Resource Allocation for Blockchain-Enabled Distributed Network Function Virtualization (NFV) with Mobile Edge Cloud (MEC)

Mobile Edge Cloud (MEC) has emerged as a promising paradigm shift from the centralized mobile cloud due to the explosive growth of edge devices and traffic volumes. Network Function Virtualization (NFV) is a key technology for managing and orchestrating the virtualized instances in MEC. However, it is challenging to perform efficient resource allocation in distributed NFV with MEC due to the multiple existence of NFV Management and Orchestration (MANO) systems in distributed NFV. In this work, we propose a blockchain-enabled NFV framework to reach consensus among multiple MANO systems for complex MEC scenarios. Moreover, we take both the latency of services and operational cost into consideration to achieve better resource allocation. Then, we formulate the efficient resource allocation for services in blockchain-enabled NFV with MEC as a multi-objective optimization problem. Due to the fact that it is difficult to solve this multi-objective optimization problem by traditional methods, we propose a dueling deep reinforcement learning approach. Simulation results are presented to show the effectiveness of our proposed scheme.

[1]  Tom Schaul,et al.  Dueling Network Architectures for Deep Reinforcement Learning , 2015, ICML.

[2]  Marko Vukolic,et al.  Blockchain Consensus Protocols in the Wild , 2017, DISC.

[3]  Nei Kato,et al.  Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach , 2019, IEEE Transactions on Emerging Topics in Computing.

[4]  Gregor Frick,et al.  Distributed NFV Orchestration in a WMN-Based Disaster Network , 2018, 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN).

[5]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.

[6]  Weifa Liang,et al.  Efficient Algorithms for Capacitated Cloudlet Placements , 2016, IEEE Transactions on Parallel and Distributed Systems.

[7]  Haipeng Yao,et al.  Blockchain-Based Software-Defined Industrial Internet of Things: A Dueling Deep ${Q}$ -Learning Approach , 2019, IEEE Internet of Things Journal.

[8]  Roch Glitho,et al.  On the Placement of VNF Managers in Large-Scale and Distributed NFV Systems , 2017, IEEE Transactions on Network and Service Management.

[9]  Yanhua Zhang,et al.  Virtualization for Distributed Ledger Technology (vDLT) , 2018, IEEE Access.

[10]  Marc Peter Deisenroth,et al.  Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.

[11]  Xing Zhang,et al.  A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications , 2017, IEEE Access.

[12]  Song Guo,et al.  Service provisioning update scheme for mobile application users in a cloudlet network , 2017, 2017 IEEE International Conference on Communications (ICC).

[13]  Lei Zhao,et al.  Routing for Crowd Management in Smart Cities: A Deep Reinforcement Learning Perspective , 2019, IEEE Communications Magazine.