AI-Assisted Framework for Green-Routing and Load Balancing in Hybrid Software-Defined Networking: Proposal, Challenges and Future Perspective

The explosive growth of IP networks, the advent of cloud computing, and the rapid progress in wireless communications witnessed today reflect significant progress towards meeting the explosive data traffic demands. Consequently, communications service providers should deploy efficient and intelligent network solutions to accommodate the huge traffic demands and to ease the capacity pressure on their network infrastructure. Besides, vendors should develop novel energy-efficient networks to reduce network utility costs and carbon footprint. Software-defined networking (SDN) provides a suitable solution, however, complete SDN deployment is currently unachievable in the short-term. An alternative is the hybrid SDN/ open shortest path forwarding (OSPF) network, which allows the deployment of SDN in legacy networks. Nevertheless, hybrid SDN/OSPF also faces several technical, economic and organizational challenges. Although many energy-efficiency routing solutions exist in hybrid SDN/OSPF networks, they are generic and reactive by design. Moreover, these solutions are characterized by manual control plane forwarding configurations, leading to sub-optimal performance. The recent promising combination of SDN and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) in traffic management and control provides tremendous opportunities. In this paper, we first provide a review of the most recent optimization approaches for global energy-efficient routing and load balancing. Next, we investigate a scalable and intelligent integrated architectural framework that leverages deep reinforcement learning (DRL) techniques to realize predictive and rate adaptive energy-efficient routing with guaranteed quality of service (QoS), in transitional hybrid SDN/OSPF networks. Based on the need to minimize global network energy consumption and improve link performance, this paper provides key research insights into the current progress in hybrid SDN/OSPF, ML and AI in the hope of stimulating more research.

[1]  Patrick Siarry,et al.  A survey on optimization metaheuristics , 2013, Inf. Sci..

[2]  Kenneth J. Christensen,et al.  Managing energy consumption costs in desktop PCs and LAN switches with proxying, split TCP connections, and scaling of link speed , 2005, Int. J. Netw. Manag..

[3]  Xin-She Yang,et al.  Metaheuristic Optimization: Algorithm Analysis and Open Problems , 2011, SEA.

[4]  Erol Gelenbe,et al.  Routing and G-Networks to Optimise Energy and Quality of Service in Packet Networks , 2010, ICST E-Energy.

[5]  Jaime Lloret,et al.  Including artificial intelligence in a routing protocol using Software Defined Networks , 2017, 2017 IEEE International Conference on Communications Workshops (ICC Workshops).

[6]  Barry E. Mullins,et al.  SDN shim: Controlling legacy devices , 2015, 2015 IEEE 40th Conference on Local Computer Networks (LCN).

[7]  R. Srinivasa Rao,et al.  Optimization of Distribution Network Configuration for Loss Reduction Using Artificial Bee Colony Algorithm , 2008 .

[8]  Zhitang Chen,et al.  Online flow size prediction for improved network routing , 2016, 2016 IEEE 24th International Conference on Network Protocols (ICNP).

[9]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[10]  Yusheng Ji,et al.  Understanding the Modeling of Computer Network Delays using Neural Networks , 2018, Big-DAMA@SIGCOMM.

[11]  Zuqing Zhu,et al.  Programmable Multilayer INT: An Enabler for AI-Assisted Network Automation , 2020, IEEE Communications Magazine.

[12]  Nei Kato,et al.  Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning , 2017, IEEE Transactions on Computers.

[13]  Marivi Higuero,et al.  A Survey on the Contributions of Software-Defined Networking to Traffic Engineering , 2017, IEEE Communications Surveys & Tutorials.

[14]  Lei Wang,et al.  Intelligent path control for energy-saving in hybrid SDN networks , 2018, Comput. Networks.

[15]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[16]  P. Varalakshmi,et al.  Congestion Control Mechanism in Software Defined Networking by Traffic Rerouting , 2018, 2018 Second International Conference on Computing Methodologies and Communication (ICCMC).

[17]  Umme Zakia,et al.  Dynamic load balancing in SDN-based data center networks , 2017, 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).

[18]  Daniel Pérez Palomar,et al.  A tutorial on decomposition methods for network utility maximization , 2006, IEEE Journal on Selected Areas in Communications.

[19]  Min Chen,et al.  TIDE: Time-relevant deep reinforcement learning for routing optimization , 2019, Future Gener. Comput. Syst..

[20]  Zachary Chase Lipton.  A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.

