مکانیابی زنجیره توابع سرویس موازی شده با استفاده مجدد از توابع شبکه مجازی در شبکه‌های مبتنی بر محاسبات ابر-مه
محورهای موضوعی : شبکه های کامپیوتری و امنیت
فاطمه زاهدی
1
,
محمدرضا ملاحسینی اردکانی
2
,
احمد ﺣﯿﺪری ﺷﺮﯾﻒ آﺑﺎدی
3
1 - دانشجوی دکتری گروه کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران
2 - استادیار گروه کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران
3 - استادیار گروه کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران
کلید واژه: تابع شبکه مجازی, شبکه‌های مبتنی بر محاسبات ابر-مه, زنجیره تابع سرویس,
چکیده مقاله :
مجازی سازی تابع شبکه، جعبه‌های میانی سخت‌افزاری را به مجموعه‌ای از تابع شبکه مجازی مبتنی بر نرم‌افزار تبدیل می‌کند که می‌تواند میزبان تقاضای رو به رشد سرویس‌های حساس به تأخیر در شبکه های مبتنی بر محاسبات ابر-مه باشد. مکانیابی پویا زنجیره توابع سرویس با استفاده مجدد از نمونه های توابع شبکه می‌تواند منجر به بهبود استفاده از منابع و صرفه جویی در زمان شود. برای رسیدگی به این مسئله، ما یک روش مکانیابی زنجیره توابع سرویس موازی شده با استفاده مجدد از تابع شبکه در شبکه های مبتنی بر محاسبات ابر-مه را پیشنهاد می کنیم. در اینجا، مسئله مکانیابی با استفاده از رویکردهای یادگیری تقویتی عمیق باهدف حداکثر سازی پاداش تجمعی بلندمدت پیکربندی‌شده است. این روش با اشتراک گذاشته توابع شبکه به‌صورت موازی می‌تواند شتاب محاسباتی را در ارائه سرویس‌های آنلاین محقق کند. علاوه بر این، روش پیشنهادی با استخراج توزیع توابع شبکه مقداردهی شده توانایی پذیرش درخواست های آینده را افزایش می دهد. نتایج شبیه‌سازی برتری روش پیشنهادی نشان می دهد، جایی که با در نظر گرفتن معیار هزینه پولی بیش از 7% نسبت به بهترین روش های موجود عملکرد بهتری دارد.
Network function virtualization technology transforms hardware middleboxes into sets of software-based Virtual Network Function (VNF ) that can host the growing demand for latency-sensitive services at the fog-cloud computing-based networks. Dynamic placement of service functions chain by reusing (VNF) instances can improve resource utilization and save time. To address this problem, we propose a parallelized service function chain placement method by reusing (VNF) in fog-cloud computing-based networks. Here, the placement problem is configured using deep reinforcement learning approaches with the aim of maximizing long-term cumulative reward. By sharing (VNF) s in parallel, this method can achieve computational acceleration in providing online services. In addition, the proposed method increases the ability to accept future requests by extracting the distribution of the initialized (VNF) s. The simulation results show the superiority of the proposed method, where considering the monetary cost criterion of more than 7%, it performs better than the best existing methods.
[1] W. Li, H. Wang, X. Zhang, D. Li, L. Yan, Q. Fan and R. Yao, "Security Service Function Chain Based on Graph Neural Network," Information, vol. 13, no. 2, p. 78, 2022, doi: 10.3390/info13020078.
[2] K. Kaur, V. Mangat and K. Kumar, "A comprehensive survey of service function chain provisioning approaches in SDN and NFV architecture," Computer Science Review, vol. 38, p. 100298, 2020, doi: 10.1016/j.cosrev.2020.100298.
[3] M. Mechtri, C. Ghribi and D. Zeghlache, "A Scalable Algorithm for the Placement of Service Function Chains," in IEEE Transactions on Network and Service Management, vol. 13, no. 3, pp. 533-546, Sept. 2016, doi: 10.1109/TNSM.2016.2598068.
[4] A. Tomassilli, F. Giroire, N. Huin and S. Pérennes, "Provably Efficient Algorithms for Placement of Service Function Chains with Ordering Constraints," IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, Honolulu, HI, USA, 2018, pp. 774-782, doi: 10.1109/INFOCOM.2018.8486275.
