On Modeling Network Slicing Communication Resources with SARSA Optimization
This addresses resource orchestration for network slicing in mobile and IoT systems, but appears incremental as it applies an existing reinforcement learning method to a specific domain.
The paper tackles the problem of optimizing communication resource sharing among network slices in dynamic networks like 5G/6G and IoT, proposing a conceptual model and using a SARSA agent to solve it, with results discussed from a proof-of-concept prototype.
Network slicing is a crucial enabler to support the composition and deployment of virtual network infrastructures required by the dynamic behavior of networks like 5G/6G mobile networks, IoT-aware networks, e-health systems, and industry verticals like the internet of vehicles (IoV) and industry 4.0. The communication slices and their allocated communication resources are essential in slicing architectures for resource orchestration and allocation, virtual network function (VNF) deployment, and slice operation functionalities. The communication slices provide the communications capabilities required to support slice operation, SLA guarantees, and QoS/ QoE application requirements. Therefore, this contribution proposes a networking slicing conceptual model to formulate the optimization problem related to the sharing of communication resources among communication slices. First, we present a conceptual model of network slicing, we then formulate analytically some aspects of the model and the optimization problem to address. Next, we proposed to use a SARSA agent to solve the problem and implement a proof of concept prototype. Finally, we present the obtained results and discuss them.