8,714 research outputs found
Assigning real-time tasks on heterogeneous multiprocessors with two types of processors
Consider the problem of scheduling a set of implicitdeadline
sporadic tasks on a heterogeneous multiprocessor
so as to meet all deadlines. Tasks cannot migrate and
the platform is restricted in that each processor is either
of type-1 or type-2 (with each task characterized by a
different speed of execution upon each type of processor).
We present an algorithm for this problem with a timecomplexity
of O(n·m), where n is the number of tasks and
m is the number of processors. It offers the guarantee that
if a task set can be scheduled by any non-migrative algorithm
to meet deadlines then our algorithm meets deadlines
as well if given processors twice as fast. Although this result
is proven for only a restricted heterogeneous multiprocessor,
we consider it significant for being the first realtime
scheduling algorithm to use a low-complexity binpacking
approach to schedule tasks on a heterogeneous
multiprocessor with provably good performance
A conjecture about provably good task assignment on heterogeneous multiprocessor platforms but with a stronger adversary
Consider the problem of scheduling a set of
implicit-deadline sporadic tasks to meet all deadlines on a
heterogeneous multiprocessor platform. We use an algorithm
proposed in [1] (we refer to it as LP-EE) from state-of-the-art
for assigning tasks to heterogeneous multiprocessor platform
and (re-)prove its performance guarantee but for a stronger
adversary.We conjecture that if a task set can be scheduled to
meet deadlines on a heterogeneous multiprocessor platform
by an optimal task assignment scheme that allows task
migrations then LP-EE meets deadlines as well with no
migrations if given processors twice as fast. We illustrate
this with an example
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_
Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks
This paper considers multiprocessor task scheduling in a multistage hybrid
flow-shop environment. The problem even in its simplest form is NP-hard in the
strong sense. The great deal of interest for this problem, besides its
theoretical complexity, is animated by needs of various manufacturing and
computing systems. We propose a new approach based on limited discrepancy
search to solve the problem. Our method is tested with reference to a proposed
lower bound as well as the best-known solutions in literature. Computational
results show that the developed approach is efficient in particular for
large-size problems
Efficient mapping algorithms for scheduling robot inverse dynamics computation on a multiprocessor system
Two efficient mapping algorithms for scheduling the robot inverse dynamics computation consisting of m computational modules with precedence relationship to be executed on a multiprocessor system consisting of p identical homogeneous processors with processor and communication costs to achieve minimum computation time are presented. An objective function is defined in terms of the sum of the processor finishing time and the interprocessor communication time. The minimax optimization is performed on the objective function to obtain the best mapping. This mapping problem can be formulated as a combination of the graph partitioning and the scheduling problems; both have been known to be NP-complete. Thus, to speed up the searching for a solution, two heuristic algorithms were proposed to obtain fast but suboptimal mapping solutions. The first algorithm utilizes the level and the communication intensity of the task modules to construct an ordered priority list of ready modules and the module assignment is performed by a weighted bipartite matching algorithm. For a near-optimal mapping solution, the problem can be solved by the heuristic algorithm with simulated annealing. These proposed optimization algorithms can solve various large-scale problems within a reasonable time. Computer simulations were performed to evaluate and verify the performance and the validity of the proposed mapping algorithms. Finally, experiments for computing the inverse dynamics of a six-jointed PUMA-like manipulator based on the Newton-Euler dynamic equations were implemented on an NCUBE/ten hypercube computer to verify the proposed mapping algorithms. Computer simulation and experimental results are compared and discussed
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
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