[21]  Chih-Wei Huang,et al.  A study of deep learning networks on mobile traffic forecasting , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[22]  Nei Kato,et al.  A Novel Non-Supervised Deep-Learning-Based Network Traffic Control Method for Software Defined Wireless Networks , 2018, IEEE Wireless Communications.

[23]  Mohamed Cheriet,et al.  Multiple-Step-Ahead Traffic Prediction in High-Speed Networks , 2018, IEEE Communications Letters.

[24]  Nei Kato,et al.  State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems , 2017, IEEE Communications Surveys & Tutorials.

[25]  Albert Cabellos-Aparicio,et al.  Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case. , 2020 .

[26]  Franco Davoli,et al.  Energy Efficiency in the Future Internet: A Survey of Existing Approaches and Trends in Energy-Aware Fixed Network Infrastructures , 2011, IEEE Communications Surveys & Tutorials.

[27]  Yusheng Ji,et al.  Deep Convolutional LSTM Network-based Traffic Matrix Prediction with Partial Information , 2019, 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM).

[28]  Kee-Eung Kim,et al.  Learning to Cooperate via Policy Search , 2000, UAI.

[29]  Didem Gözüpek,et al.  A survey on energy efficiency in software defined networks , 2017, Comput. Networks.

[30]  Michael Negnevitsky,et al.  Artificial Intelligence: A Guide to Intelligent Systems , 2001 .

[31]  Young-Jin Kim,et al.  Software-defined traffic load balancing for cost-effective data center interconnection service , 2017, 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM).

[32]  Lamia Chaari,et al.  Energy-Aware Routing in Carrier-Grade Ethernet Using SDN Approach , 2018, IEEE Transactions on Green Communications and Networking.

[33]  Albert-László Barabási,et al.  Controllability of complex networks , 2011, Nature.

[34]  Song Guo,et al.  AI Routers & Network Mind: A Hybrid Machine Learning Paradigm for Packet Routing , 2019, IEEE Computational Intelligence Magazine.

[35]  Wei Song,et al.  Achieving near-optimal traffic engineering in hybrid Software Defined Networks , 2015, 2015 IFIP Networking Conference (IFIP Networking).

[36]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[37]  Rym M'Hallah,et al.  Energy-aware routing for software-defined networks with discrete link rates: A benders decomposition-based heuristic approach , 2017, Sustain. Comput. Informatics Syst..

[38]  Lena Wosinska,et al.  Energy-Efficient Design of Survivable WDM Networks with Shared Backup , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[39]  Adlen Ksentini,et al.  Improving Traffic Forecasting for 5G Core Network Scalability: A Machine Learning Approach , 2018, IEEE Network.

[40]  Yang Xu,et al.  SINET: Enabling Scalable Network Routing with Deep Reinforcement Learning on Partial Nodes , 2019, SIGCOMM Posters and Demos.

[41]  Phone Lin,et al.  Modeling Energy Saving Mechanism for Green Routers , 2018, IEEE Transactions on Green Communications and Networking.

[42]  Dino Farinacci,et al.  MPLS Label Stack Encoding , 2001, RFC.

[43]  Keqin Li,et al.  An Artificial Neural Network Approach to Power Consumption Model Construction for Servers in Cloud Data Centers , 2020, IEEE Transactions on Sustainable Computing.

[44]  Jamil Salem Barbar,et al.  Computer network traffic prediction: a comparison between traditional and deep learning neural networks , 2015, Int. J. Big Data Intell..

[45]  Lei Xie,et al.  Energy-aware traffic engineering in hybrid SDN/IP backbone networks , 2016, Journal of Communications and Networks.

[46]  Nei Kato,et al.  An Intelligent Traffic Load Prediction-Based Adaptive Channel Assignment Algorithm in SDN-IoT: A Deep Learning Approach , 2018, IEEE Internet of Things Journal.

[47]  L. D. Dhinesh Babu,et al.  Honey bee behavior inspired load balancing of tasks in cloud computing environments , 2013, Appl. Soft Comput..

[48]  Lei Luo,et al.  Poster Abstract: Deep Learning Workloads Scheduling with Reinforcement Learning on GPU Clusters , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[49]  Raouf Boutaba,et al.  A comprehensive survey on machine learning for networking: evolution, applications and research opportunities , 2018, Journal of Internet Services and Applications.

[50]  Yi Wu,et al.  Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments , 2017, NIPS.