[5] S. Zhang, W. Jia, Z. Tang, J. Lou and W. Zhao, "Efficient instance reuse approach for service function chain placement in mobile edge computing," Computer Networks, vol. 211, p. 109010, 2022, doi: 10.1016/j.comnet.2022.109010.
[6] K. Yang, H. Zhang and P. Hong, "Energy-Aware Service Function Placement for Service Function Chaining in Data Centers," IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA, 2016, pp. 1-6, doi: 10.1109/GLOCOM.2016.7841805.
[7] S. Xie, J. Ma and J. Zhao, "FlexChain: Bridging Parallelism and Placement for Service Function Chains," in IEEE Transactions on Network and Service Management, vol. 18, no. 1, pp. 195-208, March 2021, doi: 10.1109/TNSM.2020.3047834.
[8] L. Gupta, M. Samaka, R. Jain, A. Erbad, D. Bhamare and C. Metz, "COLAP: A predictive framework for service function chain placement in a multi-cloud environment," IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2017, pp. 1-9, doi: 10.1109/CCWC.2017.7868377.
[9] H. Ko, D. Suh, H. Baek, S. Pack and J. Kwak, "Optimal placement of service function in service function chaining," Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, Austria, 2016, pp. 102-105, doi: 10.1109/ICUFN.2016.7536993.
[10] Z. Allybokus, N. Perrot, J. Leguay, L. Maggi and E. Gourdin, "Virtual function placement for service chaining with partial orders and anti‐affinity rules," Networks, vol. 71, no. 2, pp. 97-106, 2018, doi: 10.1002/net.21768.
[11] Y. Xiao et al., "NFVdeep: Adaptive Online Service Function Chain Deployment with Deep Reinforcement Learning," IEEE/ACM 27th International Symposium on Quality of Service (IWQoS), Phoenix, AZ, USA, 2019, pp. 1-10, doi: 10.1145/3326285.3329056.
[12] J. Pei, P. Hong, K. Xue and D. Li, "Efficiently Embedding Service Function Chains with Dynamic Virtual Network Function Placement in Geo-Distributed Cloud System," in IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 10, pp. 2179-2192, 1 Oct. 2019, doi: 10.1109/TPDS.2018.2880992.
[13] R. S. Ponmagal, S. Karthick, B. Dhiyanesh, S. Balakrishnan and K. Venkatachalam, "Optimized virtual network function provisioning technique for mobile edge cloud computing," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 6, pp. 5807-5815, 2021, doi: 10.1007/s12652-020-02122-8.
[14] K. S. Ghai, S. Choudhury and A. Yassine, "Efficient algorithms to minimize the end-to-end latency of edge network function virtualization," Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 10, pp. 3963-3974, 2020, .
[15] S. Yang, F. Li, S. Trajanovski, X. Chen, Y. Wang and X. Fu, "Delay-Aware Virtual Network Function Placement and Routing in Edge Clouds," in IEEE Transactions on Mobile Computing, vol. 20, no. 2, pp. 445-459, 1 Feb. 2021, doi: 10.1109/TMC.2019.2942306.
[16] T. Subramanya, D. Harutyunyan and R. Riggio, "Machine learning-driven service function chain placement and scaling in MEC-enabled 5G networks," Computer Networks, vol. 166, p. 106980, 2020, doi: 10.1016/j.comnet.2019.106980.
[17] H. Chen, X. Wang, Y. Zhao, T. Song, Y. Wang, S. Xu and L. Li, "MOSC: A method to assign the outsourcing of service function chain across multiple clouds," Computer Networks, vol. 133, pp. 166-182, 2018, doi: 10.1016/j.comnet.2018.01.020.
[18] D. Bhamare, A. Erbad, R. Jain, M. Zolanvari and M. Samaka, "Efficient virtual network function placement strategies for cloud radio access networks," Computer Communications, vol. 127, pp. 50-60, 2018, doi: 10.1016/j.comcom.2018.05.004.
[19] Y. Xie, S. Wang and Y. Dai, "Revenue-maximizing virtualized network function chain placement in dynamic environment," Future Generation Computer Systems, vol. 108, pp. 650-661, 2020, doi: 10.1016/j.future.2020.03.011.
[20] R. Kouah, A. Alleg, A. Laraba and T. Ahmed, "Energy-Aware Placement for IoT-Service Function Chain," IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Barcelona, Spain, 2018, pp. 1-7, doi: 10.1109/CAMAD.2018.8515003.
