Fast Resource Allocation for Resilient Service Coordination in an NFV-Enabled Internet-of-Things System

Tuan-Minh Pham, Thi-Minh Nguyen

Abstract


Network Functions Virtualization (NFV) is a new way of leveraging an Internet-of-Things (IoT) system to provide real-time and highly flexible service creation. In an NFV-enabled Internet-of-Things (NIoT) system, several IoT functions implemented as Virtual Network Functions can be linked as a service function chain to build a customized IoT service quickly. It is important for an IoT service to be able to recover from a failure. However, the supply of a resilient IoT service in an NIoT system is challenging due to the coordination of distributed VNF instances. In this paper, we formulate the problem of resilient service coordination in an NIoT system as a mixed-integer linear programming model, namely RSOd. The model offers the optimal resource allocation for minimizing service disruption when a failure happens at a node of an NIoT system. We also develop two modified versions of RSOd for different use cases required by an IoT provider. Further, two approximation algorithms are proposed to provide a resilient service for a large-scale NIoT system. The evaluation results show that RSOd and its modified versions produce the optimal resource allocation in significantly reduced time compared to previous work. The results suggest that an IoT provider should carefully select an appropriate resource allocation strategy as it has to pay a resource cost to minimize the service disruption. The results also show that our proposed priority-based heuristic algorithm outperforms an approximation algorithm based on Simulated Annealing
in terms of the service disruption and computation time.


Full Text:

PDF

References


ETSI, “SmartM2M: Virtualized iot architectures with cloud back-ends, TR 103 527 V1.1.1,” 2018. [Online]. Available:https://www.etsi.org/deliver/etsi tr/103500 103599/103527/01.01.01 60/tr 103527v010101p.pdf

H. Huang and S. Guo, “Proactive failure recovery for nfv in distributed edge computing,” IEEE Communications Magazine, vol. 57,no. 5, pp. 131–137, 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8663964

D. Ergenc, J. Rak, and M. Fischer, “Service-based resilience for embedded iot networks,” in 2020 50th AnnualIEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2020, pp. 540–551. [Online]. Available:https://ieeexplore.ieee.org/document/9153441

T.-M. Pham, S. Fdida, T.-T.-L. Nguyen, and H.-N. Chu, “Modeling and analysis of robust service composition for network functionsvirtualization,” Computer Networks, vol. 166, p. 106989, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S1389128619305080

L. Sanabria-Russo, J. Serra, D. Pubill, and C. Verikoukis, “Curate: On-demand orchestration of services for health emergenciesprediction and mitigation,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 2, pp. 438–445, 2021. [Online].Available: https://ieeexplore.ieee.org/document/9186033

T.-M. Pham, “Optimizing service function chaining migration with explicit dynamic path,” IEEE Access, vol. 10, pp. 16 992–17 002,2022. [Online]. Available: https://ieeexplore.ieee.org/document/9709333/

T.-M. Pham and T.-T.-L. Nguyen, “Optimization of resource management for nfv-enabled iot systems in edge cloud computing,”IEEE Access, vol. 8, pp. 178 217–178 229, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9206011

D. P. Abreu, K. Velasquez, M. Curado, and E. Monteiro, “A resilient internet of things architecture for smart cities,” Annals ofTelecommunications, vol. 72, no. 1, pp. 19–30, 2017. [Online]. Available: https://link.springer.com/article/10.1007/s12243-016-0530-y

O. Kaiwartya, A. H. Abdullah, Y. Cao, J. Lloret, S. Kumar, R. R. Shah, M. Prasad, and S. Prakash, “Virtualization in wirelesssensor networks: Fault tolerant embedding for internet of things,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 571–580, 2018.[Online]. Available: https://ieeexplore.ieee.org/document/7954010

D. Ratasich, F. Khalid, F. Geissler, R. Grosu, M. Shafique, and E. Bartocci, “A roadmap toward the resilient internet of thingsfor cyber-physical systems,” IEEE Access, vol. 7, pp. 13 260–13 283, 2019. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8606923

C. Berger, P. Eichhammer, H. P. Reiser, J. Domaschka, F. J. Hauck, and G. Habiger, “A survey on resilience in the iot: Taxonomy,classification, and discussion of resilience mechanisms,” ACM Comput. Surv., vol. 54, no. 7, sep 2021. [Online]. Available:https://dl.acm.org/doi/10.1145/3462513

W. Si, D. Starobinski, and M. Laifenfeld, “A robust load balancing and routing protocol for intra-car hybrid wired/wirelessnetworks,” IEEE Transactions on Mobile Computing, vol. 18, no. 2, p. 250–263, 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8353355

F. M. Malik, H. A. Khattak, A. Almogren, O. Bouachir, I. U. Din, and A. Altameem, “Performance evaluation of datadissemination protocols for connected autonomous vehicles,” IEEE Access, vol. 8, pp. 126 896–126 906, 2020. [Online]. Available:https://ieeexplore.ieee.org/document/9129666

A. Muhammad, M. Saqib, and W.-C. Song, “Sensor virtualization and data orchestration in internet of vehicles (iov),” in2021 IFIP/IEEE International Symposium on Integrated Network Management (IM), 2021, pp. 998–1003. [Online]. Available:https://ieeexplore.ieee.org/document/9463936

Q. Luo, S. Hu, C. Li, G. Li, and W. Shi, “Resource scheduling in edge computing: A survey,” IEEE Communications SurveysTutorials, vol. 23, no. 4, pp. 2131–2165, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9519636

M. A. Shahid, N. Islam, M. M. Alam, M. M. Su’ud, and S. Musa, “A comprehensive study of load balancing approaches in the cloudcomputing environment and a novel fault tolerance approach,” IEEE Access, vol. 8, pp. 130 500–130 526, 2020. [Online]. Available:https://ieeexplore.ieee.org/abstract/document/9139971

S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, no. 4598, pp. 671–680, may1983.

“Geant dataset.” [Online]. Available: http://www.topology-zoo.org/dataset.html

M. Drobyshevskiy and D. Turdakov, “Random graph modeling: A survey of the concepts,” ACM Comput. Surv., vol. 52, no. 6, 2019.[Online]. Available: https://dl.acm.org/doi/abs/10.1145/3369782

“IBM ILOG CPLEX Optimizer.” [Online]. Available: https://www.ibm.com/analytics/cplex-optimizer/

T.-M. Pham, “Traffic engineering based on reinforcement learning for service function chaining with delay guarantee,” IEEE Access,vol. 9, pp. 121 583–121 592, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9526552/




DOI: http://dx.doi.org/10.21553/rev-jec.320

Copyright (c) 2023 REV Journal on Electronics and Communications


ISSN: 1859-378X

Copyright © 2011-2024
Radio and Electronics Association of Vietnam
All rights reserved