[51]  Olivier Bonaventure,et al.  On the co-existence of distributed and centralized routing control-planes , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[52]  Luca Maria Gambardella,et al.  Flexible, High Performance Convolutional Neural Networks for Image Classification , 2011, IJCAI.

[53]  Kotaro Kataoka,et al.  AMPS: Application aware multipath flow routing using machine learning in SDN , 2017, 2017 Twenty-third National Conference on Communications (NCC).

[54]  F. Richard Yu,et al.  A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges , 2019, IEEE Communications Surveys & Tutorials.

[55]  Myriana Rifai,et al.  Bringing Energy Aware Routing Closer to Reality with SDN Hybrid Networks , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[56]  Albert Cabellos-Aparicio,et al.  A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization , 2017, ArXiv.

[57]  Eduard Alarcón,et al.  Machine learning-based network modeling: An artificial neural network model vs a theoretical inspired model , 2017, 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN).

[58]  Lizy Kurian John,et al.  Efficient Prediction of Network Traffic for Real-Time Applications , 2019, J. Comput. Networks Commun..

[59]  Hao Li.  Traffic scheduling in software-defined backhaul network , 2018 .

[60]  Jie Wu,et al.  Saving Energy in Partially Deployed Software Defined Networks , 2016, IEEE Transactions on Computers.

[61]  Hua Qu,et al.  Towards traffic matrix prediction with LSTM recurrent neural networks , 2018 .

[62]  Majd Latah,et al.  Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview , 2018, IET Networks.

[63]  Haipeng Yao,et al.  NetworkAI: An Intelligent Network Architecture for Self-Learning Control Strategies in Software Defined Networks , 2018, IEEE Internet of Things Journal.

[64]  Sandhya,et al.  A survey: Hybrid SDN , 2017, J. Netw. Comput. Appl..

[65]  Julong Lan,et al.  EARS: Intelligence-driven experiential network architecture for automatic routing in software-defined networking , 2020, China Communications.

[66]  Admela Jukan,et al.  Divide and conquer: Partitioning OSPF networks with SDN , 2014, 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM).

[67]  Marco Polverini,et al.  A Survey on Energy-Aware Design and Operation of Core Networks , 2016, IEEE Communications Surveys & Tutorials.

[68]  Seungmin Rho,et al.  Traffic engineering in software-defined networking: Measurement and management , 2016, IEEE Access.

[69]  Pu Wang,et al.  Delay-Optimal Traffic Engineering through Multi-agent Reinforcement Learning , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[70]  Markus Hidell,et al.  Can Energy-Aware Routing Improve the Energy Savings of Energy-Efficient Ethernet? , 2018, IEEE Transactions on Green Communications and Networking.

[71]  Latha R. Nair,et al.  Data mining in software defined networking - a survey , 2017, 2017 International Conference on Computing Methodologies and Communication (ICCMC).

[72]  Julong Lan,et al.  DROM: Optimizing the Routing in Software-Defined Networks With Deep Reinforcement Learning , 2018, IEEE Access.

[73]  Nei Kato,et al.  On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control , 2018, IEEE Wireless Communications.

[74]  Cristina Cervello-Pastor,et al.  Achieving Energy Efficiency: An Energy-Aware Approach in SDN , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[75]  Xinchang Zhang,et al.  A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning , 2019, IEEE Access.

[76]  Olivier Bonaventure,et al.  Opportunities and research challenges of hybrid software defined networks , 2014, CCRV.

[77]  Sujata Banerjee,et al.  Incremental Deployment of SDN in Hybrid Enterprise and ISP Networks , 2016, SOSR.

[78]  Nadir Shah,et al.  Hybrid SDN Networks: A Survey of Existing Approaches , 2018, IEEE Communications Surveys & Tutorials.

[79]  Wai-xi Liu.  Intelligent Routing based on Deep Reinforcement Learning in Software-Defined Data-Center Networks , 2019, 2019 IEEE Symposium on Computers and Communications (ISCC).

[80]  Casimer DeCusatis,et al.  Predicting network attack patterns in SDN using machine learning approach , 2016, 2016 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN).

[81]  Marco Mellia,et al.  Reducing Power Consumption in Backbone Networks , 2009, 2009 IEEE International Conference on Communications.

[82]  George N. Rouskas,et al.  Power Efficient Traffic Grooming in Optical WDM Networks , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[83]  Mehdi Bennis,et al.  Wireless Network Intelligence at the Edge , 2018, Proceedings of the IEEE.

