Service Chain Placement by Using an African Vulture Optimization Algorithm Based VNF in Cloud-Edge Computing
Abstract The use of virtual network functions (VNFs) enables the implementation of service function chains (SFCs), which is an innovative approach for delivering network services. The deployment of service chains on the actual network infrastructure and the establishment of virtual connections between VNF instances are crucial factors that significantly impact the quality of network services provided. Current research on the allocation of vital VNFs and resource constraints on the edge network has overlooked the potential benefits of employing SFCs with instance reuse. This strategy offers significant improvements in resource utilization and reduced startup time. The proposed approach demonstrates superior performance compared to existing state-of-the-art methods in maintaining inbound service chain requests, even in complex network typologies observed in real-world scenarios. We propose a novel technique called African vulture optimization algorithm for virtual network functions (AVOAVNF), which optimizes the sequential arrangement of SFCs. Extensive simulations on edge networks evaluate the AVOAVNF methodology, considering metrics such as latency, energy consumption, throughput, resource cost, and execution time. The results indicate that the proposed method outperforms BGWO, DDRL, BIP, and MILP techniques, reducing energy consumption by 8.35%, 12.23%, 29.54%, and 52.29%, respectively.
- Referencias
- Cómo citar
- Del mismo autor
- Métricas
Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2017). Mobile Edge Computing: A survey. IEEE Internet of Things Journal, 5(1), 450–465. https://doi.org/10.1109/JIOT.2017.2750180
Abdollahzadeh, B., Gharehchopogh, F.S., & Mirjalili, S. (2021). African vulture’s optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408. https://doi.org/10.1016/j.cie.2021.107408
Akhtar, N., Matta, I., Raza, A., Goratti, L., Braun, T., & Esposito, F. (2021). Managing chains of application functions over Multi-Technology edge networks. IEEE Transactions on Network and Service Management, 18(1), 511–525. https://doi.org/10.1109/TNSM.2021.3050009
Ale, L., Zhang, N., Fang, X., Chen, X., Wu, S., & Li, L. (2021). Delay-Aware and Energy-Efficient computation offloading in Mobile-Edge Computing using deep Reinforcement learning. IEEE Transactions on Cognitive Communications and Networking, 7(3), 881–892. https://doi.org/10.1109/TCCN.2021.3066619
Almurshed, O., Rana, O., & Chard, K. (2022). Greedy Nominator Heuristic: Virtual function placement on fog resources. Concurrency and Computation: Practice and Experience, 34(6), e6765. https://doi.org/10.1002/cpe.6765
Attaoui, W., Sabir, E., Elbiaze, H. and Guizani, M., 2022. VNF and Container Placement: Recent Advances and Future Trends. arXiv preprint arXiv:2204.00178.
Bahreini, T., & Grosu, D. (2020). Efficient Algorithms for Multi-Component application placement in mobile edge Computing. IEEE Transactions on Cloud Computing, 10(4), 2550–2563. https://doi.org/10.1109/tcc.2020.3038626
Cziva, R., & Pezaros, D. P. (2017). Container network functions: Bringing NFV to the network edge. IEEE Communications Magazine, 55(6), 24–31. https://doi.org/10.1109/MCOM.2017.1601039
Deng, S., Xiang, Z., Taheri, J., Khoshkholghi, M. A., Yin, J., Zomaya, A. Y., & Dustdar, S. (2021). Optimal application deployment in resource constrained distributed edges. IEEE Transactions on Mobile Computing, 20(5), 1907–1923. https://doi.org/10.1109/tmc.2020.2970698
Gao, X., Liu, R., & Kaushik, A. (2022). Virtual network function placement in satellite edge computing with a potential game approach. IEEE Transactions on Network and Service Management, 19(2), 1243–1259. https://doi.org/10.1109/TNSM.2022.3141165
