Skip to main content

Advertisement

SpringerLink
Log in

SDN-DVFS: an enhanced QoS-aware load-balancing method in software defined networks

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Recently, software defined networks (SDN) has been considered as a promising technology for improving the network performance. However, the load imbalance problem considerably reduces quality of service (QoS) level in SDNs. Traffic distribution in SDN affects the efficiency and creates other challenges like unbalanced load distribution which will significantly affect the network performance and traffic increase which leads to delay increase as well. To address this challenge, a novel method, named SDN-DVFS, has been proposed to fairly balance the traffic load on the servers and improve the QoS in the network. The proposed method deals with the load-balancing problem in SDNs based on the dynamic voltage frequency scaling (DVFS) in which it considers the overload of each virtual machine (VM), efficiency of the host machine, and the load applied by each user. This method relies on a dynamic traffic in which the on-demand requests arrive one by one without any prior knowledge of future arrivals. SDN-DVFS balances the traffic load over the network and improves the network resource utilization even if there is a large number of VMs in the network. Moreover, the proposed method reduces the synchronization cost between the data and controller layers which leads to the less response time. Regarding energy parameter, the average energy consumption in the proposed method is 1.53 kWh, which is 48.7% less than the number 2.99 recorded by similar method PSOAP. PSOAP considering two parameters of traffic release delay and controllers’ capacity as a particle in PSO algorithm adjusts them in SDN in a way that it can improve convergence accuracy and load balancing. Simulation results demonstrated the superiority of the proposed method in terms of the energy, latency, and packet delivery rate in comparison with similar recent methods.

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

source to destination in the proposed method

Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Notes

  1. Dynamic Frequency Scaling.

  2. Dynamic Voltage Scaling.

References

  1. Akyildiz, I.F., Lee, A., Wang, P., Luo, M., Chou, W.: A roadmap for traffic engineering in SDN-OpenFlow networks. Comput. Netw. 71, 1–30 (2014)

    Google Scholar 

  2. Akyildiz, I.F., Wang, P., Lin, S.-C.: SoftAir: a software defined networking architecture for 5G wireless systems. Comput. Netw. 85, 1–18 (2015)

    Google Scholar 

  3. Torkzadeh, S., Soltanizadeh, H., Orouji, A.A.: Energy-aware routing considering load balancing for SDN: a minimum graph-based ant colony optimization. Cluster Comput. 24, 2293 (2021)

    Google Scholar 

  4. Mishra, A., Gupta, N., Gupta, B.: Defense mechanisms against DDoS attack based on entropy in SDN-cloud using POX controller. Telecommun. Syst. 77(1), 47–62 (2021)

    Google Scholar 

  5. Bhushan, K., Gupta, B.B.: Distributed denial of service (DDoS) attack mitigation in software defined network (SDN)-based cloud computing environment. J. Ambient. Intell. Humaniz. Comput. 10(5), 1985–1997 (2019)

    Google Scholar 

  6. Rego, A., Garcia, L., Sendra, S., Lloret, J.: Software defined network-based control system for an efficient traffic management for emergency situations in smart cities. Future Gener. Comput. Syst. 88, 243–253 (2018)

    Google Scholar 

  7. Yang, Z., Yeung, K.L.: Sdn candidate selection in hybrid ip/sdn networks for single link failure protection. IEEE/ACM Trans. Netw. 28(1), 312–321 (2020)

    Google Scholar 

  8. Chen, Y.-T., Li, C.-Y., Wang, K.: A fast converging mechanism for load balancing among SDN multiple controllers. In: 2018 IEEE Symposium on Computers and Communications (ISCC), IEEE, pp. 00682–00687 (2018)

  9. Hochbaum, D.S.: Complexity and algorithms for nonlinear optimization problems. Ann. Oper. Res. 153(1), 257–296 (2007)

    MathSciNet  MATH  Google Scholar 

  10. Jain, S., et al.: B4: experience with a globally-deployed software defined WAN. In: ACM SIGCOMM Computer Communication Review, vol. 43, no. 4, pp. 3–14. ACM (2013)

  11. Yeganeh, S.H., Tootoonchian, A., Ganjali, Y.: On scalability of software-defined networking. IEEE Commun. Mag. 51(2), 136–141 (2013)

