Genetic Algorithm Based Scheduler for Computational Grids (original) (raw)
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Genetic algorithm based schedulers for grid computing systems
2007
Abstract. In this paper we present Genetic Algorithms (GAs) based schedulers for efficiently allocating jobs to resources in a Grid system. Scheduling is a key problem in emergent computational systems, such as Grid and P2P, in order to benefit from the large computing capacity of such systems. We present an extensive study on the usefulness of GAs for designing efficient Grid schedulers when makespan and flowtime are minimized.
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
LGR: The New Genetic Based Scheduler for Grid Computing Systems
International Journal of Computer and Electrical Engineering, 2009
The computational grid provides a promising platform for the deployment of various high-performance computing applications. In computational grid, an efficient scheduling of task onto the processors that minimizes the entire execution time is vital for achieving a high performance. Solving this problem is very hard and many attempts have been made to solve the problem. Using classical algorithms, With regard to the complexity of this problem, is not the good way; so the indefinite method acts better than classical method. Evolutionary algorithms are the best choice for solving this hard problem. In this paper, contrary to prior ways, the new string representation has been used, communication costs has not been ignored and presents as a major factor for reaching to optimum solution
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.
GLOA: A New Job Scheduling Algorithm for Grid Computing
2013
The purpose of grid computing is to produce a virtual supercomputer by using free resources available through widespread networks such as the Internet. This resource distribution, changes in resource availability, and an unreliable communication infrastructure pose a major challenge for efficient resource allocation. Because of the geographical spread of resources and their distributed management, grid scheduling is considered to be a NP-complete problem. It has been shown that evolutionary algorithms offer good performance for grid scheduling. This article uses a new evaluation (distributed) algorithm inspired by the effect of leaders in social groups, the group leaders' optimization algorithm (GLOA), to solve the problem of scheduling independent tasks in a grid computing system. Simulation results comparing GLOA with several other evaluation algorithms show that GLOA produces shorter makespans.
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.
Hierarchic Genetic Scheduler Of Independent Jobs In Computational Grid Environment
ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera, 2009
In this work we present an implementation of Hierarchic Genetic Strategy (HGS) for Independent Job Scheduling on Computational Grids. In our formulation of the scheduling problem, makespan and flowtime parameters are simultaneously optimized. The efficient assignment of jobs to machines that optimizes both objectives is crucial for many Grid systems. The objective of this work is to examine several variations of HGS operators in order to identify a configuration of operators and parameters that works best for the problem. Differently from classical GA algorithms, which maintain only an unstructured population of individuals, HGS performs by many small populations enabling a concurrent search in the optimization domain. From the experimental study we observed that HGS implementation outperforms existing classical GA schedulers for most of considered instances of a static benchmark for the problem.
A Genetic-Based Scheduling Algorithm to Minimize the Makespan of the Grid Applications
Communications in Computer and Information Science, 2010
High throughput computing (HTC) is of great importance in grid computing environments. HTC is aimed at minimizing the total makespan of all of the tasks submitted to the grid environment in long execution of the system. To achieve HTC in grids, suitable task scheduling algorithms should be applied to dispatch the submitted tasks to the computational resources appropriately. In this paper, a new task scheduling algorithm is proposed to assign the tasks to the grid resources with goal of minimizing the total makespan of the environment. The proposed algorithm uses genetic approach to find the most suitable match between the tasks and grid resources. The simulation results obtained from applying the proposed algorithm to schedule independent and sequential tasks to the grid resources demonstrate the applicability of the algorithm in grid environments.
Genetic Algorithm for Independent Job Scheduling in Grid Computing
MENDEL
Grid computing refers to the infrastructure which connects geographically distributed computers ownedby various organizations allowing their resources, such as computational power and storage capabilities, to beshared, selected, and aggregated. Job scheduling is the problem of mapping a set of jobs to a set of resources.It is considered one of the main steps to e ciently utilise the maximum capabilities of grid computing systems.The problem under question has been highlighted as an NP-complete problem and hence meta-heuristic methodsrepresent good candidates to address it. In this paper, a genetic algorithm with a new mutation procedure tosolve the problem of independent job scheduling in grid computing is presented. A known static benchmark forthe problem is used to evaluate the proposed method in terms of minimizing the makespan by carrying out anumber of experiments. The obtained results show that the proposed algorithm performs better than some knownalgorithms taken from the lit...
Future Generation Computer Systems, 2011
Independent Job Scheduling is one of the most useful versions of scheduling in grid systems. It aims at computing efficient and optimal mapping of jobs and/or applications submitted by independent users to the grid resources. Besides traditional restrictions, mapping of jobs to resources should be computed under high degree of heterogeneity of resources, the large scale and the dynamics of