Runtime control for application failure prevention in resource-constrained devices

ABSTRACT:

In the last decades, there has been a growing interest towards new use cases such as extended reality glasses, unmanned aerial vehicle (UAV), and autonomous driving.
The technological advancement observed in such scenarios has also been enabled by
the increasing capabilities of small formfactor devices. Although such devices allow
to achieve remarkable computing performance with relatively low energy consumption,
these are often used in contexts in which the trade-offs between power consumption
and application performance play a key role (e.g., battery powered systems).
Furthermore, if such trade-offs are not carefully set, the performance degradation
can lead to system failure. The work proposed in this thesis aims at investigating
this type of problems, and to propose a runtime model-controller based on the joint
optimization of the platform and application parameters to reduce system failure.
The proposed architecture is evaluated in a UAV emulated environment, in which
the used platform embeds hardware features comparable to the ones of a drone,
while the localization and mapping application executed on such device makes use
of real-world visual-inertial datasets. The proposed runtime model-controller solution
relies on the monitoring of the platform CPU peaks for identifying application’s
failure. It has also been empirically demonstrated that the model-controller
can substantially decrease the number of failures and, in specific scenarios, improve
localization accuracy and power consumption even compared to the optimal static
parameter configurations. Moreover, the solution has been proven to be simple and
generalizable in scenarios characterized by different levels of concurrency, and in
the datasets tested