Hazra, A., Adhikari, M., Amgoth, T., & Srirama, S. N. (2021). Intelligent Service Deployment Policy for Next-Generation Industrial Edge Networks. IEEE Transactions on Network Science and Engineering, 9(5), 3057–3066. https://doi.org/10.1109/tnse.2021.3122178
Khan, W. Z., Ahmed, E., Hakak, S., Yaqoob, I., & Ahmed, A. (2019). Edge Computing: a survey. Future Generation Computer Systems, 97, 219–235. https://doi.org/10.1016/j.future.2019.02.050
Khoshkholghi, M. A., Khan, M. G., Noghani, K. A., Taheri, J., Bhamare, D., Kassler, A., Xiang, Z., Deng, S., & Yang, X. (2020). Service function chain placement for joint cost and latency optimization. Mobile Networks and Applications, 25(6), 2191–2205. https://doi.org/10.1007/s11036-020-01661-w
Khoshkholghi, M. A., & Mahmoodi, T. (2022). Edge Intelligence for service function chain deployment in NFV-enabled networks. Computer Networks, 219, 109451. https://doi.org/10.1016/j.comnet.2022.109451
Kouah, R., Alleg, A., Laraba, A., & Ahmed, T. (2018). Energy-aware placement for iot-service function chain. 2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 1–7. IEEE. https://doi.org/10.1109/CAMAD.2018.8515003
Liang, W., Cui, L., & Tso, F. P. (2022). Low-latency service function chain migration in edge-core networks based on open Jackson networks. Journal of Systems Architecture, 124, 102405. https://doi.org/10.1016/j.sysarc.2022.102405
Luizelli, M. C., Cordeiro, W., Buriol, L. S., & Gaspary, L. P. (2017). A fix-and-optimize approach for efficient and large scale virtual network function placement and chaining. Computer Communications, 102, 67–77. https://doi.org/10.1016/j.comcom.2016.11.002
Magoula, L., Barmpounakis, S., Stavrakakis, I., & Alonistioti, N., (2021). A genetic algorithm approach for service function chain placement in 5G and beyond, virtualized edge networks. Computer Networks, 195, 108157. https://doi.org/10.1016/j.comnet.2021.108157
Matias, J., Garay, J., Toledo, N., Unzilla, J., & Jacob, E. (2015). Toward an SDN-enabled NFV architecture. IEEE Communications Magazine, 53(4), 187–193. https://doi.org/10.1109/MCOM.2015.7081093
Munusamy, A., Adhikari, M., Balasubramanian, V., Khan, M. A., Menon, V. G., Rawat, D., & Srirama, S. N. (2021). Service deployment strategy for predictive analysis of FinTech IoT applications in edge networks. IEEE Internet of Things Journal.
Pei, J., Hong, P., Pan, M., Liu, J., & Zhou, J. (2019). Optimal VNF placement via deep reinforcement learning in SDN/NFV-Enabled networks. IEEE Journal on Selected Areas in Communications, 38(2), 263–278. https://doi.org/10.1109/jsac.2019.2959181
Pham, C., Tran, N. H., Ren, S., Saad, W., & Hong, C. S. (2017). Traffic-Aware and Energy-Efficient VNF placement for service chaining: joint sampling and matching approach. IEEE Transactions on Services Computing, 13(1), 172–185. https://doi.org/10.1109/tsc.2017.2671867
Qu, H., Wang, K., & Zhao, J. (2022). Priority-awareness VNF migration method based on deep Reinforcement learning. Computer Networks, 208, 108866. https://doi.org/10.1016/j.comnet.2022.108866
Sahoo, B. M., Pandey, H. M., & Amgoth, T. (2022). A genetic algorithm inspired optimized cluster head selection method in wireless sensor networks. Swarm and Evolutionary Computation, 75, 101151. https://doi.org/10.1016/j.swevo.2022.101151
Schardong, F., Nunes, I., & Schaeffer-Filho, A. (2021). NFV Resource Allocation: A Systematic Review and Taxonomy of VNF forwarding graph embedding. Computer Networks, 185, 107726. https://doi.org/10.1016/j.comnet.2020.107726
Shahjalal, M., Farhana, N., Roy, P., Razzaque, M. A., Kaur, K., & Hassan, M. M. (2022). A binary Gray Wolf optimization algorithm for deployment of virtual network functions in 5G hybrid cloud. Computer Communications, 193, 63–74. https://doi.org/10.1016/j.comcom.2022.06.041
