  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.