[21] Y. Liu, H. Lu, X. Li, Y. Zhang, L. Xi and D. Zhao, "Dynamic Service Function Chain Orchestration for NFV/MEC-Enabled IoT Networks: A Deep Reinforcement Learning Approach," in IEEE Internet of Things Journal, vol. 8, no. 9, pp. 7450-7465, 1 May1, 2021, doi: 10.1109/JIOT.2020.3038793.
[22] L. Gu, D. Zeng, W. Li, S. Guo, A. Y. Zomaya and H. Jin, "Intelligent VNF Orchestration and Flow Scheduling via Model-Assisted Deep Reinforcement Learning," in IEEE Journal on Selected Areas in Communications, vol. 38, no. 2, pp. 279-291, Feb. 2020, doi: 10.1109/JSAC.2019.2959182.
[23] G. Li, B. Feng, H. Zhou, Y. Zhang, K. Sood and S. Yu, "Adaptive service function chaining mappings in 5G using deep Q-learning," Computer Communications, vol. 152, pp. 305-315, 2020, doi: 10.1016/j.comcom.2020.01.035.
[24] R. Solozabal, J. Ceberio, A. Sanchoyerto, L. Zabala, B. Blanco and F. Liberal, "Virtual Network Function Placement Optimization With Deep Reinforcement Learning," in IEEE Journal on Selected Areas in Communications, vol. 38, no. 2, pp. 292-303, Feb. 2020, doi: 10.1109/JSAC.2019.2959183.
[25] J. Zheng et al., "Optimizing NFV Chain Deployment in Software-Defined Cellular Core," in IEEE Journal on Selected Areas in Communications, vol. 38, no. 2, pp. 248-262, Feb. 2020, doi: 10.1109/JSAC.2019.2959180.
[26] J. Sun, G. Huang, G. Sun, H. Yu, A. K. Sangaiah and V. Chang, "A Q-learning-based approach for deploying dynamic service function chains," Symmetry, vol. 10, no. 11, p. 646, doi: 10.3390/sym10110646.
[27] T. Wang et al., "DRL-SFCP: Adaptive Service Function Chains Placement with Deep Reinforcement Learning," ICC 2021 - IEEE International Conference on Communications, Montreal, QC, Canada, 2021, pp. 1-6, doi: 10.1109/ICC42927.2021.9500964.
[28] L. Wang, W. Mao, J. Zhao and Y. Xu, "DDQP: A Double Deep Q-Learning Approach to Online Fault-Tolerant SFC Placement," in IEEE Transactions on Network and Service Management, vol. 18, no. 1, pp. 118-132, March 2021, doi: 10.1109/TNSM.2021.3049298.
[29] G. L. Santos, D. D. F. Bezerra, E. D. S. Rocha, L. Ferreira, A. L. C. Moreira, G. E. Gonçalves and P. T. Endo, "Service function chain placement in distributed scenarios: a systematic review," Journal of Network and Systems Management, vol. 30, no. 4, pp. 1-39, 2022, doi: 10.1007/s10922-021-09626-4.
[30] J. Cai, Z. Huang, J. Luo, Y. Liu, H. Zhao and L. Liao, "Composing and deploying parallelized service function chains," Journal of Network and Computer Applications, vol. 163, p. 102637, 2020, doi: 10.1016/j.jnca.2020.102637.
[31] M. Zeng, W. Fang and Z. Zhu, "Orchestrating Tree-Type VNF Forwarding Graphs in Inter-DC Elastic Optical Networks," in Journal of Lightwave Technology, vol. 34, no. 14, pp. 3330-3341, July 2016, doi: 10.1109/JLT.2016.2565002.
_||_[1] W. Li, H. Wang, X. Zhang, D. Li, L. Yan, Q. Fan and R. Yao, "Security Service Function Chain Based on Graph Neural Network," Information, vol. 13, no. 2, p. 78, 2022, doi: 10.3390/info13020078.
[2] K. Kaur, V. Mangat and K. Kumar, "A comprehensive survey of service function chain provisioning approaches in SDN and NFV architecture," Computer Science Review, vol. 38, p. 100298, 2020, doi: 10.1016/j.cosrev.2020.100298.
