Changes in enterprise networks require updated configurations. However, manual configurations with slow update efficiency, poor performance, and handling limitations, lead to the unavailability of updated networks. Therefore, we propose an efficient network renascence framework, NetRen, which synthesizes OSPF/BGP configurations driven by service and traffic migration. We follow the workflow of sketch extraction, configuration synthesis, and repair. Initially, comprehensive graphs are constructed to represent configuration sketches. We propose a GraphTrans synthesizer with Transformer's benefits of long-range focus and parallel reasoning. Training samples with the optimization relationship enable the synthesizer to achieve a mapping that optimizes performance based on configurations. To overcome the satisfiability barrier, configurations from the synthesizer are input to the stepwise configuration repairer as well-initialized solutions, achieving rapid configuration repair. Experiments demonstrate that the consistency of network configurations output by the GraphTrans synthesizer averages 98%. NetRen achieves a 312.4× increase in synthesis efficiency and a 5.83% improvement in network performance.

Edge network slicing promises better utilization of network resources by dynamically allocating resources on demand. However, addressing the imbalance between slice resources and user demands becomes challenging when complex user behaviors lead to bursty traffic within the edge network. Hence, we propose a comprehensive dynamic slice strategy with two coupled sub-strategies (i) bursty-sensitive slice resource coordination and (ii) proactive demand resource matching to find an optimal balance. For obtaining stable strategies, the edge network with bursty traffic is formulated as a bi-level Lyapunov optimization problem. Then we propose a resource allocation and request redirection (RA-RR) algorithm with polynomial complexity by introducing deep reinforcement learning to guarantee real-time. Specifically, two agents are trained to solve two sub-strategies, and the Lyapunov drift-plus-penalty function is used as the reward to keep queues stable. RA-RR is responsive to fluctuations in demand and realizes an efficient interaction of coupled decision-making. Moreover, a training method based on alternating optimization is designed to ensure convergence of the RA-RR algorithm. Experiments demonstrate that the proposal can maximize network revenue while ensuring the stability of slice services when edge traffic bursts, and has an average improvement of 20.4% compared with comparisons.

Network slicing is rapidly prevailing in the edge network, which provides computing, network, and storage resources for various services. When the multiple service providers (SPs) respond to their tenants in parallel, individual decisions on the dynamic and shared edge network may lead to resource conflicts, which affects the delivery of network slicing services. Existing works ignore resource interaction and coordination in the multi-SP scenario, which is not in line with the actual situation. Indeed, the complexity of resource interaction caused by the coexistence of multiple SP policies increases the difficulty to solve the formulated optimization model. In this article, we focus on the multi-SP network slicing deployment in parallel. The coordination of network resources between SPs is designed as an effective multi-agent communication mechanism that is merged into multi-agent deep reinforcement learning (MADRL). To deal with dynamic edge networks, we design the neurons hotplugging learning which realizes scalability without a high cost of model retraining. Experiments on real and random networks demonstrate that the proposed multi-SP network slicing mechanism can successfully learn coordination policies and easily adapt to various network scales. It improves the accepted requests by 7.4%, reduces resource conflicts by 14.5%, and shortens the model convergence time by 83.3%.

Network slicing is rapidly prevailing in edge cloud, which provides computing, network and storage resources for various services. When the multiple service providers (SPs) respond to their tenants in parallel, individual decisions on the dynamic and shared edge cloud may lead to resource conflicts. The resource conflicts problem can be formulated as a multi-objective constrained optimization model; however, it is challenging to solve it due to the complexity of resource interactions caused by co-existing multi-SP policies. Therefore, we propose a CommDRL scheme based on multi-agent deep reinforcement learning (MADRL) and multi-agent communication to tackle the challenge. CommDRL can coordinate network resources between SPs with less overhead. Moreover, we design the neurons hotplugging learning in CommDRL to deal with dynamic edge cloud, which realizes scalability without a high cost of model retraining. Experiments demonstrate that CommDRL can successfully obtain deployment policies and easily adapt to various network scales. It improves the accepted requests by 7.4%, reduces resource conflicts by 14.5%, and shortens the model convergence time by 83.3%.