The Grid is an emerging technology for enabling resource sharing and coordinated problem solving in dynamic multi-institutional virtual organizations. This paper presents algorithms, methods, and software for a Grid resource manager, responsible for resource brokering and scheduling in Grids. The broker selects computing resources based on actual job requirements and a number of criteria identifying the available resources, with the aim to minimize the turnaround time for the individual application. In pervious work, we proposed Grid-JQA [8] [9]. In this work we propose an aggregation formula for the QoS parameters. The formula is a unit less combination of the parameters together with weighting factors. It is shown that the formula needs to put into a threshold consideration. A discussion on the threshold and its level is also provided. The paper is finalized by the results obtained from simulation and a comparison study.

One of the important problems in grid environments is data replication in grid sites. Reliability and availability of data replication in some cases is considered low. To separate sites with high reliability and high availability of sites with low availability and low reliability, clustering can be used. In this study, the data grid dynamically evaluate and predict the condition of the sites. The reliability and availability of sites were calculated and it was used to make decisions to replicate data. With these calculations, we have information on the locations of users in grid with reliability and availability or cost commensurate with the value of the work they did. This information can be downloaded from users who are willing to send them data with suitable reliability and availability. Simulation results show that the addition of the two parameters, reliability and availability, assessment criteria have been improved in certain access patterns.

This paper investigates the scheduling problem of independent tasks in market-based grids. The heterogeneity and autonomy of resources in grids highlight the need for more flexible models and approaches to be exploited in these environments. To address this issue, a two-level market model is presented in this paper to schedule tasks to the grid resources. In the proposed model, users submit their own tasks to a centralized resource manager named meta-scheduler.Meta-scheduler knows general information about each of the administrative domains, called sites, existing in the low-level part of the model. Using the information gathered from all of the sites, meta-scheduler selects more suitable sites to execute the tasks with the aim of minimizing the overall cost of tasks execution. In this model, meta-scheduler not only targets the minimization of overall cost of the tasks execution, but also achieves this objective without any presumption about the policies and algorithms implemented in the lower layers of the system which addresses the dynamicity of environment. In addition to the two-level market model, a new task scheduling algorithm called GA-VNS which is an enhanced version of genetic algorithm is presented to be applied in market-based grids. GA-VNS can be used by local schedulers in each site with the policy of cost minimization considering the makespan of the system as a second criterion. The results obtained from performance evaluation of GA-VNS and other well-known algorithms in this context show that GA-VNS outperforms other algorithms in terms of the overall cost of tasks execution.

Large-scale data management is a critical problem in a distributed system such as cloud, P2P system, World Wide Web (WWW), and Data Grid. One of the effective solutions is data replication technique, which efficiently reduces the cost of communication and improves the data reliability and response time. Various replication methods can be proposed depending on when, where, and how replicas are generated and removed. In this paper, different replication algorithms are investigated to determine which attributes are assumed in a given algorithm and which are declined. We provide a tabular representation of important factors to facilitate the future comparison of data replication algorithms. This paper also presents some interesting discussions about future works in data replication by proposing some open research challenges.

Introduction: With writing the special code based on Grid search technique, best method for describing the elements energy spectrum is introduced. This method is suitable for investigation of Atomic, molecular and nuclear Energy spectrum, laser pulses and study of spectrum that obtained by laser interaction with cell and human body.Microraman spectrum of eye lens that fitted by De. Mul and time spectrum of one molecule that excited by 585 nm laser and fitted by Gnu plot software. In a special application, this method is used for 22Na spectrums.Material and Methods: For fitting mathematical function to a set of experimental data, the first step is seeing data plot in every software. Then, an initial mathematical function is guessed and the function parameters change as far as possible for initial fitting. After this step, by using this special code based on Grid search technique, is the X2 changes between function data and experimental data are compared. If the X2 is high, it will reduce by introducing small change in every parameter of mathematical function. This is done step, by step, so the X2 reaches to the lowest.Results: By this method, we can have the formulation of element energy spectrum. For example, the equation of 22Na energy spectrum’s description with least squares fitted by experiment data is: N=199.0477 +76100EXP (0.00167 (E-508.7539) 2) + 75100EXP (0.00168 (E-509.2428) 2) + 3450.8070 EXP (0.0000831(E-655.356) 2) + 4002.813EXP (0.0001431(E-839.401) 2) + 11000EXP (0.00105 (E-1048.0230) 2) Fitting start with fit parameter X2=1107.6 and finished with X2 =44.16.Conclusion: By using the special code based on Grid search technique, we try to introduce the best method for describing of the elements energy spectrum.

Data Grid is an infrastructure that controls huge amount of data files, and provides intensive computational resources across geographically distributed collaboration. The heterogeneity and geographic dispersion of grid resources and applications place some complex problems such as job scheduling. Most existing scheduling algorithms in Grids only focus on one kind of Grid jobs which can be data-intensive or computation-intensive. However, only considering one kind of jobs in scheduling does not result in suitable scheduling in the viewpoint of all systems, and sometimes causes wasting of resources on the other side. To address the challenge of simultaneously considering both kinds of jobs, a new Integrated Job Scheduling Strategy (IJSS) is proposed in this paper. At one hand, the IJSS algorithm considers both data and computational resource availability of the network, and on the other hand, considering the corresponding requirements of each job, it determines a value called W to the job. Using the W value, the importance of two aspects (being data or computation intensive) for each job is determined, and then the job is assigned to the available resources. The simulation results with OptorSim show that IJSS outperforms comparing to the existing algorithms mentioned in literature as number of jobs increases.