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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mostafijur Rahman
Daffodil International University (DIU)

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A noble approach to develop dynamically scalable namenode in hadoop distributed file system using secondary storage Tumpa Rani Shaha; Md. Nasim Akhtar; Fatema Tuj Johora; Md. Zakir Hossain; Mostafijur Rahman; R. B. Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i2.pp729-736

Abstract

For scalable data storage, Hadoop is widely used nowadays. It provides a distributed file system that stores data on the compute nodes. Basically, it represents a master/slave architecture that consists of a NameNode and copious Data Nodes. Data Nodes contain application data and metadata of application data resides in the Main Memory of NameNode. In cached approach, they fragment the metadata depending on the last access time and move the least frequently used data to secondary memory. If the requested data is not found in main memory then the secondary data will be loaded again on the RAM. So when the secondary data reloads to the primary memory then the NameNode main memory limitation arises again. The focus of this research is to reduce the namespace problem of main memory and to make the system dynamically scalable. A new Metadata Fragmentation Algorithm is proposed that separates the metadata list of NameNode dynamically. The NameNode creates Secondary Memory File in perspective of the threshold value and allocates secondary memory location based on the requirement. According to the proposed algorithm the maximum third, out of fourth of main memory is used at the secondary file caching time. The free space aids in faster operation by Dynamically Scalable NameNode approach. This proposed algorithm shows that the space utilization is increased to 17% and time utilization is increased to 0.0005% with the comparison of the existing fragmentation algorithm.
A dynamic K-means clustering for data mining Md. Zakir Hossain; Md.Nasim Akhtar; R.B. Ahmad; Mostafijur Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i2.pp521-526

Abstract

Data mining is the process of finding structure of data from large data sets. With this process, the decision makers can make a particular decision for further development of the real-world problems. Several data clusteringtechniques are used in data mining for finding a specific pattern of data. The K-means method isone of the familiar clustering techniques for clustering large data sets.  The K-means clustering method partitions the data set based on the assumption that the number of clusters are fixed.The main problem of this method is that if the number of clusters is to be chosen small then there is a higher probability of adding dissimilar items into the same group. On the other hand, if the number of clusters is chosen to be high, then there is a higher chance of adding similar items in the different groups. In this paper, we address this issue by proposing a new K-Means clustering algorithm. The proposed method performs data clustering dynamically. The proposed method initially calculates a threshold value as a centroid of K-Means and based on this value the number of clusters are formed. At each iteration of K-Means, if the Euclidian distance between two points is less than or equal to the threshold value, then these two data points will be in the same group. Otherwise, the proposed method will create a new cluster with the dissimilar data point. The results show that the proposed method outperforms the original K-Means method.
Optimization of video quality via fuzzy logic base adaptive media playout Farij Ehtiba; Badlishah Ahmad; Mostafijur Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i2.pp527-533

Abstract

This paper addresses the issue of interruption of Virable Bit Rate (VBR) video streaming over IP network, due to channel quality fluctuation. Specifically, a Fuzzy Logic (FL) Control principle is fused with Adaptive Media Playout (AMP) control to estimate an appropriate playout frame rate, namely FLAMP. Based on the estimation of the playout frame rate, the FLAMP algorithm adjusts the consumption rate smoothly to avoid video stream degradation. Simulation results validate that the FLAMP scheme efficiently reduces the buffer outage probability and provides better visual quality where FLAMP gives 26.5% less ferquence of playout interruption, and 21.6% less variance of distortion of playout as compared to APTA.
An approach to building energy clusters using particle swarm optimization algorithm for allocating the tasks in computational grid Rashedul Islam; Md Nasim Akhtar; Badlishah R Ahmad; Utpal Kanti Das; Mostafijur Rahman; Zahereel Ishwar Abdul Khalib
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 2: May 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i2.pp826-833

Abstract

The proper mapping in case of allocation of available tasks among particles is a challenging job to accomplish. It requires proper procedural approach and effectual algorithm or strategy. The deterministic polynomial time for task allocation problem is relative. The existence of proper and exact approach for allocation problem is void. However, for the survival of the grid and executing the assigned tasks, the reserved tasks need to be allocated equally among the particles of the grid space. At the same time, the applied model for task allocation must not consume unnecessary time and memory. We applied Particle Swarm Optimization (PSO) for allocating the task. Additionally, the particles will be divided into three clusters based on their energy level. Each cluster will have its own cluster header. Cluster headers will be used to search the task into space. In a single cluster, particles member will be of same energy level status such as full energy, half energy, and no energy level. As a result, the system will use the limited time for searching task for the remaining tasks in it if a particular task requires allocating half task to a particle.
Co-Authors Badlishah Ahmad Badlishah R Ahmad Farij Ehtiba Fatema Tuj Johora Md Nasim Akhtar Md. Nasim Akhtar Md. Zakir Hossain Md. Zakir Hossain Md.Nasim Akhtar R. B. Ahmad R.B. Ahmad Rashedul Islam Tumpa Rani Shaha Utpal Kanti Das Zahereel Ishwar Abdul Khalib