[84]  Jianping Wu,et al.  Traffic Engineering in SDN/OSPF Hybrid Network , 2014, 2014 IEEE 22nd International Conference on Network Protocols.

[85]  Murali S. Kodialam,et al.  Traffic engineering in software defined networks , 2013, 2013 Proceedings IEEE INFOCOM.

[86]  Paola Grosso,et al.  An Online Power-Aware Routing in SDN with Congestion-Avoidance Traffic Reallocation , 2018, 2018 IFIP Networking Conference (IFIP Networking) and Workshops.

[87]  Tianshu Wei,et al.  Deep reinforcement learning: Framework, applications, and embedded implementations: Invited paper , 2017, 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[88]  Jin Wei,et al.  Reinforcement Learning-Driven QoS-Aware Intelligent Routing for Software-Defined Networks , 2019, 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[89]  Prashant Kaushik,et al.  Traffic Prediction in Telecom Systems Using Deep Learning , 2018, 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO).

[90]  Jadwiga Indulska,et al.  Efficient topology discovery in OpenFlow-based Software Defined Networks , 2016, Comput. Commun..

[91]  Orhan Gemikonakli,et al.  LearnQoS: A Learning Approach for Optimizing QoS Over Multimedia-Based SDNs , 2018, 2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB).

[92]  M. Maksimovic,et al.  Greening the Future: Green Internet of Things (G-IoT) as a Key Technological Enabler of Sustainable Development , 2018 .

[93]  Xirong Que,et al.  Maximizing Network Utilization in Hybrid Software-Defined Networks , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[94]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[95]  Guy Pujolle,et al.  NeuTM: A neural network-based framework for traffic matrix prediction in SDN , 2017, NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium.

[96]  Sudip Misra,et al.  Sway: Traffic-Aware QoS Routing in Software-Defined IoT , 2018, IEEE Transactions on Emerging Topics in Computing.

[97]  Rolf Stadler,et al.  Learning end-to-end application QoS from openflow switch statistics , 2017, 2017 IEEE Conference on Network Softwarization (NetSoft).

[98]  Abdelkader Outtagarts,et al.  Deep Reinforcement Learning Based QoS-Aware Routing in Knowledge-Defined Networking , 2018, QSHINE.

[99]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[100]  Federico Tramarin,et al.  Energy Efficient Ethernet for Real-Time Industrial Networks , 2015, IEEE Transactions on Automation Science and Engineering.

[101]  Yanhua Zhang,et al.  Deep Q-Learning for Routing Schemes in SDN-Based Data Center Networks , 2020, IEEE Access.

[102]  Jean C. Walrand,et al.  Knowledge-Defined Networking: Modelització de la xarxa a través de l’aprenentatge automàtic i la inferència , 2016 .

[103]  Yishay Mansour,et al.  Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.

[104]  Ejder Bastug,et al.  Hierarchical Deep Double Q-Routing , 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[105]  Mohamed Cheriet,et al.  Embedding Multiple-Step-Ahead Traffic Prediction in Network Energy Efficiency Problem , 2019, 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC).

[106]  Dervis Karaboga,et al.  A survey: algorithms simulating bee swarm intelligence , 2009, Artificial Intelligence Review.

[107]  Adriana Fernández-Fernández,et al.  A Multi-Objective Routing Strategy for QoS and Energy Awareness in Software-Defined Networks , 2017, IEEE Communications Letters.

[108]  Viktor K. Prasanna,et al.  Deep Learning Models For Aggregated Network Traffic Prediction , 2019, 2019 15th International Conference on Network and Service Management (CNSM).

[109]  H. Jonathan Chao,et al.  Congestion-aware single link failure recovery in hybrid SDN networks , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[110]  Ying-Chang Liang,et al.  Applications of Deep Reinforcement Learning in Communications and Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[111]  Guy Pujolle,et al.  NeuRoute: Predictive dynamic routing for software-defined networks , 2017, 2017 13th International Conference on Network and Service Management (CNSM).

[112]  Reza Nejabati,et al.  Multilayer network analytics with SDN-based monitoring framework , 2017, IEEE/OSA Journal of Optical Communications and Networking.

[113]  Léon Bottou,et al.  Wasserstein GAN , 2017, ArXiv.

[114]  Xin Wang,et al.  Machine Learning for Networking: Workflow, Advances and Opportunities , 2017, IEEE Network.

[115]  Zhitang Chen,et al.  Predicting future traffic using Hidden Markov Models , 2016, 2016 IEEE 24th International Conference on Network Protocols (ICNP).

