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

Performance Evaluation of Automatically Generated Data Parallel Programs

International audienceIn this paper, the problem of evaluating the performance of parallel programs generated by data parallel compilers is studied. These compilers take as input an application written in a sequential language augmented with data distribution directives and produce a parallel version based on the specifed partitioning of data. A methodology for evaluating the relationships existing among the program characteristics, the data distribution adopted, and the performance indices measured during the program execution is described. It consists of three phases: a "static" description of the program under study, a "dynamic" description, based on the measurement and the analysis of its execution on a real system, and the construction of a workload model, by using workload characterization techniques. Following such a methodology, decisions related to the selection of the data distribution to be adopted can be facilitated. The approach is exposed through the use of the Pandore environment, designed for the execution of sequential programs on distributed memory parallel computers. It is composed of a compiler, a runtime system and tools for trace and profile generation. The results of an experiment explaining the methodology are presented

Characterizing and Subsetting Big Data Workloads

Big data benchmark suites must include a diversity of data and workloads to be useful in fairly evaluating big data systems and architectures. However, using truly comprehensive benchmarks poses great challenges for the architecture community. First, we need to thoroughly understand the behaviors of a variety of workloads. Second, our usual simulation-based research methods become prohibitively expensive for big data. As big data is an emerging field, more and more software stacks are being proposed to facilitate the development of big data applications, which aggravates hese challenges. In this paper, we first use Principle Component Analysis (PCA) to identify the most important characteristics from 45 metrics to characterize big data workloads from BigDataBench, a comprehensive big data benchmark suite. Second, we apply a clustering technique to the principle components obtained from the PCA to investigate the similarity among big data workloads, and we verify the importance of including different software stacks for big data benchmarking. Third, we select seven representative big data workloads by removing redundant ones and release the BigDataBench simulation version, which is publicly available from http://prof.ict.ac.cn/BigDataBench/simulatorversion/.Comment: 11 pages, 6 figures, 2014 IEEE International Symposium on Workload Characterizatio