Tajiki, M. M., Salsano, S., Chiaraviglio, L., Shojafar, M., & Akbari, B. (2018). Joint Energy Efficient and QOS-Aware path allocation and VNF placement for service function chaining. IEEE Transactions on Network and Service Management, 16(1), 374–388. https://doi.org/10.1109/TNSM.2018.2873225
Tomassilli, A., Giroire, F., Huin, N., & Pérennes, S. (2018). Probably efficient algorithms for placement of service function chains with ordering constraints. IEEE INFOCOM 2018 – IEEE Conference on Computer Communications, 774–782. IEEE. https://doi.org/10.1109/INFOCOM.2018.8486275
Wang, M., Cheng, B., Feng, W., & Chen, J. (2019). An efficient service function chain placement algorithm in a MEC-NFV environment. 2019 IEEE Global Communications Conference (GLOBECOM), 1–6. IEEE. https://doi.org/10.1109/GLOBECOM38437.2019.9013235
Wang, S., Zafer, M., & Leung, K. K. (2017). Online placement of Multi-Component applications in edge computing environments. IEEE Access, 5, 2514–2533. https://doi.org/10.1109/access.2017.2665971
Wang, X., Ning, Z., Guo, L., Guo, S., Gao, X., & Wang, G. (2021). Online learning for distributed computation offloading in wireless powered mobile edge computing networks. IEEE Transactions on Parallel and Distributed Systems, 33(8), 1841–1855. https://doi.org/10.1109/tpds.2021.3129618
Yang, B., Chai, W. K., Pavlou, G., & Katsaros, K.V. (2016, October). Seamless support of low latency mobile applications with nfv-enabled mobile edge-cloud. 2016 5th IEEE International Conference on Cloud Networking (Cloudnet), 136–141. IEEE. https://doi.org/10.1109/CloudNet.2016.21
Yang, S., Li, F., Trajanovski, S., Chen, X., Wang, Y., & Fu, X. (2019). Delay-aware virtual network function placement and routing in edge clouds. IEEE Transactions on Mobile Computing, 20(2), 445–459. https://doi.org/10.1109/TMC.2019.2942306
Yang, X. S. (2010). Firefly algorithm, Levy flights and global optimization. In Research and development in intelligent systems XXVI: Incorporating applications and innovations in intelligent systems XVII (pp. 209–218). Springer London. https://doi.org/10.1007/978-1-84882-983-1_15
Zahedi, S. R., Jamali, S., & Bayat, P. (2022). EMCFIS: Evolutionary Multi-criteria Fuzzy Inference System for virtual network function placement and routing. Applied Soft Computing, 117, 108427. https://doi.org/10.1016/j.asoc.2022.108427
Zhang, S., Jia, W., Tang, Z., Lou, J., & Zhao, W. (2022). Efficient instance reuse approach for service function chain placement in mobile edge computing. Computer Networks, 211, 109010. https://doi.org/10.1016/j.comnet.2022.109010
Zhang, Y., Zhang, F., Si, T., & Rezaeipanah, A. (2022). A dynamic planning model for deploying service functions chain in Fog-cloud computing. Journal of King Saud University - Computer and Information Sciences, 34(10), 7948–7960. https://doi.org/10.1016/j.jksuci.2022.07.012
Abdollahzadeh, B., Gharehchopogh, F.S., & Mirjalili, S. (2021). African vulture’s optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408. https://doi.org/10.1016/j.cie.2021.107408
Akhtar, N., Matta, I., Raza, A., Goratti, L., Braun, T., & Esposito, F. (2021). Managing chains of application functions over Multi-Technology edge networks. IEEE Transactions on Network and Service Management, 18(1), 511–525. https://doi.org/10.1109/TNSM.2021.3050009
Ale, L., Zhang, N., Fang, X., Chen, X., Wu, S., & Li, L. (2021). Delay-Aware and Energy-Efficient computation offloading in Mobile-Edge Computing using deep Reinforcement learning. IEEE Transactions on Cognitive Communications and Networking, 7(3), 881–892. https://doi.org/10.1109/TCCN.2021.3066619
Almurshed, O., Rana, O., & Chard, K. (2022). Greedy Nominator Heuristic: Virtual function placement on fog resources. Concurrency and Computation: Practice and Experience, 34(6), e6765. https://doi.org/10.1002/cpe.6765
Attaoui, W., Sabir, E., Elbiaze, H. and Guizani, M., 2022. VNF and Container Placement: Recent Advances and Future Trends. arXiv preprint arXiv:2204.00178.
