3 research outputs found
Modeling, Analysis, and Hard Real-time Scheduling of Adaptive Streaming Applications
In real-time systems, the application's behavior has to be predictable at
compile-time to guarantee timing constraints. However, modern streaming
applications which exhibit adaptive behavior due to mode switching at run-time,
may degrade system predictability due to unknown behavior of the application
during mode transitions. Therefore, proper temporal analysis during mode
transitions is imperative to preserve system predictability. To this end, in
this paper, we initially introduce Mode Aware Data Flow (MADF) which is our new
predictable Model of Computation (MoC) to efficiently capture the behavior of
adaptive streaming applications. Then, as an important part of the operational
semantics of MADF, we propose the Maximum-Overlap Offset (MOO) which is our
novel protocol for mode transitions. The main advantage of this transition
protocol is that, in contrast to self-timed transition protocols, it avoids
timing interference between modes upon mode transitions. As a result, any mode
transition can be analyzed independently from the mode transitions that
occurred in the past. Based on this transition protocol, we propose a hard
real-time analysis as well to guarantee timing constraints by avoiding
processor overloading during mode transitions. Therefore, using this protocol,
we can derive a lower bound and an upper bound on the earliest starting time of
the tasks in the new mode during mode transitions in such a way that hard
real-time constraints are respected.Comment: Accepted for presentation at EMSOFT 2018 and for publication in IEEE
Transactions on Computer-Aided Design of Integrated Circuits and Systems
(TCAD) as part of the ESWEEK-TCAD special issu
Resource Optimization for Real-Time Streaming Applications Using Task Replication
Computer Systems, Imagery and Medi