Göksu Zekiye ÖZEN | Rayımbek SULTANOV

Article | 2017 | Turkish Journal of Electrical Engineering and Computer Sciences25 ( 2 )

Hadoop framework uses MapReduce programming paradigm to process big data by distributing data across a cluster and aggregating. MapReduce is one of the methods used to process big data hosted on large clusters. In this method, jobs are processed by dividing into small pieces and distributing over nodes. Parameters such as distributing method over nodes, the number of jobs held in a parallel fashion and the number of nodes in the cluster affect the execution time of jobs. The aim of this paper is to determine how number of nodes, maps and reduces affect the performance of Hadoop framework on a cloud environment. For this purpose, tes . . .ts were carried out on a Hadoop cluster with 10 nodes hosted on a cloud environment by running PiEstimator, Grep, Teragen and Terasort benchmarking tools on it. These benchmarking tools available under Hadoop framework are classified as CPU-intensive and CPU-light applications as a result of tests. In CPU-light applications; increasing number of nodes, maps and reduces do not improve efficiency of these applications, even they cause increase of time spent on jobs by using system resources unnecessarily. Therefore, in CPU-light applications, selecting number of nodes, maps and reduces as minimum are found as the optimization of time spent on a process. In CPU-intensive applications, according to the phase that small job pieces are processed, it is found that selecting number of maps or reduces equal to total number of CPUs on a cluster as the optimization of time spent on a process.- Keywords: Big Data, Hadoop, Cloud Computing, MapReduce, KVM Virtualization Environment, Benchmarking Tools, Ganglia. More less

Rayımbek SULTANOV | Rita İSMAİLOVA

Article | 2019 | Turkish Journal of Electrical Engineering and Computer Sciences27 ( 5 )

This paper proposes a minimal length difference algorithm for construction of a line in an image by solving the problem of optimal contour approximation. In this algorithm, a method for finding interest points is proposed, and the object matching (classification) is done by mapping interest points onto a vector space. In cases where the lines in the representation of the images are not smooth, the algorithm converges rapidly. The results of the experiments showed that for convergence of the contour simplification, there were 5-6 iterations for n = 13. To check how close the curve approximation calculated by the algorithm above, the . . .researchers have calculated the length of the curve simplification manually. This length was then compared to the length of the original curve. The results showed that the length of the simplified curve grew rapidly to 92%-95% of the original curve length. The further increase in the number of points does not affect this indicator. According to the obtained results, the relative difference and the relative difference distance are good metrics to match object More less