Trusted resource allocation based on proof-of-reputation consensus mechanism for edge computing

【Author】 Hu, Qiaohong; Cheng, Hongju; Zhang, Xiaoqi; Lin, Chengkuan

【Source】PEER-TO-PEER NETWORKING AND APPLICATIONS

【影响因子】3.488

【Abstract】Resource allocation aims at using the storage, computing and bandwidth between edge servers and end users while satisfying the requirement of different application scenarios. However, there are some security threats in the allocation process, such as unreal resources and denial of service, in which the trusted resource allocation becoming a critical research content. Blockchain technology is one important solution for this problem, but the high-delay and low-throughput shortcomings in blockchain cannot fulfill the requirements of time-sensitive applications. In this paper, we firstly utilize a reputation evaluation mechanism to calculate and rate the servers' reputation values in the resource allocation process. The high-reputation servers are added as candidate leaders into a block generation group, while the remaining low-reputation edge servers are added as verifiers into a block verification group. Secondly, we utilize a round-robin mechanism to choose a leader from the block generation group. The leader is responsible for generating a new block in each timeslot and is updated in different timeslots. Thirdly, we utilize a reward and punishment mechanism to encourage low-reputation servers to participate in consensus. Finally, we propose a proof-of-reputation consensus mechanism to realize the trusted resource allocation based on smart contracts, which can match the end users with high-reputation edge servers automatically. Simulation results show the consensus delay and throughput of resource allocation achieve significant performance improvements in comparison with current schemes, and the system is more sensitive to dishonest edge servers and quickly reduces their reputation values.

【Keywords】Resource allocation; Smart contracts; Blockchain; Edge computing; Consensus mechanism; Reputation evaluation

【发表时间】

【收录时间】2022-01-01

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