Abstract
The increasing quality and various requirements of network services are guaranteed because of the advancement of the emerging network paradigm, software-defined networking (SDN), and benefits from the centralized and software-defined architecture. The SDN not only facilitates the configuration of the network policies for traffic engineering but also brings convenience for network state obtainment. The traffic of numerous services is transmitted within a network, whereas each service may demand different network metrics, such as low latency or low packet loss rate. Corresponding quality of service policies must be enforced to meet the requirements of different services, and the balance of link utilization is also indispensable. In this research, Reinforcement Discrete Learning-Based Service-Oriented Multipath Routing (RED-STAR) has been proposed to understand the policy of distributing an optimal path for each service. The RED-STAR takes the network state and service type as input values to dynamically select the path a service must be forwarded. Custom protocols are designed for network state obtainment, and a deep learning-based traffic classification model is also integrated to identify network services. With the differentiated reward scheme for every service type, the reinforcement learning model in RED-STAR gradually achieves high reward values in various scenarios. The experimental results show that RED-STAR can adopt the dynamic network environment, obtaining the highest average reward value of 1.8579 and the lowest average maximum bandwidth utilization of 0.3601 among all path distribution schemes in a real-case scenario.
Highlights
As the diversity of network services increases, users demand high quality of service (QoS) [1]
A traffic engineering scheme must forward the traffic of specific services to routes, whereas the traffic of various services is being transmitted within a network
The main problem can be formulated as follows: given a set of services and network link states, an optimal path must be assigned for the traffic of each network service to meet its QoS requirements and the link utilization must be balanced as much as possible
Summary
As the diversity of network services increases, users demand high quality of service (QoS) [1]. E Reinforcement Discrete Learning Service-Oriented Multipath Routing (RED-STAR) mechanism is proposed to dynamically distribute routes in a network to every service and to tackle the problem. As a deep reinforcement learning (DRL) [20] method, RED-STAR considers the network metrics, that is, bandwidth utilization, link latency, and packet loss rate, as the environment state. (3) e reward scheme considers different QoS requirements of services and load balancing issues, and RED-STAR distributes routes to services relying on the custom reward scheme. (4) e DRL mechanism is applied in the SDN, takes network metrics and service type as the environment, and considers routes in the network as the action set, which is a novel traffic engineering paradigm. E results show that the proposed method performs better than other route distribution schemes when considering load balancing and QoS requirements.
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