Design and evaluation of job scheduling strategies for grid computing

Grid computing is intended to offer an easy and seamless access to remote resources. The scheduling task of allocating these resources automatically to user jobs is an essential part of a grid environment. This work discusses the evaluation and design of different scheduling strategies. A concept for the design process of such a scheduling system is presented. The evaluation of scheduling algorithms for single parallel machines is done by theoretical analysis and by simulation experiments. The theoretical approach by competitive analysis lead to bounds for the worst-case scenarios. As there is great interest in the scheduling performance of a real system installation, simulations have been applied for further evaluation. In addition to the theoretical analysis, the presented preemptive scheduling algorithm is also effcient in terms of makespan and average response time in a real system scenario if compared to other scheduling algorithms. In some of the examined scenarios the algorithm could outperform other common algorithms such as backfilling. Based on these results, scheduling algorithms for the grid environment have been developed. On one hand, these methods base on modifications of the examined conventional scheduling strategies for single parallel machines. On the other hand, a scheduling strategy with a market economic approach is presented. As a proof of concept a possible architecture of a scheduling environment is presented, which has been used for the evaluation of the presented algorithms. The work ends with a brief conclusion on the discussed scheduling strategies and gives an outlook on future work

Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid

The concept of Grid computing is becoming the most important research area in the high performance computing. Under this concept, the jobs scheduling in Grid computing has more complicated problems to discover a diversity of available resources, select the appropriate applications and map to suitable resources. However, the major problem is the optimal job scheduling, which Grid nodes need to allocate the appropriate resources for each job. In this paper, we combine Fuzzy C-Mean and Genetic Algorithms which are popular algorithms, the Grid can be used for scheduling. Our model presents the method of the jobs classifications based mainly on Fuzzy C-Mean algorithm and mapping the jobs to the appropriate resources based mainly on Genetic algorithm. In the experiments, we used the workload historical information and put it into our simulator. We get the better result when compared to the traditional algorithms for scheduling policies. Finally, the paper also discusses approach of the jobs classifications and the optimization engine in Grid scheduling

Способ иерархического планирования задач в Grid-системах

The improved method for hierarchical planning is proposed. Efficiency increasing of planning is achieved by physical links of processor’s compute node and ability to support duplex communication.Предложен усовершенствованный способ иерархического планирования. Повышение эффективности планирования достигается за счет физических каналов связи у процессоров вычислительного узла и, возможности поддержки дуплексного режима передачи информации

Performance Evaluation of Dynamic Scheduling for Grid Systems

Schedulers are applications responsible for job management including resource allocation for a specific job, splitting them to ensure parallel task execution, data management, event correlation, and service-level management capabilities. When Grids allotted a number of jobs, such applications have to consider the overhead time, cost regarding to and from Grid resources, job transmission and at job processing, Grid resources for allocation of the jobs. In this paper, it is proposed to investigate the performance of dynamic scheduling algorithm of schedulers for executing different number of tasks is evaluated

Genetic Algorithm Approach for Implementation of Job Scheduling Problem

A job scheduling maps and schedules the virtual machine (VM) resources to physical machines (VM) for getting the finest mapping result to achieve the proper system load balance. Job scheduling system tries to find the best suitable schedule in a system for VMs and PMs, by considering various on time restrictions into concern. The ultimate goal of job scheduling is to schedule adaptable virtual machines to physical machines, getting a suitable order in order to enhance resource utility. This research paper proposes an approach in order to discuss a Job Scheduling problem to progress resource utility with the help of Genetic Algorithm (GA). DOI: 10.17762/ijritcc2321-8169.15067

Exploration based Genetic Algorithm for Job Scheduling on Grid Computing

Grid computing presents a new trend to distribute and Internet computing to coordinate large scale heterogeneous resources providing sharing and problem solving in dynamic, multi- institutional virtual organizations. Scheduling is one of the most important problems in computational grid to increase the performance. Genetic Algorithm is adaptive method that can be used to solve optimization problems, based on the genetic process of biological organisms. The objective of this research is to develop a job scheduling algorithm using genetic algorithm with high exploration processes. To evaluate the proposed scheduling algorithm this study conducted a simulation using GridSim Simulator and a number of different workload. The research found that genetic algorithm get best results when increasing the mutation and these result directly proportional with the increase in the number of job. The paper concluded that, the mutation and exploration process has a good effect on the final execution time when we have large number of jobs. However, in small number of job mutation has no effects

Иерархический способ планирования для GRID

Paper is devoted to increasing of Grid systems real performance by effective scheduling method using. We propose an improved hierarchical scheduling method that uses one of the three optimization criterion determined by user. This method efficiency provides by creation of the new list scheduling algorithms for homogeneous and heterogeneous Grid system nodes and creation of task graph transformation.Статья посвящена повышению пользовательской производительности Grid систем за счет более эффективного способа планирования вычислений. Предложен усовершенствованный иерархический способ планирования, ориентированный на один из трех критериев оптимизации по выбору пользователя. Эффективность данного способа обеспечивается за счет разработанных авторами списочных алгоритмов для однородных и неоднородных узлов Grid систем, а также предложенных способов трансформации графов задач