Multi-criteria reactive approach for joint dynamic VNF load balancing and service auto-scaling in NFV

Multi-criteria reactive approach for joint dynamic VNF load balancing and service auto-scaling in NFV

Amir Kusedghi, Ahmad Akbari


The evolution of 5G key-enabler technologies, such as Software-defined Networking (SDN) and Network Function Virtualization (NFV), has brought network operators" attention to procure an efficient service delivery mechanism. Therefore, it is vital to leverage the service management and orchestration functionalities that operate in harmony. This can curb the undesirable negative impact of inconsistency on the quality of service. In this paper, we investigate the joint load balancing and auto-scaling of the elastic services that are being provisioned in the computing infrastructure at the edge or cloud. We address the necessity and challenges of designing the load balancing algorithm and scale decision making policy, aware of one another, through several practical scenarios on multimedia service delivery in NFV. It is demonstrated that changing the VNF load balancing method may deteriorate the scaling quality of decision-making. Consequently, we propose a novel multi-criteria reactive approach using a mutual score to dynamically adapt the VNF load balancing algorithm with the auto-scaling engine. We implement the proposed joint method on the management and orchestration layer of our previously developed NFV/SDN testbed, called XeniumNFV, to evaluate the effectiveness of our approach by conducting extensive experiments on the elastic web service.


