An improved rank-based genetic algorithm with limited iterations for grid scheduling (original) (raw)

Rank-Based Genetic Algorithm with Limited Iteration for Grid Scheduling

2009

In Grid Computing the number of resources and tasks is usually very large, which makes the scheduling task very complex optimization problem. Genetic algorithms (GAs) have been broadly used to solve these NP-complete problems efficiently. On the other hand, the Standard Genetic algorithm (SGA) is too slow when used in a realistic scheduling due to its time-consuming iteration. This paper proposes a new Rank-based Roulette Wheel Selection Genetic Algorithm (RRWSGA) for scheduling independent tasks in the grid environment, which increases the performance and the quality of schedule with a limited number of iterations, RRWSGA improves the reliability in the selection process while matching an acceptable output. A fast reduction of makespan making the RRWSGA of practical concern for grid environment. The results are encouraging, and can be used for real-world scheduling problems.

An Improved Genetic Algorithm with Limited Iteration for Grid Scheduling

2007

In grid environment the numbers of resources and tasks to be scheduled are usually variable. This kind of characteristics of grid makes the scheduling approach a complex optimization problem. Genetic algorithm (GA) has been widely used to solve these difficult NP-complete problems. However the conventional GA is too slow to be used in a realistic scheduling due to its time-consuming iteration. This paper presents an improved genetic algorithm for scheduling independent tasks in grid environment, which can increase search efficiency with limited number of iteration by improving the evolutionary process while meeting a feasible result.

Rank Based Genetic Scheduler for Grid Computing Systems

2010

Computational grids have become attractive and promising platforms for solving large-scale high-performance applications of multi-institutional interest. However, the management of resources and computational tasks is a critical and complex undertaking as these resources and tasks are geographically distributed and a heterogeneous in nature. This paper proposes a novel Rank Based Genetic Scheduler for Grid Computing Systems (RGSGCS) for scheduling independent tasks in the grid environment by minimizing Makespan and Flowtime. The novel RGSGCS speeds up convergence and shortens the search time better than Standard Genetic Algorithm (SGA) using Rank-based fitness, at the same time the heuristic initialization of initial population using Minimum Completion Time (MCT) heuristic which allows RGSGCS to obtain a high quality feasible scheduling solution. The simulation results show that RGSGCS has better search time than SGA.

Use of genetic algorithms for scheduling jobs in large scale grid applications

Technological and Economic Development …, 2006

In this paper we present the implementation of Genetic Algorithms (GA) for job scheduling on computational grids that optimizes the makespan and the total flowtime. Job scheduling on computational grids is a key problem in large scale grid-based applications for solving complex problems. The aim is to obtain an efficient scheduler able to allocate a large number of jobs originated from large scale applications to grid resources. Several variations for GA operators are examined in order to identify which works best for the problem. To this end we have developed a grid simulator package to generate large and very large size instances of the problem and have used them to study the performance of GA implementation. Through extensive experimenting and fine tuning of parameters we have identified the configuration of operators and parameters that outperforms the existing implementations in the literature for static instances of the problem. The experimental results show the robustness of the implementation, improved performance of static instances compared to reported results in the literature and, finally, a fast reduction of the makespan making thus the scheduler of practical interest for grid environments.

Genetic Algorithm for Grid Scheduling using Best Rank Power

2009

The large computing capacity provided by grid systems is beneficial for solving complex problems by using many nodes of the grid at the same time. The usefulness of a grid system largely depends, among other factors, on the efficiency of the system regarding the allocation of jobs to grid resources.

Scheduling in a grid computing environment using genetic algorithms

… of the 16th International Parallel and …, 2002

We investigate the possibility to use the computing GRID in a flexible way to permit the maximum usage of resources. In our simulation the jobs submitted by the users provide a characterization of themself to help the system scheduler to do an optimal scheduling in case of resources contention. We use a genetic algorithms to select an optimal or suboptimal scheduling of the jobs. In this preliminary tests we show how the solution founded may maximize the total machine throughput considering not only the single job request but all the job requests during the scheduling process. In this work we show that it is possible to resolve conflicts in the usage of the total computing power and in the data locality for a reasonable number of jobs. © in that order but also the jobs ordered as

Tuning struggle strategy in genetic algorithms for scheduling in computational grids

2008

Abstract Job Scheduling on Computational Grids is gaining importance due to the need for efficient large-scale Grid-enabled applications. Among different optimization techniques addressed for the problem, Genetic Algorithm (GA) is a popular class of solution methods. As GAs are high level algorithms, specific algorithms can be designed by choosing the genetic operators as well as the evolutionary strategies. In this paper we focus on Struggle GAs and their tuning for the scheduling of independent jobs in computational grids.