PASHE: Privacy Aware Scheduling in a Heterogeneous Fog Environment

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.