Abstract:
Fog computing extends the functionality of the
traditional cloud data center (cdc) using micro data centers
(mdcs) located at the edge of the network. These mdcs provide
both computation and storage to applications. Their proximity
to users makes them a viable option for executing jobs with
tight deadlines and latency constraints. Moreover, it may be
the case that these mdcs have diverse execution capacities, i.e.
they have heterogeneous architectures. The implication for this
is that tasks may have variable execution costs on different
mdcs. We propose PASHE (Privacy Aware Scheduling in a
Heterogeneous Fog Environment), an algorithm that schedules
privacy constrained real-time jobs on heterogeneous mdcs
and the cdc. Three categories of tasks have been considered:
private, semi-private and public. Private tasks with tight
deadlines are executed on the local mdc of users. Semi-private
tasks with tight deadlines are executed on “preferred” remote
mdcs. Public tasks with loose deadlines are sent to the cdc
for execution. We also take account of user mobility across
different mdcs. If the mobility pattern of users is predictable,
PASHE reserves computation resources on remote mdcs for job
execution. Simulation results show that PASHE offers superior
performance versus other scheduling algorithms in a fog
computing environment, taking account of mdc heterogeneity,
user mobility and application security.