Bahreini, T., & Grosu, D. (2020). Efficient Algorithms for Multi-Component application placement in mobile edge Computing. IEEE Transactions on Cloud Computing, 10(4), 2550–2563. https://doi.org/10.1109/tcc.2020.3038626
Cziva, R., & Pezaros, D. P. (2017). Container network functions: Bringing NFV to the network edge. IEEE Communications Magazine, 55(6), 24–31. https://doi.org/10.1109/MCOM.2017.1601039
Deng, S., Xiang, Z., Taheri, J., Khoshkholghi, M. A., Yin, J., Zomaya, A. Y., & Dustdar, S. (2021). Optimal application deployment in resource constrained distributed edges. IEEE Transactions on Mobile Computing, 20(5), 1907–1923. https://doi.org/10.1109/tmc.2020.2970698
Gao, X., Liu, R., & Kaushik, A. (2022). Virtual network function placement in satellite edge computing with a potential game approach. IEEE Transactions on Network and Service Management, 19(2), 1243–1259. https://doi.org/10.1109/TNSM.2022.3141165
Hazra, A., Adhikari, M., Amgoth, T., & Srirama, S. N. (2021). Intelligent Service Deployment Policy for Next-Generation Industrial Edge Networks. IEEE Transactions on Network Science and Engineering, 9(5), 3057–3066. https://doi.org/10.1109/tnse.2021.3122178
Khan, W. Z., Ahmed, E., Hakak, S., Yaqoob, I., & Ahmed, A. (2019). Edge Computing: a survey. Future Generation Computer Systems, 97, 219–235. https://doi.org/10.1016/j.future.2019.02.050
Khoshkholghi, M. A., Khan, M. G., Noghani, K. A., Taheri, J., Bhamare, D., Kassler, A., Xiang, Z., Deng, S., & Yang, X. (2020). Service function chain placement for joint cost and latency optimization. Mobile Networks and Applications, 25(6), 2191–2205. https://doi.org/10.1007/s11036-020-01661-w
Khoshkholghi, M. A., & Mahmoodi, T. (2022). Edge Intelligence for service function chain deployment in NFV-enabled networks. Computer Networks, 219, 109451. https://doi.org/10.1016/j.comnet.2022.109451
Kouah, R., Alleg, A., Laraba, A., & Ahmed, T. (2018). Energy-aware placement for iot-service function chain. 2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 1–7. IEEE. https://doi.org/10.1109/CAMAD.2018.8515003
Liang, W., Cui, L., & Tso, F. P. (2022). Low-latency service function chain migration in edge-core networks based on open Jackson networks. Journal of Systems Architecture, 124, 102405. https://doi.org/10.1016/j.sysarc.2022.102405
Luizelli, M. C., Cordeiro, W., Buriol, L. S., & Gaspary, L. P. (2017). A fix-and-optimize approach for efficient and large scale virtual network function placement and chaining. Computer Communications, 102, 67–77. https://doi.org/10.1016/j.comcom.2016.11.002
Magoula, L., Barmpounakis, S., Stavrakakis, I., & Alonistioti, N., (2021). A genetic algorithm approach for service function chain placement in 5G and beyond, virtualized edge networks. Computer Networks, 195, 108157. https://doi.org/10.1016/j.comnet.2021.108157
Matias, J., Garay, J., Toledo, N., Unzilla, J., & Jacob, E. (2015). Toward an SDN-enabled NFV architecture. IEEE Communications Magazine, 53(4), 187–193. https://doi.org/10.1109/MCOM.2015.7081093
Munusamy, A., Adhikari, M., Balasubramanian, V., Khan, M. A., Menon, V. G., Rawat, D., & Srirama, S. N. (2021). Service deployment strategy for predictive analysis of FinTech IoT applications in edge networks. IEEE Internet of Things Journal.
