  Fog computing offloads latency critical application services running on the
Cloud in close proximity to end-user devices onto resources located at the edge
of the network. The research in this paper is motivated towards characterising
and estimating the time taken to offload a service using containers, which is
investigated in the context of the `Save and Load' container migration
technique. To this end, the research addresses questions such as whether fog
offloading can be accurately modelled and which system and network related
parameters influence offloading. These are addressed by exploring a catalogue
of 21 different metrics both at the system and process levels that is used as
input to four estimation techniques using collective model and individual
models to predict the time taken for offloading. The study is pursued by
collecting over 1.1 million data points and the preliminary results indicate
that offloading can be modelled accurately.
