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.

A comparative study of energy-aware scheduling algorithms for computational grids

2016 ◽  
Vol 117 ◽  
pp. 153-165 ◽  
Author(s):  
Silvana Teodoro ◽  
Andriele Busatto do Carmo ◽  
Daniel Couto Adornes ◽  
Luiz Gustavo Fernandes
Keyword(s):  
Comparative Study ◽  
Scheduling Algorithms ◽  
Computational Grids ◽  
Energy Aware ◽  
Energy Aware Scheduling

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.

Predictive Reactive Approach for Energy-Aware Scheduling and Control of Flexible Manufacturing Processes

2018 ◽  
Vol 11 (4) ◽  
pp. 43-62 ◽  
Author(s):  
Mohammed El Amine Meziane ◽  
Noria Taghezout
Keyword(s):  
Energy Consumption ◽  
Supply Chains ◽  
Flexible Manufacturing ◽  
Manufacturing Processes ◽  
Energy Aware ◽  
Manufacturing Efficiency ◽  
Multi Objective Genetic Algorithm ◽  
Energy Needs ◽  
Energy Aware Scheduling ◽  
And Control

Manufacturing processes are responsible for a considerable amount of global energy consumption and world CO2 emissions. Reducing energy consumption during manufacturing is considered one of the most important strategies in contributing to the green supply chain. In this context, the authors propose a new predictive-reactive approach to control energy consumption during manufacturing processes. In addition to forecasting the energy needs, the proposed approach controls the uncertainty of energy volatility and limits energy waste during manufacturing processes. With the integration of this economic-environmental manufacturing efficiency in supply chains, and controlling uncertainty, this approach positively contributes to green and agile supply chains. A multi-objective genetic algorithm (NSGA-2) is proposed as a predictive method, and a new reactive method is developed to dynamically control the energy consumption throughout the peak energy consumption in real time. The approach was tested on the AIP-PRIMECA benchmark, which reflects a real production cell.

scholarly journals Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Weizhe Zhang ◽  
Hucheng Xie ◽  
Boran Cao ◽  
Albert M. K. Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Real Time ◽  
Task Scheduling ◽  
Particle Swarm ◽  
Energy Aware ◽  
Swarm Optimization ◽  
Heterogeneous Multiprocessors ◽  
Time Task ◽  
Feasible Solutions

Energy consumption in computer systems has become a more and more important issue. High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors. In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors. Then we propose a particle swarm optimization (PSO) based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions. Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions. Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.

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