scholarly journals Load Balancing in Computational Grid Using Genetic Algorithm

2012 ◽  
Vol 1 (1) ◽  
pp. 8-17 ◽  
Author(s):  
S. Prakash ◽  
D. P. Vidyarthi
Keyword(s):  
Genetic Algorithm ◽  
Load Balancing ◽  
Computational Grid

scholarly journals An Optimal Solution in cloud computing for Load Balancing using Genetic Algorithm

2019 ◽  
Vol 8 (6) ◽  
pp. 2490-2494
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Optimal Solution ◽  
Cloud Services ◽  
Research Trend ◽  
Schedule Length ◽  
Research Gap ◽  
Load Balancing Algorithm

Cloud computing is a research trend which bring various cloud services to the users. Cloud environment face various challenges and issues to provide efficient services. In this paper, a novel Genetic Algorithm based load balancing algorithm has been implemented to balance the load in the network. The literature review has been studied to understand the research gap. More specifically, load balancing technique authenticate the network by enabling Virtual Machines (VM). The proposed technique has been further evaluated using the Schedule Length Runtime (SLR) and Energy consumption (EC) parameters. Overall, the effective results has been obtained such as 46% improvement in consuming the energy and 12 % accuracy for the SLR measurement. In addition, results has been compared with the conventional approaches to validate the outcomes.

Using Genetic Algorithm for Scheduling Tasks of Computational Grids in the Gridsim Simulator

2014 ◽  
pp. 521-535
Author(s):  
João Phellipe ◽  
Carla Katarina ◽  
Francisco das Chagas ◽  
Dario Aloise
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Distributed System ◽  
Task Scheduling ◽  
Computational Grid ◽  
Computer Processing ◽  
Computational Grids ◽  
Processing Power

Computer processing power has evolved considerably in recent years. However, there are problems that still require many machines to perform a large amount of processing in a parallel and distributed way. In this context, the task scheduling in a distributed system present many algorithms. In this chapter, the authors present a scheduler based on genetic algorithms in order to distribute tasks more efficiently in a computational grid; it has been implemented in GRIDSIM, a computational grid simulator with the features and attributes of a real grid.

Energy Aware Grid Scheduling for Dependent Task Using Genetic Algorithm

2016 ◽  
Vol 7 (2) ◽  
pp. 18-36
Author(s):  
Shiv Prakash ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Job Scheduling ◽  
Computing System ◽  
Computational Grid ◽  
Task Execution ◽  
Energy Aware ◽  
Optimal Energy ◽  
Proposed Model ◽  
Dynamic Voltage

Consumption of energy in the large computing system is an important issue not only because energy sources are depleting fast but also due to the deteriorating environmental conditions. A computational grid is a large heterogeneous distributed computing platform which consumes enormous energy in the task execution. Energy-aware job scheduling, in the computational grid, is an important issue that has been addressed in this work. If the tasks are properly scheduled, keeping the optimal energy concern, it is possible to save the energy consumed by the system in the task execution. The prime objective, in this work, is to schedule the dependent tasks of a job, on the grid nodes with optimal energy consumption. Energy consumption is estimated with the help of Dynamic Voltage Frequency Scaling (DVFS). Makespan, while optimizing the energy consumption, is also taken care of in the proposed model. GA is applied for the purpose and therefore the model is named as Energy Aware Genetic Algorithm (EAGA). Performance evaluation of the proposed model is done using GridSim simulator. A comparative study with other existing models viz. min-min and max-min proves the efficacy of the proposed model.

Hull mixed-model assembly line balancing using a multi-objective genetic algorithm simulated annealing optimization approach

2016 ◽  
Vol 25 (1) ◽  
pp. 30-40 ◽  
Author(s):  
Yu-guang Zhong
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Load Balancing ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Optimization Approach ◽  
Multi Objective ◽  
Mutation Operators ◽  
Mixed Model Assembly Line

Hull assembly line balancing has significant impact on performance of shipbuilding system and is usually a multi-objective optimization problem. In this article, the primary objectives of the hull assembly line balancing are to minimize the number of workstations, to minimize the static load balancing index, to minimize the dynamic load balancing index between workstations, and to minimize the multi-station-associated complexity. Because this problem comes under combinatorial optimization category and is non-deterministic polynomial-time hard, an improved genetic algorithm simulated annealing is presented. In genetic algorithm simulated annealing, the task sequence numbers are used as chromosomes, and selection, crossover, and mutation operators only deal with the elements of task set instead of the ones of the problem space. In order to prevent the algorithm appearing early convergence or getting local optimal result, the simulated annealing algorithm is used to deal with the individuals. Meanwhile, the algorithm is embedded with the hierarchical scheduling tactics in order to solve the selection problem on optimal solution in the Pareto-optimal set. A number of benchmark problems are solved to prove the superior efficiency of the proposed algorithm. Finally, a case study of the optimization of a hull assembly line was given to illustrate the feasibility and effectiveness of the method.

scholarly journals Genetic Algorithm based Resource Broker for Computational Grid

2013 ◽  
Vol 10 ◽  
pp. 572-580 ◽  
Author(s):  
Susmita Singh ◽  
Madhulina Sarkar ◽  
Sarbani Roy ◽  
Nandini Mukherjee
Keyword(s):  
Genetic Algorithm ◽  
Computational Grid ◽  
Resource Broker
Sign in / Sign up

Export Citation Format

Share Document