@daliaer

Task pricing algorithms in decentralized computing marketplaces dynamically set costs based on supply, demand, and node capabilities. Auction-based models, where nodes bid for tasks, optimize resource allocation but may underprice complex jobs. Predictive algorithms using historical data and real-time metrics (e.g., CPU load) improve accuracy. Experiments show that machine learning-driven pricing reduces task completion times by 20% while maintaining profitability. However, balancing fairness with efficiency remains challenging, as over-optimization could exclude smaller providers. Hybrid models combining auctions with baseline prices offer a viable compromise.

0 reply

0 recast

0 reaction