Blockchain-Based Predictive Resource Sharing in NFV Environments with LSTM-Based Demand Forecasting

Abstract

: Network Function Virtualization (NFV) enables network functions to be implemented as software through flexible deployment capabilities. However, static demand assumptions, centralized security dependencies, and reactive decision-making approaches hinder efficient resource allocation among VNFs. This paper addresses these challenges by proposing a hybrid framework that integrates LSTM-based demand prediction regression model for time series forecasting, game-theoretic resource allocation, and blockchain-based access control. The blockchain provides decentralized, tamper-proof storage of cryptographic keys and automated enforcement of sharing agreements through smart contracts, eliminating reliance on trusted third parties.The proposed predictive algorithm combines real-time demand data with LSTM forecasts to estimate effective demand, enabling proactive resource allocation. The framework is evaluated through extensive simulations, comparing the proposed method with greedy matching and diagonalization baseline approaches. Results show that the proposed method achieves a 10.3% improvement in social utility over greedy matching, while reaching a near-optimal solution with a cost 6.3 × lower than diagonalization. The LSTM model achieves a Mean Absolute Percentage Error (MAPE) of 8.91% and an R 2 score of 0.880.These results demonstrate that integrating demand prediction into game-theoretic NFV resource sharing significantly enhances efficiency while preserving strong security guarantees.