We present a smart Service Manager whose role is

to direct user requests (such as those coming from IoT devices)

at the edge towards appropriate servers where the services they

request can be satisfied, when services can be housed at different

Fog locations, and the system is subject to variations in workload.

The approach we propose is based on using an SDN controller as

a decision element, and to incorporate measurement data based

machine learning that uses Reinforcement Learning to make the

best choices. The system we have developed is illustrated with

experimental results on a test-bed in the presence of time-varying

loads at the servers. The experiments confirm the ability of the

system to adapt to significant changes in system load so as to

preserve the QoS perceived by end users.