Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks

Fueled by the explosive growth of ultra-low-latency and real-time applications with specific computing and network performance requirements,the computing force network(CFN)has become a hot research subject.The primary CFN challenge is to leverage network resources and computing resources.Although recent advances in deep reinforcement learning(DRL)have brought significant improvement in network optimization,these methods still suffer from topology changes and fail to generalize for those topologies not seen in training.This paper proposes a graph neural network(GNN)based DRL framework to accommodate network trafic and computing resources jointly and efficiently.By taking advantage of the generalization capability in GNN,the proposed method can operate over variable topologies and obtain higher performance than the other DRL methods.

Reputation-based joint optimization of user satisfaction and resource utilization in a computing force network

Under the development of computing and network convergence,considering the computing and network resources of multiple providers as a whole in a computing force network(CFN)has gradually become a new trend.However,since each computing and network resource provider(CNRP)considers only its own interest and competes with other CNRPs,introducing multiple CNRPs will result in a lack of trust and difficulty in unified scheduling.In addition,concurrent users have different requirements,so there is an urgent need to study how to optimally match users and CNRPs on a many-to-many basis,to improve user satisfaction and ensure the utilization of limited resources.In this paper,we adopt a reputation model based on the beta distribution function to measure the credibility of CNRPs and propose a performance-based reputation update model.Then,we formalize the problem into a constrained multi-objective optimization problem and find feasible solutions using a modified fast and elitist non-dominated sorting genetic algorithm(NSGA-II).We conduct extensive simulations to evaluate the proposed algorithm.Simulation results demonstrate that the proposed model and the problem formulation are valid,and the NSGA-II is effective and can find the Pareto set of CFN,which increases user satisfaction and resource utilization.Moreover,a set of solutions provided by the Pareto set give us more choices of the many-to-many matching of users and CNRPs according to the actual situation.