407 research outputs found
Integrating Job Parallelism in Real-Time Scheduling Theory
We investigate the global scheduling of sporadic, implicit deadline,
real-time task systems on multiprocessor platforms. We provide a task model
which integrates job parallelism. We prove that the time-complexity of the
feasibility problem of these systems is linear relatively to the number of
(sporadic) tasks for a fixed number of processors. We propose a scheduling
algorithm theoretically optimal (i.e., preemptions and migrations neglected).
Moreover, we provide an exact feasibility utilization bound. Lastly, we propose
a technique to limit the number of migrations and preemptions
ILP-based approaches to partitioning recurrent workloads upon heterogeneous multiprocessors
The problem of partitioning systems of independent constrained-deadline sporadic tasks upon heterogeneous multiprocessor platforms is considered. Several different integer linear program (ILP) formulations of this problem, offering different tradeoffs between effectiveness (as quantified by speedup bound) and running time efficiency, are presented
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
Energy-Efficient Scheduling for Homogeneous Multiprocessor Systems
We present a number of novel algorithms, based on mathematical optimization
formulations, in order to solve a homogeneous multiprocessor scheduling
problem, while minimizing the total energy consumption. In particular, for a
system with a discrete speed set, we propose solving a tractable linear
program. Our formulations are based on a fluid model and a global scheduling
scheme, i.e. tasks are allowed to migrate between processors. The new methods
are compared with three global energy/feasibility optimal workload allocation
formulations. Simulation results illustrate that our methods achieve both
feasibility and energy optimality and outperform existing methods for
constrained deadline tasksets. Specifically, the results provided by our
algorithm can achieve up to an 80% saving compared to an algorithm without a
frequency scaling scheme and up to 70% saving compared to a constant frequency
scaling scheme for some simulated tasksets. Another benefit is that our
algorithms can solve the scheduling problem in one step instead of using a
recursive scheme. Moreover, our formulations can solve a more general class of
scheduling problems, i.e. any periodic real-time taskset with arbitrary
deadline. Lastly, our algorithms can be applied to both online and offline
scheduling schemes.Comment: Corrected typos: definition of J_i in Section 2.1; (3b)-(3c);
definition of \Phi_A and \Phi_D in paragraph after (6b). Previous equations
were correct only for special case of p_i=d_
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
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