6,593 research outputs found
Power-Aware Real-Time Scheduling upon Identical Multiprocessor Platforms
In this paper, we address the power-aware scheduling of sporadic
constrained-deadline hard real-time tasks using dynamic voltage scaling upon
multiprocessor platforms. We propose two distinct algorithms. Our first
algorithm is an off-line speed determination mechanism which provides an
identical speed for each processor. That speed guarantees that all deadlines
are met if the jobs are scheduled using EDF. The second algorithm is an on-line
and adaptive speed adjustment mechanism which reduces the energy consumption
while the system is running.Comment: The manuscript corresponds to the final version of SUTC 2008
conferenc
Multiprocessor Global Scheduling on Frame-Based DVFS Systems
In this ongoing work, we are interested in multiprocessor energy efficient
systems, where task durations are not known in advance, but are know
stochastically. More precisely, we consider global scheduling algorithms for
frame-based multiprocessor stochastic DVFS (Dynamic Voltage and Frequency
Scaling) systems. Moreover, we consider processors with a discrete set of
available frequencies
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
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_
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