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

Adaptive intelligent grid scheduling system

Grid technologies are established to share the large-scale heterogeneous resources over multiple administrative domains for processing the application. In these technologies, the grid scheduling problem is crucial that must be solved in order to achieve multiple objectives within different stakeholders (end-users, owner resources and administrators) preferences. The aim of this research is to design and implement the Adaptive Intelligent Grid Scheduling System (AIGSS) in order to achieve multiple objectives named Makespan Time, Grid Efficiency and Total delayed jobs. The popular meta-heuristic algorithms, namely Ant Colony Optimization (ACO) and Tabu Search (TS) algorithms are proposed and developed to maintain the selecting appropriate grid resource to execute each job within the different job inter-arrival times and grid resources. Additionally, the clustering technique named Fuzzy C-Means (FCM) algorithm is proposed for clustering the groups of grid resources as well as jobs based on the degree of characteristic similarity. Moreover, a popular discrete event simulation tool, namely, GridSim toolkit and Alea simulation, is extended by developing the service modules on top of it. Therefore, the experiment is simulated as realistic grid environment in order to measure the proposed system. The experimental results show that the AIGSS provides reasonable multiple objectives to stakeholders within different job interarrival times and machines in grid system. In addition to the experimental results, the proposed system performs better than the other algorithms for different goals of each stakeholder. The performance of AIGSS is compared with the common and heuristic algorithms such as First-Come-First-Serve (FCFS) with Optimization, Earliest Deadline First (EDF), Minimum Tardiness Earliest Due Date (MTEDD), Minimum Completion Time (MCT), Opportunistic Load Balancing (OLB), MIN-MIN, Hill Climbing, EASY Backfilling, Simulated Annealing (SA), and Tabu Searching (TS)

Job online scheduling within dynamic grid environment

This paper proposes the idea of adaptive job scheduling algorithm by using hybrid Ant Colony Optimization (ACO) and Tabu algorithms. The idea behind the scheduling algorithm is evaluation of completion time of jobs in a service Grid. The algorithm comprises of two main techniques; first of all, Grid Information Service (GIS) collects information from each grid node, ACO evaluates complete time of jobs in possible grid nodes and then assigns job to appropriate grid node. ACO is used to minimize the average completion time of jobs through optimal job allocation on each node as well. While, Tabu algorithm is used to adjust performance of grid system because online jobs are submitted to grid system from time to time. This paper shows that the algorithm can find an optimal processor for each machine to allocate to a job that minimizes the tardiness time of a job when the job is scheduled in the system

Optimalisation of a job scheduler in the grid environment by using fuzzy C-mean

Grid computing is the principle in utilizing and sharing large-scale resources to solve complex scientific problems. Under this principle, Grid environment has problems in flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources. However, the major problems include optimal job scheduling, and which grid nodes allocate the resources for each job. This paper proposes the model for optimizing jobs scheduling in Grid environment. The model presents the results of the simulation of the Grid environment of jobs allocation to different nodes. We develop the results of job characteristics to three classifications depending on jobs run time in machines, which have been obtained using the optimization of jobs scheduling. The results prove the model by using Fuzzy c-mean clustering technique for predicting the characterization of jobs and optimization of jobs scheduling in Grid environment. This prediction and optimization engine will provide Jobs scheduling base upon historical information. This paper presents the need for such a prediction and optimization engine that discusses the approach for history-based prediction and optimization. Simulation runs demonstrate that our algorithm leads to better results than the traditional algorithms for scheduling policies used in Grid environment

A static jobs scheduling for independent jobs in Grid Environment by using Fuzzy C-Mean and Genetic algorithms

The concept of Grid computing is becoming a more important for the high performance computing world. Such flexible resource request could offer the opportunity to optimize several parameters, such as coordinated resource sharing among dynamic collections of individuals, institutions, and resources. Specifically, we investigate the static job scheduling algorithm for independent jobs. In this paper we propose and evaluate experimentally a static scheduling for independent jobs that rely on determining job characteristics at runtime and jobs allocate to resources. We present a static job scheduling algorithm by using Fuzzy C-Mean and Genetic algorithms. Our model presents the strategies of allocating jobs to different nodes, which we have developed the model by using Fuzzy C-Mean algorithm for prediction the characteristics of jobs that run in Grid environment and Genetic algorithm for jobs allocated to large scale sharing of resources. The performance of our model in a static job scheduling have researchers will be discussed. Our model has shown that the scheduling system will allocate jobs efficiently and effectively

Adaptive intelligence job online scheduling within dynamic grid environment based on gridsim

This paper concentrates on the design and implement of the grid system for study of adaptive job scheduling algorithm based on GridSim. The common problems of job scheduling in grid system like heterogeneous of jobs, resources and dynamic an arrival time of new jobs significantly changes, can be deal with this solution. The idea behind the adaptive job scheduling algorithm is the hybrid algorithms that consist of Ant Colony Optimization (ACO) and Tabu algorithms. Additionally, the provided common information from Grid Information Service (GIS) and an arrival new job are calculated by Fuzzy C-Means (FCM) algorithm in order· to evaluate the current status of resources and groups of arrival jobs. Moreover, both dynamic and static information are handled by the solution. In static case, the resource information such as a number of CPUs of a machine, CPU speed, a number machine in the grid system is significantly known in advance while dynamic information like the arrival jobs that are submitted to the system any time during simulation. In the results, this paper shows the comparison results between the adaptive job scheduling algorithms and the traditional algorithms

Meta-scheduler in Grid environment with multiple objectives by using genetic algorithm

Grid computing is the principle in utilizing and sharing large-scale resources of heterogeneous computing systems to solve the complex scientific problem. Such flexible resource request could offer the opportunity to optimize several parameters, such as coordinated resource sharing among dynamic collections of individuals, institutions, and resources. However, the major opportunity is in optimal job scheduling, which Grid nodes need to allocate the resources for each job. This paper proposes and evaluates a new method for job scheduling in heterogeneous computing Systems. Its objectives are to minimize the average waiting time and make-span time. The minimization is proposed by using a multiple objective genetic algorithm (GA), because the job scheduling problem is NP-hard problem. Our model presents the strategies of allocating jobs to different nodes. In this preliminary tests we show how the solution founded may minimize the average waiting time and the make-span time in Grid environment. The benefits of the usage of multiple objective genetic algorithm is improving the performance of the scheduling is discussed. The simulation has been obtained using historical information to study the job scheduling in Grid environment. The experimental results have shown that the scheduling system using the multiple objective genetic algorithms can allocate jobs efficiently and effectively