NFV/SDN Enabled Architecture for Efficient Adaptive Management of Renewable and Non-Renewable Energy

Ever-increasing energy consumption, the depletion of non-renewable resources, the climate impact associated with energy generation, and finite energy-production capacity are important concerns that drive the urgent creation of new solutions for energy management. In this regard, by leveraging the massive connectivity provided by emerging 5G communications, this paper proposes a long-term sustainable Demand-Response (DR) architecture for the efficient management of available energy consumption for Internet of Things (IoT) infrastructures. The proposal uses Network Functions Virtualization (NFV) and Software Defined Networking (SDN) technologies as enablers and promotes the primary use of energy from renewable sources. Associated with architecture, this paper presents a novel consumption model conditioned on availability and in which the consumers are part of the management process. To efficiently use the energy from renewable and non-renewable sources, several management strategies are herein proposed, such as prioritization of the energy supply and workload scheduling using time-shifting capabilities. The complexity of the proposal is analyzed in order to present an appropriate architectural framework. The energy management solution is modeled as an Integer Linear Programming (ILP) and, to verify the improvements in energy utilization, an algorithmic solution and its evaluation are presented. Finally, open research problems and application scenarios are discussed.

[1]  Dimitrios P. Pezaros,et al.  Container Network Functions: Bringing NFV to the Network Edge , 2017, IEEE Communications Magazine.

[2]  Pierluigi Siano,et al.  A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems , 2015, IEEE Transactions on Industrial Electronics.

[3]  Sonja Klingert,et al.  Greensdas leveraging power adaption collaboration between energy provider and data centres , 2013, 2013 Sustainable Internet and ICT for Sustainability (SustainIT).

[4]  Fernando M. V. Ramos,et al.  Software-Defined Networking: A Comprehensive Survey , 2014, Proceedings of the IEEE.

[5]  Shaolei Ren,et al.  A Truthful Incentive Mechanism for Emergency Demand Response in Geo-Distributed Colocation Data Centers , 2016, ACM Trans. Model. Perform. Evaluation Comput. Syst..

[6]  Sonja Klingert,et al.  Renewable energy-aware data centre operations for smart cities the DC4Cities approach , 2015, 2015 International Conference on Smart Cities and Green ICT Systems (SMARTGREENS).

[7]  Shiwen Mao,et al.  Distributed Online Algorithm for Optimal Real-Time Energy Distribution in the Smart Grid , 2013, IEEE Internet of Things Journal.

[8]  Hakki C. Cankaya Software Defined Networking and Virtualization for Smart Grid , 2018 .

[9]  Sungyong Park,et al.  An Energy-Aware Service Function Chaining and Reconfiguration Algorithm in NFV , 2016, 2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS*W).

[10]  Cisco Visual Networking Index: Forecast and Methodology 2016-2021.(2017) http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual- networking-index-vni/complete-white-paper-c11-481360.html. High Efficiency Video Coding (HEVC) Algorithms and Architectures https://jvet.hhi.fraunhofer. , 2017 .

[11]  Fredrik Tufvesson,et al.  5G: A Tutorial Overview of Standards, Trials, Challenges, Deployment, and Practice , 2017, IEEE Journal on Selected Areas in Communications.

[12]  Girish Ghatikar,et al.  Demand Response Opportunities and Enabling Technologies for Data Centers: Findings From Field Studies , 2012 .

[13]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[14]  Tarik Taleb,et al.  NFV: Security Threats and Best Practices , 2017, IEEE Communications Magazine.

[15]  Sonja Klingert,et al.  A Generic Architecture for Demand Response: The ALL4Green Approach , 2013, 2013 International Conference on Cloud and Green Computing.

[16]  Randy L. Ekl,et al.  Security Technology for Smart Grid Networks , 2010, IEEE Transactions on Smart Grid.

[17]  Sonja Klingert,et al.  Making Data Centers Fit for Demand Response: Introducing GreenSDA and GreenSLA Contracts , 2018, IEEE Transactions on Smart Grid.

[18]  Xavier Hesselbach,et al.  An NFV-Based Energy Scheduling Algorithm for a 5G Enabled Fleet of Programmable Unmanned Aerial Vehicles , 2019, Wirel. Commun. Mob. Comput..

[19]  Jan T. Bialasiewicz,et al.  Power-Electronic Systems for the Grid Integration of Renewable Energy Sources: A Survey , 2006, IEEE Transactions on Industrial Electronics.

[20]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[21]  A Q Huang,et al.  The Future Renewable Electric Energy Delivery and Management (FREEDM) System: The Energy Internet , 2011, Proceedings of the IEEE.

[22]  Seung Ho Hong,et al.  An IoT-based energy-management platform for industrial facilities , 2016 .

[23]  Jose Medina,et al.  Demand Response and Distribution Grid Operations: Opportunities and Challenges , 2010, IEEE Transactions on Smart Grid.

[24]  Sonja Klingert,et al.  Integrating data centres into demand-response management: A local case study , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[25]  Nicholas Good,et al.  Review and classification of barriers and enablers of demand response in the smart grid , 2017 .

[26]  Daohua Zhu,et al.  Energy informatics: Fundamentals and standardization , 2017, ICT Express.

[27]  Hermann de Meer,et al.  Constructing Dependable Smart Grid Networks using Network Functions Virtualization , 2016, Journal of Network and Systems Management.

[28]  Francisco Manzano-Agugliaro,et al.  Intelligent homes’ technologies to optimize the energy performance for the net zero energy home , 2017 .

[29]  L.H. Tsoukalas,et al.  From smart grids to an energy internet: Assumptions, architectures and requirements , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.

[30]  Jiannong Cao,et al.  Providing flexible services for heterogeneous vehicles: an NFV-based approach , 2016, IEEE Network.

[31]  Xavier Hesselbach,et al.  Demand-Response Power Management Strategy Using Time Shifting Capabilities , 2018, e-Energy.

[32]  IMT Vision – Framework and overall objectives of the future development of IMT for 2020 and beyond M Series Mobile , radiodetermination , amateur and related satellite services , 2015 .

[33]  M. O. Oseni Improving households’ access to electricity and energy consumption pattern in Nigeria: Renewable energy alternative , 2012 .

[34]  David Pisinger,et al.  Algorithms for Knapsack Problems , 1995 .

[35]  Cemal Keles,et al.  Multi-source energy mixing by time rate multiple PWM for microgrids , 2016, 2016 4th International Istanbul Smart Grid Congress and Fair (ICSG).

[36]  Eric Keller,et al.  Software-defined energy communication networks: From substation automation to future smart grids , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[37]  Kevin J. Warner,et al.  The 21st century population-energy-climate nexus , 2016 .

[38]  Sakir Sezer,et al.  A Survey of Security in Software Defined Networks , 2016, IEEE Communications Surveys & Tutorials.