• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.134-140
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• 2023

The distributed system is playing a vital role in storing and processing big data and data generation is speedily increasing from various sources every second. Hadoop has a scalable, and efficient distributed system supporting commodity hardware by combining different networks in the topographical locality. Node support in the Hadoop cluster is rapidly increasing in different versions which are facing difficulty to manage clusters. Hadoop does not provide Node management, adding and deletion node futures. Node identification in a cluster completely depends on DHCP servers which managing IP addresses, hostname based on the physical address (MAC) address of each Node. There is a scope to the hacker to theft the data using IP or Hostname and creating a disturbance in a distributed system by adding a malicious node, assigning duplicate IP. This paper proposing novel node management for the distributed system using DNA hiding and generating a unique key using a unique physical address (MAC) of each node and hostname. The proposed mechanism is providing better node management for the Hadoop cluster providing adding and deletion node mechanism by using limited computations and providing better node security from hackers. The main target of this paper is to propose an algorithm to implement Node information hiding in DNA sequences to increase and provide security to the node from hackers.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.8-15
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• 2012

In this paper, we propose a new monitor for collecting job information from Hadoop clusters in real time. This monitor is made of two programs called Collector and Agent. Agent collects Hadoop cluster's node information and job information, and Collector analyzes the collected information and saves it in a database. Also, Collector was placed in a new node outside the Hadoop cluster so that it does not affect Hadoop's work and will not cause overload. When the proposed monitor was implemented and applied, the testbed cluster was able to detect the occurrence of dead nodes immediately. In addition, we were able to find Hadoop jobs which were inefficient and when we modified such jobs to further enhance the performance of Hadoop.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.2
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• pp.367-374
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• 2017

This paper discusses management problems of Hadoop distributed node, which is a platform for big data processing, and proposes a novel technique for enabling flexible node management of Hadoop Distributed File System. Hadoop cannot configure Hadoop cluster dynamically because it judges temporarily unavailable nodes as a failure. Delayed block replication scheme proposed in this paper delays the removal of unavailable node as much as possible so as to be easily rejoined. Experimental results show that the proposed scheme increases flexibility of node management with little impact on distributed processing performance when the cluster size changes.

• Smart Media Journal
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• v.6 no.3
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• pp.68-74
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• 2017

Big data processing processes various types of data such as files, images, and video to solve problems and provide insightful useful information. Currently, various platforms are used for big data processing, but many organizations and enterprises are using Hadoop for big data processing due to the simplicity, productivity, scalability, and fault tolerance of Hadoop. In addition, Hadoop can build clusters on various hardware platforms and handle big data by dividing into a name node (master) and a data node (slave). In this paper, we use a fully distributed mode used by actual institutions and companies as an operation mode. We have constructed a Hadoop cluster using a low-power and low-cost single board for smooth experiment. The performance analysis of Name node is compared through the same data processing using single board and laptop as name nodes. Analysis of influence by number of data nodes increases the number of data nodes by two times from the number of existing clusters. The effect of the above experiment was analyzed.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1035-1043
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• 2021

An Internet of Things (IOT) sensor network is an effective solution for monitoring environmental conditions. However, IOT sensor networks generate massive data such that the abilities of massive data storage, processing, and query become technical challenges. To solve the problem, a Hadoop cloud platform is proposed. Using the time and workload genetic algorithm (TWLGA), the data processing platform enables the work of one node to be shared with other nodes, which not only raises efficiency of one single node but also provides the compatibility support to reduce the possible risk of software and hardware. In this experiment, a Hadoop cluster platform with TWLGA scheduling algorithm is developed, and the performance of the platform is tested. The results show that the Hadoop cloud platform is suitable for big data processing requirements of IOT sensor networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5252-5268
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• 2018

To support a large-scale Hadoop cluster, Hadoop heartbeat messages are designed to deliver the significant messages, including task scheduling and completion messages, via piggybacking to reduce the number of messages received by the NameNode. Although Hadoop is designed and optimized for high-throughput computing via batch processing, the real-time processing of large amounts of data in Hadoop is increasingly important. This paper evaluates Hadoop's performance and costs when the heartbeat period is controlled to support latency sensitive applications. Through an empirical study based on Hadoop 2.0 (YARN) architecture, we improve Hadoop's I/O performance as well as application performance by up to 13 percent compared to the default configuration. We offer a guideline that predicts the performance, costs and limitations of the total system by controlling the heartbeat period using simple equations. We show that Hive performance can be improved by tuning Hadoop's heartbeat periods through extensive experiments.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.340-349
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• 2017

This paper is proposed Hadoop security protocol to protect a reply attack and impersonation attack. The proposed hadoop security protocol is consists of user authentication module, public key based data node authentication module, name node authentication module, and data node authentication module. The user authentication module is issued the temporary access ID from TGS after verifing user's identification on Authentication Server. The public key based data node authentication module generates secret key between name node and data node, and generates OTKL(One-Time Key List) using Hash-chain. The name node authentication module verifies user's identification using user's temporary access ID, and issues DT(Delegation Token) and BAT(Block Access Token) to user. The data node authentication module sends the encrypted data block to user after verifing user's identification using OwerID of BAT. Therefore the proposed hadoop security protocol dose not only prepare the exposure of data node's secret key by using OTKL, timestamp, owerID but also detect the reply attack and impersonation attack. Also, it enhances the data access of data node, and enforces data security by sending the encrypted data.

• Journal of Information Technology and Architecture
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• v.11 no.4
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• pp.449-462
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• 2014

In big data era, there are a number of considerable parts in processing systems for capturing, storing, and analyzing stored or streaming data. Unlike traditional data handling systems, a big data processing system needs to concern the characteristics (format, velocity, and volume) of being handled data in the system. In this situation, virtualized computing platform is an emerging platform for handling big data effectively, since virtualization technology enables to manage computing resources dynamically and elastically with minimum efforts. In this paper, we analyze resource utilization of virtualized computing resources to discover suitable deployment models in Apache Hadoop and HBase-based big data processing environment. Consequently, Task Tracker service shows high CPU utilization and high Disk I/O overhead during MapReduce phases. Moreover, HRegion service indicates high network resource consumption for transfer the traffic data from DataNode to Task Tracker. DataNode shows high memory resource utilization and Disk I/O overhead for reading stored data.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.361-366
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• 2023

Apache Hadoop, a representative framework for distributed processing of big data, has the advantage of increasing cluster size up to thousands of nodes to improve parallel distributed processing performance. However, reducing the size of the cluster is limited to the extent of permanently decommissioning nodes with defects or degraded performance, so there are limitations to operate multiple nodes flexibly in small clusters. In this paper, we discuss the problems that occur when removing nodes from the Hadoop cluster and propose a dynamic down-scaling technique to manage the distributed cluster more flexibly. To do this, we design and implement a modified Hadoop system and interfaces to support dynamic down-scaling of the cluster which supports temporary pause of a node and reconnection of it when necessary, rather than decommissioning the node when removing a node from the Hadoop cluster. We have verified that effective downsizing can be performed without performance degradation based on experimental results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.915-916
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• 2014

With the upcoming era of internet of things, the study of sensor network has been paid attention. Raspberry pi is a tiny versatile computer system which is able to act as a sensor node in hadoop cluster network. In this paper, we deployed 5 Raspberry pi's to construct an experimental testbed of hadoop sensor network with 5-node map-reduce hadoop software framework. We compared and analyzed the network architecture in terms of efficiency, resource management, and throughput using various parameters. We used a learning machine with support vector machine as test workload. In our experiments, Raspberry pi fulfilled the role of distributed computing sensor node in the sensor network.