Efficient network slicing is vital to deal with the highly variable and
dynamic characteristics of network traffic generated by a varied range of
applications. The problem is made more challenging with the advent of new
technologies such as 5G and new architectures such as SDN and NFV. Network
slicing addresses a challenging dynamic network resource allocation problem
where a single network infrastructure is divided into (virtual) multiple slices
to meet the demands of different users with varying requirements, the main
challenges being --- the traffic arrival characteristics and the job resource
requirements (e.g., compute, memory and bandwidth resources) for each slice can
be highly dynamic. Traditional model-based optimization or queueing theoretic
modeling becomes intractable with the high reliability, and stringent bandwidth
and latency requirements imposed by 5G technologies. In addition these
approaches lack adaptivity in dynamic environments. We propose a deep
reinforcement learning approach to address this dynamic coupled resource
allocation problem. Model evaluation using both synthetic simulation data and
real workload driven traces demonstrates that our deep reinforcement learning
solution improves overall resource utilization, latency performance, and
demands satisfied as compared to a baseline equal-slicing strategy.

本研究提出了一种基于深度强化学习的网络分片动态资源分配问题解决方案，模拟数据和真实负载驱动跟踪的模型评估表明，相比于基准均等分片策略，我们的解决方案提高了总资源利用率，降低了时延性能，并满足了更多的需求。

应对异构资源需求和时变流量动态的网络切片深度强化学习

Deep Reinforcement Learning for Network Slicing with Heterogeneous  Resource Requirements and Time Varying Traffic Dynamics

The combination of recent emerging technologies such as network function
virtualization (NFV) and network programmability (SDN) gave birth to the
Network Slicing revolution. 5G networks consist of multi-tenant infrastructures
capable of offering leased network "slices" to new customers (e.g., vertical
industries) enabling a new telecom business model: Slice-as-aService (SlaaS).
In this paper, we aim i ) to study the slicing admission control problem by
means of a multi-queuing system for heterogeneous tenant requests, ii ) to
derive its statistical behavior model, and iii ) to provide a utility-based
admission control optimization. Our results analyze the capability of the
proposed SlaaS system to be approximately Markovian and evaluate its
performance as compared to legacy solutions.

本文研究了基于多队列系统的异构租户请求的切片接纳控制问题，并形成了其统计行为模型，并提供了一个基于效用的接纳控制优化方法，分析了所提出的 SlaaS 系统具有近似 Markovian 的能力并评估其与传统解决方案的性能。