Multiprocessor task scheduling plays a fundamental role in parallel applications and distributed networks. All of the methods for this kind of scheduling are concerned with achieving optimal running time. In this way parallel execution of tasks on several processors based on precedence graph should be considered. In this study, first a new heuristic method has been introduced which improved the execution time of some precedence graphs. Furthermore, we presented a novel immune genetic approach for multiprocessor task scheduling problem. Finally, combination of the proposed heuristic and the genetic approach makes a new hybrid scheme which is better than other well known and recent methods
Until now, several methods have been presented to optimally solve the multiprocessor task scheduling...
The multiprocessor task scheduling problem is an NP-complete problem that is difficult to solve via ...
In scheduling, a set of machines in parallel is a setting that is important, from both the theoretic...
Multiprocessor task scheduling is an important problem in parallel applications and distributed syst...
Efficient multiprocessor task scheduling is a long-studied and difficult problem that continues to b...
The common problem of multiprocessor scheduling can be defined as allocating a task graph in a multi...
International audienceThe problem of multiprocessor scheduling consists in finding a schedule for a ...
Task Scheduling problem for heterogeneous systems is concerned with arranging the various tasks to b...
This paper presents the development of genetic algorithm approach to schedule tasks on a multiproces...
Parallel Processing refers to the concept of speeding-up the execution of a task by dividing the tas...
Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to ...
In this paper, we present a task-scheduling heuristic, based on parallel genetic algorithm (PGA). Th...
Abstract- A parallel genetic algorithm has been developed to dynamically schedule heterogeneous task...
The paper presents a novel hybrid genetic algorithm (HGA) for a deterministic scheduling problem whe...
Present work considers the minimization of the bi-criteria function including weighted sum of makesp...
Until now, several methods have been presented to optimally solve the multiprocessor task scheduling...
The multiprocessor task scheduling problem is an NP-complete problem that is difficult to solve via ...
In scheduling, a set of machines in parallel is a setting that is important, from both the theoretic...
Multiprocessor task scheduling is an important problem in parallel applications and distributed syst...
Efficient multiprocessor task scheduling is a long-studied and difficult problem that continues to b...
The common problem of multiprocessor scheduling can be defined as allocating a task graph in a multi...
International audienceThe problem of multiprocessor scheduling consists in finding a schedule for a ...
Task Scheduling problem for heterogeneous systems is concerned with arranging the various tasks to b...
This paper presents the development of genetic algorithm approach to schedule tasks on a multiproces...
Parallel Processing refers to the concept of speeding-up the execution of a task by dividing the tas...
Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to ...
In this paper, we present a task-scheduling heuristic, based on parallel genetic algorithm (PGA). Th...
Abstract- A parallel genetic algorithm has been developed to dynamically schedule heterogeneous task...
The paper presents a novel hybrid genetic algorithm (HGA) for a deterministic scheduling problem whe...
Present work considers the minimization of the bi-criteria function including weighted sum of makesp...
Until now, several methods have been presented to optimally solve the multiprocessor task scheduling...
The multiprocessor task scheduling problem is an NP-complete problem that is difficult to solve via ...
In scheduling, a set of machines in parallel is a setting that is important, from both the theoretic...