Expand description
Models of arrival processes (periodic, sporadic, etc.)
This module provides a central trait, ArrivalBound, which represents an upper-bounding arrival curve. Furthermore, it provides implementations of the trait for several types of arrival processes commonly studied in the literature on the analysis of real-time systems (e.g, Periodic and Sporadic tasks).
Structs§
- Approximated
Poisson - A finite approximation of a Poisson process with bounded probability of under-approximation.
- Arrival
Curve Prefix - An alternate representation of an arbitrary arrival curve intended primarily for input purposes.
- Curve
- An arrival curve (also commonly called an “upper event arrival curve” η+) that can describe arbitrarily bursty sporadic arrival processes.
- Extrapolating
Curve - An arrival curve that automatically extrapolates and caches extrapolation results using interior mutability.
- Never
- Pathological corner case: model of a task that never releases any jobs.
- Periodic
- Classic jitter-free periodic arrival process as introduced by Liu & Layland.
- Poisson
- Model of a Poisson arrival process.
- Propagated
- A simple model of arrivals induced by a precedence relationship.
- Sporadic
- The classic sporadic arrival model (originally due to Mok) with release jitter.
Traits§
- Arrival
Bound - The main interface for models describing arrival processes.
Functions§
- delta_
min_ iter - nonzero_
delta_ min_ iter - sum_of
- The sum of two arrival bounds, representing the joint arrival bound.