Pei, J., Hong, P., Pan, M., Liu, J., & Zhou, J. (2019). Optimal VNF placement via deep reinforcement learning in SDN/NFV-Enabled networks. IEEE Journal on Selected Areas in Communications, 38(2), 263–278. https://doi.org/10.1109/jsac.2019.2959181
Pham, C., Tran, N. H., Ren, S., Saad, W., & Hong, C. S. (2017). Traffic-Aware and Energy-Efficient VNF placement for service chaining: joint sampling and matching approach. IEEE Transactions on Services Computing, 13(1), 172–185. https://doi.org/10.1109/tsc.2017.2671867
Qu, H., Wang, K., & Zhao, J. (2022). Priority-awareness VNF migration method based on deep Reinforcement learning. Computer Networks, 208, 108866. https://doi.org/10.1016/j.comnet.2022.108866
Sahoo, B. M., Pandey, H. M., & Amgoth, T. (2022). A genetic algorithm inspired optimized cluster head selection method in wireless sensor networks. Swarm and Evolutionary Computation, 75, 101151. https://doi.org/10.1016/j.swevo.2022.101151
Schardong, F., Nunes, I., & Schaeffer-Filho, A. (2021). NFV Resource Allocation: A Systematic Review and Taxonomy of VNF forwarding graph embedding. Computer Networks, 185, 107726. https://doi.org/10.1016/j.comnet.2020.107726
Shahjalal, M., Farhana, N., Roy, P., Razzaque, M. A., Kaur, K., & Hassan, M. M. (2022). A binary Gray Wolf optimization algorithm for deployment of virtual network functions in 5G hybrid cloud. Computer Communications, 193, 63–74. https://doi.org/10.1016/j.comcom.2022.06.041
Tajiki, M. M., Salsano, S., Chiaraviglio, L., Shojafar, M., & Akbari, B. (2018). Joint Energy Efficient and QOS-Aware path allocation and VNF placement for service function chaining. IEEE Transactions on Network and Service Management, 16(1), 374–388. https://doi.org/10.1109/TNSM.2018.2873225
Tomassilli, A., Giroire, F., Huin, N., & Pérennes, S. (2018). Probably efficient algorithms for placement of service function chains with ordering constraints. IEEE INFOCOM 2018 – IEEE Conference on Computer Communications, 774–782. IEEE. https://doi.org/10.1109/INFOCOM.2018.8486275
Wang, M., Cheng, B., Feng, W., & Chen, J. (2019). An efficient service function chain placement algorithm in a MEC-NFV environment. 2019 IEEE Global Communications Conference (GLOBECOM), 1–6. IEEE. https://doi.org/10.1109/GLOBECOM38437.2019.9013235
Wang, S., Zafer, M., & Leung, K. K. (2017). Online placement of Multi-Component applications in edge computing environments. IEEE Access, 5, 2514–2533. https://doi.org/10.1109/access.2017.2665971
Wang, X., Ning, Z., Guo, L., Guo, S., Gao, X., & Wang, G. (2021). Online learning for distributed computation offloading in wireless powered mobile edge computing networks. IEEE Transactions on Parallel and Distributed Systems, 33(8), 1841–1855. https://doi.org/10.1109/tpds.2021.3129618
Yang, B., Chai, W. K., Pavlou, G., & Katsaros, K.V. (2016, October). Seamless support of low latency mobile applications with nfv-enabled mobile edge-cloud. 2016 5th IEEE International Conference on Cloud Networking (Cloudnet), 136–141. IEEE. https://doi.org/10.1109/CloudNet.2016.21
Yang, S., Li, F., Trajanovski, S., Chen, X., Wang, Y., & Fu, X. (2019). Delay-aware virtual network function placement and routing in edge clouds. IEEE Transactions on Mobile Computing, 20(2), 445–459. https://doi.org/10.1109/TMC.2019.2942306
Yang, X. S. (2010). Firefly algorithm, Levy flights and global optimization. In Research and development in intelligent systems XXVI: Incorporating applications and innovations in intelligent systems XVII (pp. 209–218). Springer London. https://doi.org/10.1007/978-1-84882-983-1_15
Zahedi, S. R., Jamali, S., & Bayat, P. (2022). EMCFIS: Evolutionary Multi-criteria Fuzzy Inference System for virtual network function placement and routing. Applied Soft Computing, 117, 108427. https://doi.org/10.1016/j.asoc.2022.108427
Zhang, S., Jia, W., Tang, Z., Lou, J., & Zhao, W. (2022). Efficient instance reuse approach for service function chain placement in mobile edge computing. Computer Networks, 211, 109010. https://doi.org/10.1016/j.comnet.2022.109010
Zhang, Y., Zhang, F., Si, T., & Rezaeipanah, A. (2022). A dynamic planning model for deploying service functions chain in Fog-cloud computing. Journal of King Saud University - Computer and Information Sciences, 34(10), 7948–7960. https://doi.org/10.1016/j.jksuci.2022.07.012
Pandey, A. K., & Singh, S. (2023). Service Chain Placement by Using an African Vulture Optimization Algorithm Based VNF in Cloud-Edge Computing. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 12(1), e31509. https://doi.org/10.14201/adcaij.31509
Most read articles by the same author(s)
- Saurabh Singh, Sarvpal Singh, Jay Prakash, SRG: Energy-Efficient Localized Routing to Bypass Void in Wireless Sensor Networks , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 12 (2023)
Downloads
Download data is not yet available.
+
−