    Google Scholar 

  12. Qiu, C., Cui, S., Yao, H., Xu, F., Yu, F.R., Zhao, C.: A novel QoS-enabled load scheduling algorithm based on reinforcement learning in software-defined energy internet. Future Gener. Comput. Syst. 92, 43–51 (2019)

    Google Scholar 

  13. Xu, H., Li, X.-Y., Huang, L., Deng, H., Huang, H., Wang, H.: Incremental deployment and throughput maximization routing for a hybrid SDN. IEEE/ACM Trans. Netw. 25(3), 1861–1875 (2017)

    Google Scholar 

  14. Hazra, A., Adhikari, M., Amgoth, T., Srirama, S.N.: Joint computation offloading and scheduling optimization of IoT applications in fog networks. IEEE Trans. Netw. Sci. Eng. 7(4), 3266–3278 (2020)

    MathSciNet  Google Scholar 

  15. Wallner, R., Cannistra, R.: An SDN approach: quality of service using big switch’s floodlight open-source controller. Proc. Asia-Pac. Adv. Netw. 35(14–19), 10–7125 (2013)

    Google Scholar 

  16. Boero, L., Cello, M., Garibotto, C., Marchese, M., Mongelli, M.: BeaQoS: load balancing and deadline management of queues in an OpenFlow SDN switch. Comput. Netw. 106, 161–170 (2016)

    Google Scholar 

  17. Ahammad, I., Khan, M.A.R., Salehin, Z.U., Uddin, M., Soheli, S.J.: Improvement of QOS in an IoT ecosystem by integrating fog computing and SDN. Int. J. Cloud Appl. Comput. (IJCAC) 11(2), 48–66 (2021)

    Google Scholar 

  18. Zhong, H., Lin, Q., Cui, J., Shi, R., Liu, L.: An efficient SDN load balancing scheme based on variance analysis for massive mobile users. Mobile Inf. Syst. 2015, 1 (2015)

    Google Scholar 

  19. Shang, F., Mao, L., Gong, W.: Service-aware adaptive link load balancing mechanism for Software-Defined Networking. Future Gener. Comput. Syst. 81, 452–464 (2018)

    Google Scholar 

  20. Sahoo, K.S., et al.: ESMLB: efficient switch migration-based load balancing for multi-controller SDN in IoT,". IEEE Internet Things J. 7, 5852 (2019)

    Google Scholar 

  21. Pan, C., Shi, J., Yang, L., Kong, Z.: Satellite network load balancing strategy for SDN/NFV collaborative deployment. In: 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 1406–1411. IEEE (2019)

  22. Leaf-nosed bat. In: Encyclopædia Britannica. Encyclopædia Britannica Online (2009)

  23. Lin, C., Wang, K., Deng, G.: A QoS-aware routing in SDN hybrid networks. Procedia Comput. Sci. 110, 242–249 (2017)

    Google Scholar 

  24. Tootoonchian, A., Ganjali, Y; Hyperflow: a distributed control plane for openflow. In: Proceedings of the 2010 internet network management conference on Research on enterprise networking, vol. 3 (2010)

  25. Koponen, T., et al.: Onix: a distributed control platform for large-scale production networks. OSDI 10, 1–6 (2010)

    Google Scholar 

  26. Mann, V., Kannan, K., Vishnoi, A., Iyer, A.S.: Ncp: service replication in data centers through software defined networking. In: 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), pp. 561–567. IEEE (2013)

  27. Al-Mansoori, A., Abawajy, J., Chowdhury, M.: BDSP in the cloud: scheduling and load balancing utlizing SDN and CEP. In: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), pp. 827–835. IEEE (2020)

  28. Kim, W.-S., Chung, S.-H., Moon, J.-W.: Improved content management for information-centric networking in SDN-based wireless mesh network. Comput. Netw. 92, 316–329 (2015)

    Google Scholar 

  29. Alawadi, A.H., Molnár, S.: Risk analysis of blocked rate predictions for SDN load balancing using Monte Carlo simulation. In: 2019 IEEE Symposium on Computers and Communications (ISCC), pp. 1028–1033. IEEE (2019)

  30. Swarnakar, S., Bhattacharya, S., Banerjee, C.: A bio-inspired and heuristic-based hybrid algorithm for effective performance with load balancing in cloud environment. Int. J. Cloud Appl. Comput. (IJCAC) 11(4), 59–79 (2021)

