上海科技大学知识管理系统

For systems that are based on network function virtualization (NFV), it remains a key challenge to conduct effective service chain composition with the lowest request latency and the minimum network congestion. In such an NFV system, users are usually non-cooperative, i.e., they compete with each other to optimize their own benefits. However, existing solutions often ignore such non-cooperative behaviors of users. What is more, they may fall short in the face of unexpected resource failures such as breakdown of virtual machines and loss of connections to users. In this article, we formulate the service chain composition problem with resource failures in NFV systems as a non-cooperative game, and show that such a game is a weighted potential game, aiming to search for the optimal Nash equilibrium (NE). By adopting Markov approximation techniques, we devise a distributed scheme called MH-SCCA, which achieves a provably near-optimal NE and adapts to resource failures in a timely manner. For comparison, we also propose two baseline schemes (DRL-SCCA and MCTS-SCCA) for centralized service chain composition that are based on deep reinforcement learning (DRL) and Monte Carlo tree search (MCTS) techniques, respectively. Our simulation results demonstrate the effectiveness of the three proposed schemes in terms of both latency reduction and congestion mitigation, as well as the adaptivity of MH-SCCA when faced with resource failures.