Real-Time Scheduling on Hierarchical Heterogeneous Fog Networks

Cloud computing is widely used to support offloaded data processing for various applications. However, latency constrained data processing has requirements that may not always be suitable for cloud-based processing. Fog computing brings processing closer to data generation sources, by reducing propagation and data transfer delays. It is a viable alternative for processing tasks with real-time requirements. We propose a scheduling algorithm
$RTH^{2}S$
(
R
eal
T
ime
H
eterogeneous
H
ierarchical
S
cheduling) for a set of real-time tasks on a heterogeneous integrated fog-cloud architecture. We consider a hierarchical model for fog nodes, with nodes at higher tiers having greater computational capacity than nodes at lower tiers, though with greater latency from data generation sources. Tasks with various profiles have been considered. For the regular profile jobs, we use least laxity first (LLF) to find the preferred fog node for scheduling. In case of “tagged” profiles, based on their tag values, the jobs are split in order to finish execution before the deadline, or the LLF heuristic is used. Using HPC2N workload traces across 3.5 years of activity, the real-time performance of
$RTH^{2}S$
versus comparable algorithms is demonstrated. We also consider Microsoft Azure-based costs for the proposed algorithm. Our proposed approach is validated using both simulation (to demonstrate scale up) as well as a lab-based testbed.