    Google Scholar 

  31. Hassan, H.A., Salem, S.A., Saad, E.M.: A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment. Future Gener. Comput. Syst. 112, 431 (2020)

    Google Scholar 

  32. Buell, J., Hecht, D., Heo, J., Saladi, K., Taheri, R.: Methodology for performance analysis of VMware vSphere under Tier-1 applications. VMware Tech. J. 2(1), 19–28 (2013)

    Google Scholar 

  33. Vieira, M., Sarinho, V.: AutomataMind: a serious game proposal for the automata theory learning. In: van der Spek, E., Göbel, S. (eds.) Joint International Conference on Entertainment Computing and Serious Games, pp. 452–455. Springer, Berlin (2019)

    Google Scholar 

  34. Narendra, K.S., Mukhopadhyay, S.: Mutual learning: part i-learning automata. In: 2019 American Control Conference (ACC), pp. 916–921. IEEE (2019)

  35. Li, G., Wang, X., Zhang, Z.: SDN-based load balancing scheme for multi-controller deployment. IEEE Access 7, 39612–39622 (2019)

    Google Scholar 

  36. Sahoo, K.S., et al.: ESMLB: efficient switch migration-based load balancing for multicontroller SDN in IoT. IEEE Internet Things J. 7(7), 5852–5860 (2019)

    Google Scholar 

  37. Ider, M., Barekatain, B.: An enhanced AHP–TOPSIS-based load balancing algorithm for switch migration in software-defined networks. J. Supercomput. 77, 563 (2020)

    Google Scholar 

  38. David, H., Fallin, C., Gorbatov, E., Hanebutte, U.R., Mutlu, O.: Memory power management via dynamic voltage/frequency scaling. In: Proceedings of the 8th ACM international conference on Autonomic computing, pp. 31–40. ACM (2011)

  39. Zhou, Y., et al.: A load balancing strategy of sdn controller based on distributed decision. In: 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, pp. 851–856. IEEE (2014)

  40. Zhou, Y., Ruan, L., Xiao, L., Liu, R.: A method for load balancing based on software defined network. Adv. Sci. Technol. Lett. 45, 43–48 (2014)

    Google Scholar 

  41. Nunes, B.A.A., Mendonca, M., Nguyen, X.-N., Obraczka, K., Turletti, T.: A survey of software-defined networking: past, present, and future of programmable networks. IEEE Commun. Surv. Tutor. 16(3), 1617–1634 (2014)

    Google Scholar 

  42. Guo, Z., et al.: Improving the performance of load balancing in software-defined networks through load variance-based synchronization. Comput. Netw. 68, 95–109 (2014)

    Google Scholar 

  43. Tahaei, H., Salleh, R., Khan, S., Izard, R., Choo, K.-K.R., Anuar, N.B.: A multi-objective software defined network traffic measurement. Measurement 95, 317–327 (2017)

    Google Scholar 

  44. Hamdan, M., et al.: A comprehensive survey of load balancing techniques in software-defined network. J. Netw. Comput. Appl. 174, 102856 (2021)

    Google Scholar 

  45. Huang, H., Guo, S., Wu, J., Li, J.: Green datapath for TCAM-based software-defined networks. IEEE Commun. Mag. 54(11), 194–201 (2016)

    Google Scholar 

  46. Lin, S.-C., Wang, P., Luo, M.: Control traffic balancing in software defined networks. Comput. Netw. 106, 260–271 (2016)

    Google Scholar 

  47. Nair, M.: A mediator based dynamic server load balancing approach using sdn. Int. J. Control Theory Appl. pp. 6647–6652 (2016)

  48. Cheung, C.-M., Leung, K.-C.: DFFR: a flow-based approach for distributed load balancing in data center networks. Comput. Commun. 116, 1–8 (2018)

    Google Scholar 

  49. Liu L., et al.: An SDN-based hybrid strategy for load balancing in data center networks. In: 2019 IEEE Symposium on Computers and Communications (ISCC), pp. 1–6. IEEE (2019)

  50. Belgaum, M.R., Musa, S., Alam, M.M., Su’ud, M.M.: A systematic review of load balancing techniques in software-defined networking. IEEE Access 8, 98612 (2020)

    Google Scholar 

  51. Li, L., Xu, Q.: Load balancing researches in SDN: a survey. In: 2017 7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC), pp. 403–408. IEEE (2017)

