With the increasing diversity of user demands for network resources, efficient and flexible task scheduling schemes have gained greater importance. Given that existing works about dependent task scheduling primarily focus on optimizing QoS objectives without considering the impact of user preferences on decision results, and the majority of prior research neglects the underlying relationships among dependent tasks. In this paper, we introduce a Graph Neural Networks (GNN) based dependent task scheduling algorithm (GDTA) to enhance user satisfaction and propose a QoE model to assess user-centered quality of experience. This novel approach incorporates GNN for the purpose of generating embeddings for tasks and networks, leveraging its inherent capability in extracting graph-based features. Compared with baseline algorithms across diverse task parallelisms and network topologies, our method achieves higher QoE scores and shows superior stability and generalization on unseen graph datasets.

Resource Public Key Infrastructure (RPKI) defends against BGP prefix hijacking by signing Route Origin Authorizations (ROAs) and filtering malicious BGP routes with ROAs. However, RPKI's low deployment weakens its defense against prefix hijacking. In the absence of a large fraction of Autonomous Systems (ASes) signing ROAs, the incentive to use filtering to eliminate hijacked prefixes commensurately decreases. There is a cyclic dependency here because reduced filtering in turn lessens the incentive for non-adoptees to become adoptees. Previous studies on RPKI deployment are mainly from the measurement perspective or focus on the deployment of large Internet Service Providers (ISPs). The above circular dependency problem inside the RPKI has not been fully studied. To improve RPKI's defense, this paper studies the circular dependency problem from an evolutionary game theory perspective. We model the strategy evolution of ASes choosing to deploy signing alone, deploy filtering alone, or deploy both signing and filtering. The results show that when the deployment rates of signing and filtering reach a certain range, the evolution can reach an ideal deployment state at a faster speed. Therefore, to increase the probability of evolution reaching the ideal deployment state, we propose RPKIN to widen this interval and reduce the minimum deployment rate required for signing and filtering.

In the inter-domain network, route leaks can disrupt the Internet traffic and cause large outages. The accurate detection of route leaks requires the sharing of AS business relationship information. However, the business relationship information between ASes is confidential. ASes are usually unwilling to reveal this information to the other ASes, especially their competitors. In this paper, we propose a method named FL-RLD to detect route leaks while maintaining the privacy of business relationships between ASes by using a blockchain-based federated learning framework, where ASes can collaboratively train a global detection model without directly disclosing their specific business relationships. To mitigate the lack of ground-truth validation data in route leaks, FL-RLD provides a self-validation scheme by labeling AS triples with local routing policies. We evaluate FL-RLD under a variety of datasets including imbalanced and balanced datasets, and examine different deployment strategies of FL-RLD under different topologies. According to the results, FL-RLD performs better in detecting route leaks than the single AS detection, whether the datasets are balanced or imbalanced. Additionally, the results indicate that selecting ASes with the most peers to first deploy FL-RLD brings more significant benefits in detecting route leaks than selecting ASes with the most providers and customers.