scholarly journals Static vs. Dynamic List-Scheduling Performance Comparison

10.14311/490 ◽  
2003 ◽  
Vol 43 (6) ◽  
Author(s):  
T. Hagras ◽  
J. Janeček
Keyword(s):  
Task Scheduling ◽  
Optimal Solution ◽  
Scheduling Algorithms ◽  
Low Complexity ◽  
Performance Comparison ◽  
List Scheduling ◽  
Scheduling Problem ◽  
Computing Environments ◽  
Np Complete ◽  
List Scheduling Algorithms

The problem of efficient task scheduling is one of the most important and most difficult issues in homogeneous computing environments. Finding an optimal solution for a scheduling problem is NP-complete. Therefore, it is necessary to have heuristics to find a reasonably good schedule rather than evaluate all possible schedules. List-scheduling is generally accepted as an attractive approach, since it pairs low complexity with good results. List-scheduling algorithms schedule tasks in order of priority. This priority can be computed either statically (before scheduling) or dynamically (during scheduling). This paper presents the characteristics of the two main static and the two main dynamic list-scheduling algorithms. It also compares their performance in dealing with random generated graphs with various characteristics.

scholarly journals InterRC: An Inter-Resources Collaboration Heuristic for Scheduling Independent Tasks on Heterogeneous Distributed Environments

MENDEL ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 179-188
Author(s):  
Abdelhamid Khiat ◽  
Abdelkamel Tari
Keyword(s):  
Task Scheduling ◽  
Initial Solution ◽  
Distributed Environments ◽  
Scheduling Problem ◽  
Reasonable Time ◽  
Independent Task ◽  
Np Hard Problem ◽  
Computing Environments ◽  
Scheduling Heuristic ◽  
Independent Tasks

The independent task scheduling problem in distributed computing environments with makespan optimization as an objective is an NP-Hard problem. Consequently, an important number of approaches looking to approximate the optimal makespan in reasonable time have been proposed in the literature. In this paper, a new independent task scheduling heuristic called InterRC is presented. The proposed InterRC solution is an evolutionary approach, which starts with an initial solution, then executes a set of iterations, for the purpose of improving the initial solution and close the optimal makespan as soon as possible. Experiments show that InterRC obtains a better makespan compared to the other efficient algorithms.

Research on Intelligent Algorithms for Energy-Aware Scheduling in Computational Grids

2014 ◽  
Vol 926-930 ◽  
pp. 3187-3190
Author(s):  
Yu Jing Wang ◽  
Lin Wu ◽  
Chao Kun Yan
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Scheduling Algorithms ◽  
Computational Grids ◽  
Energy Aware ◽  
Intelligent Algorithms ◽  
Complete Problem ◽  
Energy Aware Scheduling ◽  
Np Complete

As the features in Computational Grids such as heterogeneous and dynamic, grid task scheduling is an NP-complete problem. For existing energy consumption in CGs ,and based on the study of scheduling algorithms and energy, this thesis selects four kinds of intelligent algorithms GA, DE, HC and SA to analysis and implementation, and relatively researchs their makespan and energy consumption for energy-aware scheduling.

scholarly journals Teaching-Learning based Task Scheduling Optimization in Cloud Computing Environments

2019 ◽  
Vol 8 (2) ◽  
pp. 2952-2958
Keyword(s):  
Task Scheduling ◽  
Research Work ◽  
Search Space ◽  
Scheduling Problem ◽  
Optimal Resource ◽  
Np Hard Problem ◽  
Cloud Environments ◽  
Computing Environments ◽  
Teaching Learning ◽  
First Time

Generating optimal task scheduling plans in cloud environments is a tedious task as it is a np-hard problem. The optimal resource allocation in cloud environments involves more search space and time consuming. Therefore, recent researchers are focused on implementation of artificial intelligence to solve task scheduling problem. In this paper, a new and efficient evolutionary algorithm named teaching-learning based algorithm has been implemented first time to solve the task scheduling problem in cloud environments. The current research work considers the task scheduling problem as a multi-objective optimization problem. The proposed algorithm finds the best solution by minimizing the execution time and response time while maximizing the throughput of all resources to complete the assigned tasks.

scholarly journals Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
Ruey-Maw Chen ◽  
Chuin-Mu Wang
Keyword(s):  
Grid Computing ◽  
Task Scheduling ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Resource Assignment ◽  
Grid Environment ◽  
Standard Particle Swarm Optimization ◽  
Communication Topology ◽  
Np Complete

The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimode project scheduling are proposed to help in solving interesting scheduling problems. They are the best performance resource heuristic and the latest finish time heuristic. These two heuristics applied to the PSO scheme are for speeding up the search of the particle and improving the capability of finding a sound schedule. Moreover, both global communication topology and local ring communication topology are also investigated for efficient study of proposed scheme. Simulation results demonstrate that the proposed approach in this investigation can successfully solve the task-resource assignment problems in grid computing and similar scheduling problems.

List scheduling algorithms to minimize the makespan on identical parallel machines

Top ◽  
2001 ◽  
Vol 9 (2) ◽  
pp. 243-269 ◽  
Author(s):  
Ethel Mokotoff ◽  
José Luis Jimeno ◽  
Ana Isabel Gutiérrez
Keyword(s):  
Parallel Machines ◽  
Scheduling Algorithms ◽  
List Scheduling ◽  
Identical Parallel Machines ◽  
List Scheduling Algorithms
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