  52. Kaur, P., Chahal, J.K., Bhandari, A.: Load balancing in software defined networking: a review. Asian J. Comput. Sci. Technol. 7(2), 1–5 (2018)

    Google Scholar 

  53. Karakus, M., Durresi, A.: Quality of service (QoS) in software defined networking (SDN): a survey. J. Netw. Comput. Appl. 80, 200–218 (2017)

    Google Scholar 

  54. Xie, J., et al.: A survey of machine learning techniques applied to software defined networking (SDN): research issues and challenges. IEEE Commun. Surv. Tutor. 21(1), 393–430 (2018)

    Google Scholar 

  55. Hazra, A., Adhikari, M., Amgoth, T., Srirama, S.N.: Collaborative AI-enabled intelligent partial service provisioning in green industrial fog networks. IEEE Internet Things J. (2021). https://doi.org/10.1109/JIOT.2021.3110910

    Article  Google Scholar 

  56. Kashiri, N., Tsagarakis, N.G., Van Damme, M., Vanderborght, B., Caldwell, D.G.: Proxy-based sliding mode control of compliant joint manipulators. In: Filipe, J., Gusikhi, O. (eds.) Informatics in Control, Automation and Robotics, pp. 241–257. Springer, Berlin (2016)

    Google Scholar 

  57. Sminesh, C.N.: A proactive flow admission and re-routing scheme for load balancing and mitigation of congestion propagation in SDN data plane. Int. J. Comput. Netw. Commun. (IJCNC) 10(117), 2019 (2019)

    Google Scholar 

  58. Namal, S., Ahmad, I., Gurtov, A., Ylianttila, M.: SDN based inter-technology load balancing leveraged by flow admission control. In: 2013 IEEE SDN for Future Networks and Services (SDN4FNS), pp. 1–5. IEEE (2013)

  59. Khan, S., Gani, A., Wahab, A.W.A., Guizani, M., Khan, M.K.: Topology discovery in software defined networks: threats, taxonomy, and state-of-the-art. IEEE Commun. Surv. Tutor. 19(1), 303–324 (2016)

    Google Scholar 

  60. Hsu, C.-H., Kremer, U.: Compiler-directed dynamic voltage scaling for memory-bound applications. Technical Report DCS-TR-498, Department of Computer Science, Rutgers University (2002)

  61. Mishra, A., Khare, N.: Analysis of dvfs techniques for improving the gpu energy efficiency. Open J. Energy Effic. 4(04), 77 (2015)

    Google Scholar 

  62. Mokaripoor, P., Hosseini Shirvani, M.: A state of the art survey on DVFS techniques in cloud computing environment. J. Multidiscip. Eng. Sci. Technol 3(5), 4740–4743 (2016)

    Google Scholar 

  63. Pavlik, M., Mihal, R., Lacinak, L., Zolotova, I.: Supervisory control and data acquisition systems in virtual architecture built via VMware vSphare platform. In: The 16th WSEAS International Conference on Circuits, pp. 389–393. WSEAS, Kos Island (2012)

  64. Guérout, T., Monteil, T., Da Costa, G., Calheiros, R.N., Buyya, R., Alexandru, M.: Energy-aware simulation with DVFS. Simul. Model. Pract. Theory 39, 76–91 (2013)

    Google Scholar 

  65. Ding, Y., Qin, X., Liu, L., Wang, T.: Energy efficient scheduling of virtual machines in cloud with deadline constraint. Future Gener. Comput. Syst. 50, 62–74 (2015)

    Google Scholar 

Download references

Funding

This research received no specific grant from any funding agency.

Author information

Authors and Affiliations

  1. Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Marjan Mahmoudi & Behrang Barekatain

  2. Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Avid Avokh

  3. Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Behrang Barekatain

  4. Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Avid Avokh

Authors
  1. Marjan Mahmoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Avid Avokh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Behrang Barekatain
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by MM, BB, and AA. The first draft of the manuscript was written by MM and all authors commented on previous versions of the manuscript. Finally, the corresponding author checked and finalized everything.

Corresponding author

Correspondence to Behrang Barekatain.

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

Mahmoudi, M., Avokh, A. & Barekatain, B. SDN-DVFS: an enhanced QoS-aware load-balancing method in software defined networks. Cluster Comput 25, 1237–1262 (2022). https://doi.org/10.1007/s10586-021-03522-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03522-x

Keywords

Search

Navigation