Distributed computing power trading scheme based on trustworthiness assessment

Distributed computing power trading scheme based on trustworthiness assessment

Blog Article

The computing power network was primarily designed to optimize and efficiently utilize network and computing resources by connecting and integrating distributed heterogeneous computing nodes.Within this network, nodes frequently required computing resources from other nodes through trading mechanisms due to their limited local resources.The introduction of a trust evaluation mechanism effectively addressed the issue of low trustworthiness among ubiquitous heterogeneous nodes.

However, existing computing power trading schemes, which relied on reputation as a willett mini bottle key criterion for selecting trading partners, typically favored nodes with the highest reputation.This approach resulted in newly joined nodes being unable to participate fairly in computing power trading, and the untrustworthy trading environment made it difficult for nodes to assess the credibility of trading feedback.To address these challenges, a distributed computing power trading scheme based on trust evaluation was proposed.

In this scheme, the trust evaluation of computing nodes meeting the computing power requirements was automated through smart contracts, generating a trust list sorted in descending order.A random selection algorithm was utilized to choose trading partners from nodes with trust levels above a predefined threshold, ensuring randomness in resource selection and effectively mitigating collusion attacks.Additionally, an adaptive function ferrofish a32pro was employed to dynamically adjust the weights of direct and indirect trust, enabling the tracking of behavioral changes in computing nodes.

Furthermore, a method for measuring feedback reliability was designed using blockchain technology to reduce the bias in evaluation results caused by untrustworthy nodes submitting malicious ratings.Finally, experimental results demonstrated that the proposed scheme effectively identified malicious ratings and reduced the impact of malicious behaviors.The scheme is shown to ensure fair participation in trading for newly joined nodes while enhancing the reliability of trading feedback.