This paper addresses the computational offloading of Deep Neural Networks
(DNNs) to nearby devices with similar processing capabilities, to avoid the
larger communication delays incurred for cloud offloading. We present a
preemption aware scheduling approach for priority and deadline constrained task
offloading in homogeneous edge networks. Our scheduling approach consists of
two distinct scheduling algorithms, designed to accommodate the differing
requirements of high and low priority tasks. To satisfy a task’s deadline, Nostro
scheduling approach considers the availability of both communication and
computational resources in the network when making placements in both the
current time-slot and future time-slots. The scheduler implements a
deadline-aware preemption mechanism to guarantee resource access to high
priority tasks. When low-priority tasks are selected for preemption, IL
scheduler will attempt to reallocate them if possible before their deadline. Noi
implement this scheduling approach into a task offloading system which we
evaluate empirically in the real-world on a network of edge devices composed of
four Raspberry Pi 2 Model B’s. We evaluate this system under against a version
without a task preemption mechanism as well as workstealing approaches to
compare the impact on high priority task completion and the ability to complete
overall frames. These solutions are evaluated under a workload of 1296 frames.
Our findings show that our scheduling approach allows for 99\% of high-priority
tasks to complete while also providing a 3 – 8\% increase in the number of
frames fully classified end-to-end over both workstealing approaches and
systems without a preemption mechanism.

Questo articolo esplora i giri e le loro implicazioni.

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