[116]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[117]  Simon Fong,et al.  Recent advances in metaheuristic algorithms: Does the Makara dragon exist? , 2016, The Journal of Supercomputing.

[118]  Hai Yang,et al.  Network Traffic Prediction Based on LSTM Networks with Genetic Algorithm , 2018, Lecture Notes in Electrical Engineering.

[119]  Joseph Nygate,et al.  Applying big data technologies to manage QoS in an SDN , 2016, 2016 12th International Conference on Network and Service Management (CNSM).

[120]  Hyuck M. Kwon,et al.  Utility-Optimal Wireless Routing in the Presence of Heavy Tails , 2018, IEEE Transactions on Vehicular Technology.

[121]  Yuanguo Bi,et al.  Intelligent Quality of Service Aware Traffic Forwarding for Software-Defined Networking/Open Shortest Path First Hybrid Industrial Internet , 2020, IEEE Transactions on Industrial Informatics.

[122]  Junjie Zhang,et al.  CFR-RL: Traffic Engineering With Reinforcement Learning in SDN , 2020, IEEE Journal on Selected Areas in Communications.

[123]  Luca Chiaraviglio,et al.  Optimal Energy Management of UAV-Based Cellular Networks Powered by Solar Panels and Batteries: Formulation and Solutions , 2019, IEEE Access.

[124]  Tu-Liang Lin,et al.  A Parameterized Wildcard Method Based on SDN for Server Load Balancing , 2016, 2016 International Conference on Networking and Network Applications (NaNA).

[125]  Öznur Özkasap,et al.  A Classification and Survey of Energy Efficient Methods in Software Defined Networking , 2018, J. Netw. Comput. Appl..

[126]  Alan D. George,et al.  The next frontier for communications networks: power management , 2004, Comput. Commun..

[127]  Öznur Özkasap,et al.  Framework for Traffic Proportional Energy Efficiency in Software Defined Networks , 2018, 2018 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom).

[128]  Pu Wang,et al.  Distributed Multi-Hop Traffic Engineering via Stochastic Policy Gradient Reinforcement Learning , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[129]  Hamed S. Al-Raweshidy,et al.  Optimisation of Software-Defined Networks Performance Using a Hybrid Intelligent System , 2017 .

[130]  Guido Maier,et al.  Machine-Learning-Based Prediction and Optimization of Mobile Metro-Core Networks , 2018, 2018 IEEE Photonics Society Summer Topical Meeting Series (SUM).

[131]  Jialiang Liu,et al.  Inclusion of artificial intelligence in communication networks and services , 2017 .

[132]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.

[133]  Admela Jukan,et al.  SDN Partitioning: A Centralized Control Plane for Distributed Routing Protocols , 2016, IEEE Transactions on Network and Service Management.

[134]  Dafna Shahaf,et al.  A Machine Learning Approach to Routing , 2017, ArXiv.

[135]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 2005, IEEE Transactions on Neural Networks.

[136]  Nen-Fu Huang,et al.  Application identification system for SDN QoS based on machine learning and DNS responses , 2017, 2017 19th Asia-Pacific Network Operations and Management Symposium (APNOMS).

[137]  Ken Christensen,et al.  A "Green TCP/IP" to reduce electricity consumed by computers , 1998, Proceedings IEEE Southeastcon '98 'Engineering for a New Era'.

[138]  Kuochen Wang,et al.  A QoS-aware routing in SDN hybrid networks , 2017, FNC/MobiSPC.

[139]  Victor R. Lesser,et al.  Communication decisions in multi-agent cooperation: model and experiments , 2001, AGENTS '01.

[140]  Bernhard Walke,et al.  IEEE 802.11s: The WLAN Mesh Standard , 2010, IEEE Wireless Communications.

[141]  Ian F. Akyildiz,et al.  A roadmap for traffic engineering in SDN-OpenFlow networks , 2014, Comput. Networks.

[142]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[143]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[144]  Kun Cao,et al.  A Survey of Deployment Solutions and Optimization Strategies for Hybrid SDN Networks , 2019, IEEE Communications Surveys & Tutorials.

[145]  Mohamed Cheriet,et al.  Greening The Network Using Traffic Prediction and Link Rate Adaptation , 2019, 2019 IEEE Sustainability through ICT Summit (StICT).

[146]  Jing Ren,et al.  Enhancing Traffic Engineering Performance and Flow Manageability in Hybrid SDN , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).