[3] M. Mechtri, C. Ghribi and D. Zeghlache, "A Scalable Algorithm for the Placement of Service Function Chains," in IEEE Transactions on Network and Service Management, vol. 13, no. 3, pp. 533-546, Sept. 2016, doi: 10.1109/TNSM.2016.2598068.
[4] A. Tomassilli, F. Giroire, N. Huin and S. Pérennes, "Provably Efficient Algorithms for Placement of Service Function Chains with Ordering Constraints," IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, Honolulu, HI, USA, 2018, pp. 774-782, doi: 10.1109/INFOCOM.2018.8486275.
[5] S. Zhang, W. Jia, Z. Tang, J. Lou and W. Zhao, "Efficient instance reuse approach for service function chain placement in mobile edge computing," Computer Networks, vol. 211, p. 109010, 2022, doi: 10.1016/j.comnet.2022.109010.
[6] K. Yang, H. Zhang and P. Hong, "Energy-Aware Service Function Placement for Service Function Chaining in Data Centers," IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA, 2016, pp. 1-6, doi: 10.1109/GLOCOM.2016.7841805.
[7] S. Xie, J. Ma and J. Zhao, "FlexChain: Bridging Parallelism and Placement for Service Function Chains," in IEEE Transactions on Network and Service Management, vol. 18, no. 1, pp. 195-208, March 2021, doi: 10.1109/TNSM.2020.3047834.
[8] L. Gupta, M. Samaka, R. Jain, A. Erbad, D. Bhamare and C. Metz, "COLAP: A predictive framework for service function chain placement in a multi-cloud environment," IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2017, pp. 1-9, doi: 10.1109/CCWC.2017.7868377.
[9] H. Ko, D. Suh, H. Baek, S. Pack and J. Kwak, "Optimal placement of service function in service function chaining," Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, Austria, 2016, pp. 102-105, doi: 10.1109/ICUFN.2016.7536993.
[10] Z. Allybokus, N. Perrot, J. Leguay, L. Maggi and E. Gourdin, "Virtual function placement for service chaining with partial orders and anti‐affinity rules," Networks, vol. 71, no. 2, pp. 97-106, 2018, doi: 10.1002/net.21768.
[11] Y. Xiao et al., "NFVdeep: Adaptive Online Service Function Chain Deployment with Deep Reinforcement Learning," IEEE/ACM 27th International Symposium on Quality of Service (IWQoS), Phoenix, AZ, USA, 2019, pp. 1-10, doi: 10.1145/3326285.3329056.
[12] J. Pei, P. Hong, K. Xue and D. Li, "Efficiently Embedding Service Function Chains with Dynamic Virtual Network Function Placement in Geo-Distributed Cloud System," in IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 10, pp. 2179-2192, 1 Oct. 2019, doi: 10.1109/TPDS.2018.2880992.
[13] R. S. Ponmagal, S. Karthick, B. Dhiyanesh, S. Balakrishnan and K. Venkatachalam, "Optimized virtual network function provisioning technique for mobile edge cloud computing," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 6, pp. 5807-5815, 2021, doi: 10.1007/s12652-020-02122-8.
[14] K. S. Ghai, S. Choudhury and A. Yassine, "Efficient algorithms to minimize the end-to-end latency of edge network function virtualization," Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 10, pp. 3963-3974, 2020, .
[15] S. Yang, F. Li, S. Trajanovski, X. Chen, Y. Wang and X. Fu, "Delay-Aware Virtual Network Function Placement and Routing in Edge Clouds," in IEEE Transactions on Mobile Computing, vol. 20, no. 2, pp. 445-459, 1 Feb. 2021, doi: 10.1109/TMC.2019.2942306.
[16] T. Subramanya, D. Harutyunyan and R. Riggio, "Machine learning-driven service function chain placement and scaling in MEC-enabled 5G networks," Computer Networks, vol. 166, p. 106980, 2020, doi: 10.1016/j.comnet.2019.106980.
[17] H. Chen, X. Wang, Y. Zhao, T. Song, Y. Wang, S. Xu and L. Li, "MOSC: A method to assign the outsourcing of service function chain across multiple clouds," Computer Networks, vol. 133, pp. 166-182, 2018, doi: 10.1016/j.comnet.2018.01.020.
