Joint Optimization of Radio and Computational Resources Allocation in Blockchain-Enabled Mobile Edge Computing Systems

The application of blockchain to mobile edge computing (MEC) systems has attracted great interests. However, the design and optimization of blockchain and MEC in most existing works are done separately, which will result in sub-optimal performance. In this paper, we propose a joint optimization framework for blockchain-enabled MEC systems to achieve the optimal trade-off between the performance of the MEC system and the performance of the blockchain system. Specifically, both MEC and blockchain are considered as services in the framework, where energy consumption and delay/time to finality (DTF) are the performance metrics for the MEC system and the blockchain system, respectively. We formulate an optimization problem to achieve the optimal trade-off through jointly optimizing user association, data rate allocation, block producer scheduling, and computational resource allocation. To solve the problem, we decouple the optimization variables for efficient algorithm design. In addition, we develop an iterative algorithm for user association and data rate allocation and a bisection algorithm for computing resource allocation. Simulation results show the convergence of the proposed algorithms, and the proposed scheme can achieve the optimal trade-off between energy consumption and DTF.

[1]  Dario Pompili,et al.  Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges , 2016, IEEE Communications Magazine.

[2]  Jun Zhang,et al.  Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems , 2017, IEEE Transactions on Wireless Communications.

[3]  Shengli Xie,et al.  Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks , 2019, IEEE Internet of Things Journal.

[4]  Jeffrey G. Andrews,et al.  Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints , 2005, IEEE Transactions on Wireless Communications.

[5]  Dennis Miller,et al.  Blockchain and the Internet of Things in the Industrial Sector , 2018, IT Professional.

[6]  Bharat K. Bhargava,et al.  A Blockchain-Enabled Trustless Crowd-Intelligence Ecosystem on Mobile Edge Computing , 2019, IEEE Transactions on Industrial Informatics.

[7]  Thomas D. Burd,et al.  Processor design for portable systems , 1996, J. VLSI Signal Process..

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

[9]  Satoshi Nakamoto Bitcoin : A Peer-to-Peer Electronic Cash System , 2009 .

[10]  Mohsen Guizani,et al.  Blockchain-Based Mobile Edge Computing Framework for Secure Therapy Applications , 2018, IEEE Access.

[11]  Victor C. M. Leung,et al.  Distributed Resource Allocation in Blockchain-Based Video Streaming Systems With Mobile Edge Computing , 2019, IEEE Transactions on Wireless Communications.

[12]  Kaibin Huang,et al.  Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading , 2016, IEEE Transactions on Wireless Communications.

[13]  Min Sheng,et al.  Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling , 2016, IEEE Transactions on Communications.

[14]  Dong In Kim,et al.  Toward Secure Blockchain-Enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory , 2018, IEEE Transactions on Vehicular Technology.

[15]  Khaled Ben Letaief,et al.  Delay-optimal computation task scheduling for mobile-edge computing systems , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).

[16]  Zhu Han,et al.  When Mobile Blockchain Meets Edge Computing , 2017, IEEE Communications Magazine.

[17]  Davor Svetinovic,et al.  Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams , 2018, IEEE Transactions on Dependable and Secure Computing.

[18]  Keke Gai,et al.  Permissioned Blockchain and Edge Computing Empowered Privacy-Preserving Smart Grid Networks , 2019, IEEE Internet of Things Journal.

[19]  Zhu Han,et al.  Cloud/Fog Computing Resource Management and Pricing for Blockchain Networks , 2017, IEEE Internet of Things Journal.

[20]  Victor C. M. Leung,et al.  Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach , 2019, IEEE Transactions on Industrial Informatics.

[21]  Min Sheng,et al.  Energy-Efficient Subcarrier Assignment and Power Allocation in OFDMA Systems With Max-Min Fairness Guarantees , 2015, IEEE Transactions on Communications.

[22]  Zhetao Li,et al.  Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[23]  Xiaoli Chu,et al.  Enabling Low-Latency Applications in LTE-A Based Mixed Fog/Cloud Computing Systems , 2019, IEEE Transactions on Vehicular Technology.

[24]  Sachin Shetty,et al.  Towards data assurance and resilience in IoT using blockchain , 2017, MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM).

[25]  Avraham Adler,et al.  Lambert-W Function , 2015 .

[26]  Jun Li,et al.  Secure and Energy-Efficient Handover in Fog Networks Using Blockchain-Based DMM , 2018, IEEE Communications Magazine.