  We present a model for measuring the impact of offloading soft real-time jobs
over multi-tier cloud infrastructures. The jobs originate in mobile devices and
offloading strategies may choose to execute them locally, in neighbouring
devices, in cloudlets or in infrastructure cloud servers. Within this
specification, we put forward several such offloading strategies characterised
by their differential use of the cloud tiers with the goal of optimizing
execution time and/or energy consumption. We implement an instance of the model
using Jay, a software framework for adaptive computation offloading in hybrid
edge clouds. The framework is modular and allows the model and the offloading
strategies to be seamlessly implemented while providing the tools to make
informed runtime offloading decisions based on system feedback, namely through
a built-in system profiler that gathers runtime information such as workload,
energy consumption and available bandwidth for every participating device or
server. The results show that offloading strategies sensitive to runtime
conditions can effectively and dynamically adjust their offloading decisions to
produce significant gains in terms of their target optimization functions,
namely, execution time, energy consumption and fulfillment of job deadlines.