[18] D. Bhamare, A. Erbad, R. Jain, M. Zolanvari and M. Samaka, "Efficient virtual network function placement strategies for cloud radio access networks," Computer Communications, vol. 127, pp. 50-60, 2018, doi: 10.1016/j.comcom.2018.05.004.
[19] Y. Xie, S. Wang and Y. Dai, "Revenue-maximizing virtualized network function chain placement in dynamic environment," Future Generation Computer Systems, vol. 108, pp. 650-661, 2020, doi: 10.1016/j.future.2020.03.011.
[20] R. Kouah, A. Alleg, A. Laraba and T. Ahmed, "Energy-Aware Placement for IoT-Service Function Chain," IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Barcelona, Spain, 2018, pp. 1-7, doi: 10.1109/CAMAD.2018.8515003.
[21] Y. Liu, H. Lu, X. Li, Y. Zhang, L. Xi and D. Zhao, "Dynamic Service Function Chain Orchestration for NFV/MEC-Enabled IoT Networks: A Deep Reinforcement Learning Approach," in IEEE Internet of Things Journal, vol. 8, no. 9, pp. 7450-7465, 1 May1, 2021, doi: 10.1109/JIOT.2020.3038793.
[22] L. Gu, D. Zeng, W. Li, S. Guo, A. Y. Zomaya and H. Jin, "Intelligent VNF Orchestration and Flow Scheduling via Model-Assisted Deep Reinforcement Learning," in IEEE Journal on Selected Areas in Communications, vol. 38, no. 2, pp. 279-291, Feb. 2020, doi: 10.1109/JSAC.2019.2959182.
[23] G. Li, B. Feng, H. Zhou, Y. Zhang, K. Sood and S. Yu, "Adaptive service function chaining mappings in 5G using deep Q-learning," Computer Communications, vol. 152, pp. 305-315, 2020, doi: 10.1016/j.comcom.2020.01.035.
[24] R. Solozabal, J. Ceberio, A. Sanchoyerto, L. Zabala, B. Blanco and F. Liberal, "Virtual Network Function Placement Optimization With Deep Reinforcement Learning," in IEEE Journal on Selected Areas in Communications, vol. 38, no. 2, pp. 292-303, Feb. 2020, doi: 10.1109/JSAC.2019.2959183.
[25] J. Zheng et al., "Optimizing NFV Chain Deployment in Software-Defined Cellular Core," in IEEE Journal on Selected Areas in Communications, vol. 38, no. 2, pp. 248-262, Feb. 2020, doi: 10.1109/JSAC.2019.2959180.
[26] J. Sun, G. Huang, G. Sun, H. Yu, A. K. Sangaiah and V. Chang, "A Q-learning-based approach for deploying dynamic service function chains," Symmetry, vol. 10, no. 11, p. 646, doi: 10.3390/sym10110646.
[27] T. Wang et al., "DRL-SFCP: Adaptive Service Function Chains Placement with Deep Reinforcement Learning," ICC 2021 - IEEE International Conference on Communications, Montreal, QC, Canada, 2021, pp. 1-6, doi: 10.1109/ICC42927.2021.9500964.
[28] L. Wang, W. Mao, J. Zhao and Y. Xu, "DDQP: A Double Deep Q-Learning Approach to Online Fault-Tolerant SFC Placement," in IEEE Transactions on Network and Service Management, vol. 18, no. 1, pp. 118-132, March 2021, doi: 10.1109/TNSM.2021.3049298.
[29] G. L. Santos, D. D. F. Bezerra, E. D. S. Rocha, L. Ferreira, A. L. C. Moreira, G. E. Gonçalves and P. T. Endo, "Service function chain placement in distributed scenarios: a systematic review," Journal of Network and Systems Management, vol. 30, no. 4, pp. 1-39, 2022, doi: 10.1007/s10922-021-09626-4.
[30] J. Cai, Z. Huang, J. Luo, Y. Liu, H. Zhao and L. Liao, "Composing and deploying parallelized service function chains," Journal of Network and Computer Applications, vol. 163, p. 102637, 2020, doi: 10.1016/j.jnca.2020.102637.
[31] M. Zeng, W. Fang and Z. Zhu, "Orchestrating Tree-Type VNF Forwarding Graphs in Inter-DC Elastic Optical Networks," in Journal of Lightwave Technology, vol. 34, no. 14, pp. 3330-3341, July 2016, doi: 10.1109/JLT.2016.2565002.