VNF load balancing, Service auto-scaling, NFV, SDN, Edge computing, 5G networks


  • [1] A. Kusedghi, A. Ghorab, and A. Akbari, “XeniumNFV: A unified, dynamic, distributed and event-driven SDN/NFV testbed,” Proc. Of Int’l. Conf. on Cloud Computing Technology and Science (CloudCom), Nicosia, Cyprus, Dec., pp. 320–326, 2018.
  • [2] S. H. K. S. L. Chen, Y. Chen, “CLB: A novel load balancing architecture and algorithm for cloud services,” Computers and Electrical Engineering, vol. 58, no. 1, pp. 154–160, Feb. 2017.
  • [3] D. J. A. Singh and M. Malhotra, “Autonomous agent based load balancing algorithm in cloud computing,” Proc. Computer Science, vol. 45, Jan., pp. 832–841, 2015.
  • [4] C. C. Li and K. Wang, “An SLA-aware load balancing scheme for cloud datacenters,” Proc. of Int’l. Conf. on Information Networking (ICOIN), Phuket, Thailand, Feb., pp. 58–63, 2014.
  • [5] S. M. B. A. Akbar and A. Sattar, “A Comparative Study on Load Balancing Algorithms for SIP Servers,” New Delhi: Springer, Feb., pp. 79–88, 2016.
  • [6] V. Nguyen, K. Grinnemo, J. Taheri, and A. Brunstrom, “On load balancing for a virtual and distributed MME in the 5G core,” Proc. of Int’l. Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Bologna, Italy, Sept. 2018.
  • [7] M. Thai, Y. Lin, and Y. Lai, “A joint network and server load balancing algorithm for chaining virtualized network functions,” Proc. of Int’l. Conf. on Communications (ICC), Kuala Lumpur, Malaysia, May 2016.
  • [8] X. T. W. Chen, Z. Shang and H. Li, “Dynamic server cluster load balancing in virtualization environment with Openflow,” International Journal of Distributed Sensor Networks, vol. 11, no. 7, pp. 531–538, July 2015.
  • [9] W. Zhang, T. Wood, and J. Hwang, “Netkv: Scalable, self-managing, load balancing as a network function,” Proc. of Int’l. Conf. on Autonomic Computing (ICAC), Wurzburg, Germany, July 2016.
  • [10] S. J. Y. Go, R. Guinto, C. A. M. Festin, I. Austria, R. Ocampo, and W. M. Tan, “An SDN/NFV-enabled architecture for detecting personally identifiable information leaks on network traffic,” Proc. of Int’l. Conf. on Ubiquitous and Future Networks (ICUFN), Zagreb, Croatia, July 2019.
  • [11] M. Abdullah, W. Igbal, and F. Bukhari, “Containers vs virtual machines for auto-scaling multi-tier applications under dynamically increasing workloads,” Proc. of Int’l. Conf. on Intelligent Technologies and Applications (INTAP), Bahawalpur, Pakistan, Oct. 2019.
  • [12] A. Kwan, J. Wong, H. Jacobsen, and V. Muthusamy, “Hyscale: Hybrid and network scaling of dockerized microservices in cloud data centres,” Proc. of Int’l. Conf. on Distributed Computing Systems (ICDCS), Dallas, TX, July 2019.
  • [13] F. Rossi, M. Nardelli, and V. Cardellini, “Horizontal and vertical scaling of container-based applications using reinforcement learning,” Proc. of Int’l. Conf. on Cloud Computing (CLOUD), Milan, Italy, July 2019.
  • [14] C. Qu, R. N. Calheiros, and R. Buyya, “Auto-scaling web applications in clouds: A taxonomy and survey,” ACM Comput. Surv., vol. 51, no. 4, Sept. 2018.
  • [15] R. Mijumbi, S. Hasija, S. Davy, A. Davy, B. Jennings, and R. Boutaba, “Topology-aware prediction of virtual network function resource requirements,” IEEE Trans. On Network and Service Management, vol. 14, no. 1, pp. 106–120, Mar. 2017.
  • [16] V. Sciancalepore, F. Z. Yousaf, and X. Costa-Perez, “z-torch: An automated NFV orchestration and monitoring solution,” IEEE Trans. On Network and Service Management, vol. 15, no. 4, pp. 1292–1306, Dec. 2018.
  • [17] J. Duan, C. Wu, F. Le, A. X. Liu, and Y. Peng, “Dynamic scaling of virtualized, distributed service chains: A case study of IMS,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 11, pp. 2501– 2511, Nov. 2017.
  • [18] A. M. Medhat, G. A. Carella, M. Pauls, and T. Magedanz, “Extensible framework for elastic orchestration of service function chains in 5g networks,” Proc. of Int’l. Conf. on Network Function Virtualization and Software Defined Networks (NFV-SDN), Berlin, Germany, pp. 327–333, Nov. 2017.
  • [19] F. Lombardi, A. Muti, L. Aniello, R. Baldoni, S. Bonomi, and L. Querzoni, “Pascal: An architecture for proactive auto-scaling of distributed services,” Future Generation Computer Systems, vol. 98, no. 2, pp. 342 – 361, Sept. 2019.
