Performance Optimization for Blockchain-Enabled Distributed Network Function Virtualization Management and Orchestration

Distributed network function virtualization management and orchestration (NFV-MANO) offers a flexible way to manage and orchestrate diversified network services in large-scale Internet of vehicles (IoV). However, it is challenging to manage different services and resources in distributed NFV due to the difficulties of reliable message synchronization among multiple MANO systems. Recently, blockchain technology has emerged to solve the trust and security problems for the interconnections of multiple MANO systems. Moreover, multi-access edge computing (MEC) has become a prospective paradigm shift from the centralized cloud due to its advantages of completing tasks near users. In this work, the authors propose a blockchain-enabled distributed NFV framework to reach consensus among multiple MANO systems where the computation tasks of the blockchain are processed with MEC. The consensus procedures of MANO systems and blockchain nodes are explained in detail and the representation of the blockchain throughput is given. The blockchain throughput is the number of transactions a blockchain system can handle per second, which is an important evaluation indicator for the performance of a blockchain system. Th eauthors make decisions for the primary node selection, the MANO system selection and the edge server selection for reaching consensus. Moreover, the blockchain throughput, the processing delay of computation tasks of blockchain and operational costs are jointly considered in the problem formulation. A dueling deep reinforcement learning approach is applied to solve this problem. Simulation results show the effectiveness of the proposed scheme.

• Record URL:
  http://dx.doi.org/10.1109/TVT.2020.2985581
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2020, IEEE.
• Authors:
  • Fu, Xiaoyuan
  • Yu, F Richard
  • Wang, Jingyu
  • Qi, Qi
  • Liao, Jianxin
• Publication Date: 2020-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 6670-6679
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 69
  • Issue Number: 6
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Artificial intelligence; Computer network protocols; Databases; Internet; Machine learning; Motor vehicles; Optimization; Reliability; Task analysis; Telecommunications; Wireless communication systems
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01753402
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 29 2020 9:58AM