797 research outputs found
k2U: A General Framework from k-Point Effective Schedulability Analysis to Utilization-Based Tests
To deal with a large variety of workloads in different application domains in
real-time embedded systems, a number of expressive task models have been
developed. For each individual task model, researchers tend to develop
different types of techniques for deriving schedulability tests with different
computation complexity and performance. In this paper, we present a general
schedulability analysis framework, namely the k2U framework, that can be
potentially applied to analyze a large set of real-time task models under any
fixed-priority scheduling algorithm, on both uniprocessor and multiprocessor
scheduling. The key to k2U is a k-point effective schedulability test, which
can be viewed as a "blackbox" interface. For any task model, if a corresponding
k-point effective schedulability test can be constructed, then a sufficient
utilization-based test can be automatically derived. We show the generality of
k2U by applying it to different task models, which results in new and improved
tests compared to the state-of-the-art.
Analogously, a similar concept by testing only k points with a different
formulation has been studied by us in another framework, called k2Q, which
provides quadratic bounds or utilization bounds based on a different
formulation of schedulability test. With the quadratic and hyperbolic forms,
k2Q and k2U frameworks can be used to provide many quantitive features to be
measured, like the total utilization bounds, speed-up factors, etc., not only
for uniprocessor scheduling but also for multiprocessor scheduling. These
frameworks can be viewed as a "blackbox" interface for schedulability tests and
response-time analysis
On the periodic behavior of real-time schedulers on identical multiprocessor platforms
This paper is proposing a general periodicity result concerning any
deterministic and memoryless scheduling algorithm (including
non-work-conserving algorithms), for any context, on identical multiprocessor
platforms. By context we mean the hardware architecture (uniprocessor,
multicore), as well as task constraints like critical sections, precedence
constraints, self-suspension, etc. Since the result is based only on the
releases and deadlines, it is independent from any other parameter. Note that
we do not claim that the given interval is minimal, but it is an upper bound
for any cycle of any feasible schedule provided by any deterministic and
memoryless scheduler
Packing Sporadic Real-Time Tasks on Identical Multiprocessor Systems
In real-time systems, in addition to the functional correctness recurrent
tasks must fulfill timing constraints to ensure the correct behavior of the
system. Partitioned scheduling is widely used in real-time systems, i.e., the
tasks are statically assigned onto processors while ensuring that all timing
constraints are met. The decision version of the problem, which is to check
whether the deadline constraints of tasks can be satisfied on a given number of
identical processors, has been known -complete in the strong sense.
Several studies on this problem are based on approximations involving resource
augmentation, i.e., speeding up individual processors. This paper studies
another type of resource augmentation by allocating additional processors, a
topic that has not been explored until recently. We provide polynomial-time
algorithms and analysis, in which the approximation factors are dependent upon
the input instances. Specifically, the factors are related to the maximum ratio
of the period to the relative deadline of a task in the given task set. We also
show that these algorithms unfortunately cannot achieve a constant
approximation factor for general cases. Furthermore, we prove that the problem
does not admit any asymptotic polynomial-time approximation scheme (APTAS)
unless when the task set has constrained deadlines, i.e.,
the relative deadline of a task is no more than the period of the task.Comment: Accepted and to appear in ISAAC 2018, Yi-Lan, Taiwa
- …