  • [20] W. Iqbal, A. Erradi, M. Abdullah, and A. Mahmood, “Predictive auto-scaling of multi-tier applications using performance varying cloud resources,” IEEE Transactions on Cloud Computing (Early Access, pp. 1–1, Sept. 2019.
  • [21] I. Alawe, Y. Hadjadj-Aoul, A. Ksentinit, P. Bertin, C. Viho, and D. Darche, “An efficient and lightweight load forecasting for proactive scaling in 5G mobile networks,” Proc. of Int’l. Conf. on Standards for Communications and Networking (CSCN), Paris, France, Oct. 2018.
  • [22] D. Cotroneo, R. Natella, and S. Rosiello, “NFV-throttle: An overload control framework for network function virtualization,” IEEE Trans. On Network and Service Management, vol. 14, no. 4, pp. 949–963, Dec. 2017.
  • [23] R. N. D. Cotroneo and S. Rosiello, “Overload control for virtual network functions under CPU contention,” Future Generation Computer Systems, vol. 99, no. 1, pp. 164–176, Oct. 2019.
  • [24] A. Bauer, N. Herbst, S. Spinner, A. Ali-Eldin, and S. Kounev, “Chameleon: A hybrid, proactive auto-scaling mechanism on a levelplaying field,” IEEE Trans. on Parallel and Distributed Systems, vol. 30, no. 4, pp. 800–813, Apr. 2019.
  • [25] A. Bauer, V. Lesch, L. Versluis, A. Ilyushkin, N. Herbst, and S. Kounev, “Chamulteon: Coordinated auto-scaling of micro-services,” Proc. Of Int’l. Conf. on Distributed Computing Systems (ICDCS), Dallas, TX, Oct. 2019.
  • [26] B. Benifa and D. Dharma, “HAS: Hybrid auto-scaler for resource scaling in cloud environment,” Journal of Parallel and Distributed Computing, vol. 120, no. 1, pp. 1–15, Oct. 2018.
  • [27] L. Velasco, R. Casellas, and S. Llana, “A control and management architecture supporting autonomic NFV services,” Photonic Network Communications, vol. 37, no. 1, pp. 24–37, Feb. 2019.
  • [28] R. Moreira, F. de Oliveira Silva, P. Frosi Rosa, and R. Aguiar, “A flexible network and compute-aware orchestrator to enhance QoS in NFV-based multimedia services,” Proc. of Int’l. Conf. on Advanced Information Networking and Applications (AINA), Krakow, Poland, pp. 512–519, May 2018.
  • [29] R. Poddar, A. Vishnoi, and V. Mann, “Haven: Holistic load balancing and auto scaling in the cloud,” Proc. of Int’l. Conf. on Communication Systems and Networks (COMSNETS), Bangalore, India, Jan. 2015.
  • [30] W. Abderrahim and Z. Choukair, “Dependability integration in cloud-hosted telecommunication services,” IEEE Trans. on Dependable and Secure Computing, vol. 16, no. 6, pp. 957–968, Nov. 2019.
  • [31] A. J. Gonzalez, G. Nencioni, A. Kamisinski, B. E. Helvik, and P. E. ´ Heegaard, “Dependability of the NFV orchestrator: State of the art and research challenges,” IEEE Communications Surveys Tutorials, vol. 20, no. 4, pp. 3307 3329, Fourthquarter 2018.
  • [32] A. Kusedghi, Z. Bagherabadi, and A. Akbari, “An IMS-aware VM placement in cloud environment,” Proc. of Int’l. Conf. on Cloud Computing Technology and Science (CloudCom), Nicosia, Cyprus, pp. 327–334, Dec. 2018.
  • [33] “Sipp,”, Sept. 2020, accessed on 2020-09- 01.
  • [34] Sangoma Technologies, “Asterisk,”, Sept. 2020, accessed on 2020-09 01.
  • [35] OpenSIPS project, “Opensips,”, July 2020, accessed on 2020-09-01.
  • [36] Kamailio SIP project, “Kamailio sip load balancer,”, Sept. 2020, accessed on 2020-09-01.
  • [37] Docker, “Docker hub,”, Sept. 2020, accessed on 2020-09-01.
  • [38] Apache Software Foundation, “Apache httpd,”, Apr. 2020, accessed on 2020-09-01.
  • [39] HAProxy community edition, “Haproxy,”, Dec. 2019, accessed on 2020-09-01.
  • [40] The Linux Foundation, “Opendaylight,”, Dec. 2018, accessed on 2020 09-01.
  • [41] D. Mosberger and T. Jin, “Httperf—a tool for measuring web server performance,” SIGMETRICS Perform. Eval. Rev., vol. 26, no. 3, p. 31–37, Dec. 1998.
  • [42] T. Benson, A. Anand, A. Akella, and M. Zhang, “Understanding data center traffic characteristics,” SIGCOMM Comput. Commun. Rev., vol. 40, no. 1, p. 92–99, Jan. 2010.
  • [43] Z. Liu, N. Niclausse, and C. Jalpa-Villanueva, “Traffic model and performance evaluation of web servers,” Performance Evaluation, vol. 46, no. 2, pp. 77 – 100, Oct. 2001.