaeriA.S. AbualigahL. YaseenZ.M. An enhanced donkey and smuggler optimization algorithm for choosing the precise job applicant.Iran J. Comput. Sci.20236323324310.1007/s42044‑023‑00135‑y
    [Google Scholar]
  52. RajeswariG. ArthiR. MuruganK. Nature-inspired donkey and smuggler algorithm for optimal data gathering in partitioned wireless sensor networks for restoring network connectivity.Computing2024106375978710.1007/s00607‑023‑01251‑0
    [Google Scholar]
  53. AnithaN. PriyaD. BaskarC. DevisuryaV. An effective logistics network design using donkey-smugglers optimization (DSO) algorithm14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022) 2023, pp. 616–62310.1007/978‑3‑031‑27524‑1_59
    [Google Scholar]
  54. BehuraA. Optimized data transmission scheme based on proper channel coordination used in vehicular ad hoc networks.Int. J. Inf. Technol.20221421107111610.1007/s41870‑021‑00634‑w
    [Google Scholar]
  55. AlmekhlafiM. PrabuP. VenkatachalamK. AlluhaidanA.S. RadwaM. Covid-19 CT lung image segmentation using adaptive donkey and smuggler optimization algorithm.Comput. Mater. Continua2022711133115210.32604/cmc.2022.020919
    [Google Scholar]
  56. BhattU.R. ChouhanN. MarmatA.B. UpadhyayR. Deployment of cost-efficient cloud integrated WOBAN: A cluster- based approach.Int. J. Sensors Wirel. Commun. Control202111331432310.2174/2210327910666200224114030
    [Google Scholar]
/content/journals/raeeng/10.2174/0123520965335968241120092223
Loading
/content/journals/raeeng/10.2174/0123520965335968241120092223
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test