Service Function Chains (SFCs) forward data traffic through a series of Virtual Network Functions (VNFs) to enhance the flexibility of network services. One of the key challenges in SFCs is the deployment of VNFs and scheduling incoming requests across computational nodes to ensure low latency and high reliability. However, existing studies primarily focus on static networks and assume that all SFC requests are predetermined, which is impractical. In this paper, we consider the high-reliability scheduling of SFCs in a dynamic network environment, specifically within Directed Acyclic Graphs (DAGs), to maximize the number of requests that can be successfully processed while satisfying latency and reliability constraints. To tackle this issue, we propose an efficient approach to determine the redundancy of VNFs under the constraint of latency while maximizing success. Experimental results demonstrate that our proposed solution significantly enhances the success rate and effectively conserves computational resources.

As a key enabler of future 5G network, Service Function Chain (SFC) forwards the traffic flow along a chain of Virtual Network Functions (VNFs) to provide network services flexibility. One of the most important problems in SFC is to deploy the VNFs and schedule arriving requests among computing nodes to achieve low latency and high reliability. Existing works consider a static network and assume that all SFC requests are known in advance, which is impractical. In this paper, we focus on the dynamic 5G network environment where the SFC request arrives randomly following a certain distribution. Computing nodes can redeploy all types of VNF with a time cost. We formulate the problem of SFC scheduling in NFV-enabled 5G network as a mixed integer non-linear programing. The objective is to maximize the number of requests satisfying the latency and reliability constraints. To solve the problem, we propose an efficient algorithm to decide the redundancy of the VNFs while minimizing delay. Then we present a state-of-art Reinforcement Learning (RL) to learn SFC scheduling policy to increase the success rate of SFC requests. The effectiveness of our method is evaluated through extensive simulations. The result shows that our proposed RL solution can increase the success rate by 18.7% over the benchmark algorithms.

As a key enabler of future 5G network, Service Function Chain (SFC) forwards the traffic flow along a chain of Virtual Network Functions (VNFs) to provide network services flexibility. One of the most important problems in SFC is to deploy the VNFs and schedule arriving requests among computing nodes to achieve low latency and high reliability. Existing works consider a static network and assume that all SFC requests are known in advance, which is impractical. In this paper, we focus on the dynamic 5G network environment where the SFC requests arrive randomly and the computing nodes can redeploy all types of VNF with a time cost. We formulate the problem of SFC scheduling in NFV-enabled 5G network as a mixed integer non-linear programing. The objective is to maximize the number of requests satisfying the latency and reliability constraints. To solve the problem, we propose an efficient algorithm to decide the redundancy of the VNFs while minimizing delay. Then we present a state-of-art Reinforcement Learning (RL) to learn SFC scheduling policy to increase the success rate of SFC requests. The effectiveness of our method is evaluated through extensive simulations. The result shows that our proposed RL solution can increase the success rate by 18.7% over the benchmarks.