  In recent years, edge computing has become a popular choice for
latency-sensitive applications like facial recognition and augmented reality
because it is closer to the end users compared to the cloud. Although
infrastructure providers are working toward creating managed edge networks,
personal devices such as laptops and tablets, which are widely available and
are underutilized, can also be used as potential edge devices. We call such
devices Unmanaged Edge Devices (UEDs). Scheduling application tasks on such an
unmanaged edge system is not straightforward because of three fundamental
reasons-heterogeneity in the computational capacity of the UEDs, uncertainty in
the availability of the UEDs (due to devices leaving the system), and
interference among multiple tasks sharing a UED. In this paper, we present
I-BOT, an interference-based orchestration scheme for latency-sensitive tasks
on an Unmanaged Edge Platform (UEP). It minimizes the completion time of
applications and is bandwidth efficient. I-BOT brings forth three innovations.
First, it profiles and predicts the interference patterns of the tasks to make
scheduling decisions. Second, it uses a feedback mechanism to adjust for
changes in the computational capacity of the UEDs and a prediction mechanism to
handle their sporadic exits. Third, it accounts for input dependence of tasks
in its scheduling decision (such as, two tasks requiring the same input data).
To evaluate I-BOT, we run end-to-end simulations with applications representing
autonomous driving, composed of multiple tasks. We compare to two basic
baselines (random and round-robin) and two state-of-the-arts, Lavea [SEC-2017]
and Petrel [MSN-2018]. Compared to these baselines, I-BOT significantly reduces
the average service time of application tasks. This reduction is more
pronounced in dynamic heterogeneous environments, which would be the case in a
UEP.
