scholarly journals Cloud Forensics : Isolating Cloud Instance

2017 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Mariam J. AlKandari ◽  
Huda F. Al Rasheedi ◽  
Ayed A. Salman
Keyword(s):  
Cloud Computing ◽  
Digital Forensics ◽  
File System ◽  
File Systems ◽  
Distributed File System ◽  
Cloud Environment ◽  
Apache Hadoop ◽  
Cloud Forensics ◽  
Hadoop Distributed File System ◽  
Do So

Abstract—Cloud computing has been the trending model for storing, accessing and modifying the data over the Internet in the recent years. Rising use of the cloud has generated a new concept related to the cloud which is cloud forensics. Cloud forensics can be defined as investigating for evidence over the cloud, so it can be viewed as a combination of both cloud computing and digital forensics. Many issues of applying forensics in the cloud have been addressed. Isolating the location of the incident has become an essential part of forensic process. This is done to ensure that evidence will not be modified or changed.  Isolating an instant in the cloud computing has become even more challenging, due to the nature of the cloud environment. In the cloud, the same storage or virtual machine have been used by many users. Hence, the evidence is most likely will be overwritten and lost. The proposed solution in this paper is to isolate a cloud instance. This can be achieved by marking the instant that reside in the servers as "Under Investigation". To do so, cloud file system must be studied. One of the well-known file systems used in the cloud is Apache Hadoop Distributed File System (HDFS). Thus, in this paper the methodology used for isolating a cloud instance would be based on the HDFS architecture. Keywords: cloud computing; digital forensics; cloud forensics

scholarly journals Política Customizada de Balanceamento de Réplicas para o HDFS Balancer do Apache Hadoop

2019 ◽  
Author(s):  
Rhauani W. Fazul ◽  
Patricia Pitthan Barcelos
Keyword(s):  
File System ◽  
Data Replication ◽  
Distributed File System ◽  
Apache Hadoop ◽  
Hadoop Distributed File System ◽  
Fundamental Mechanism ◽  
The Way

Data replication is a fundamental mechanism of the Hadoop Distributed File System (HDFS). However, the way data is spread across the cluster directly affects the replication balancing. The HDFS Balancer is a Hadoop integrated tool which can balance the storage load on each machine by moving data between nodes, although its operation does not address the specific needs of applications while performing block rearrangement. This paper proposes a customized balancing policy for HDFS Balancer based on a system of priorities, which can be adapted and configured according to usage demands. The priorities define whether HDFS parameters, or whether cluster topology should be considered during the operation, thus making the balancing more flexible.

Hadoop Setup

2018 ◽  
pp. 45-62
Keyword(s):  
Distributed Computing ◽  
File Systems ◽  
Distributed File System ◽  
Map Reduce ◽  
Distributed File Systems ◽  
Single Node ◽  
Apache Hadoop ◽  
Commodity Hardware ◽  
Open Source Framework ◽  
Hadoop Distributed File System

Apache Hadoop is an open source framework for storage and processing massive amounts of data. The skeleton of Hadoop can be viewed as distributed computing across a cluster of computers. This chapter deals with the single node, multinode setup of Hadoop environment along with the Hadoop user commands and administration commands. Hadoop processes the data on a cluster of machines with commodity hardware. It has two components, Hadoop Distributed File System for storage and Map Reduce/YARN for processing. Single node processing can be done through standalone or pseudo-distributed mode whereas multinode is through cluster mode. The execution procedure for each environment is briefly stated. Then the chapter explores the Hadoop user commands for operations like copying to and from files in distributed file systems, running jar, creating archive, setting version, classpath, etc. Further, Hadoop administration manages the configuration including functions like cluster balance, running the dfs, MapReduce admin, namenode, secondary namenode, etc.

scholarly journals A Comprehensive Survey for Hadoop Distributed File System

2021 ◽  
pp. 46-57
Author(s):  
Karwan Jameel Merceedi ◽  
Nareen Abdulla Sabry
Keyword(s):  
Distributed Systems ◽  
Data Storage ◽  
File System ◽  
Low Cost ◽  
File Systems ◽  
Cost Effective ◽  
Distributed File System ◽  
Software Frameworks ◽  
Hadoop Distributed File System ◽  
Basic Ideas

In the last few days, data and the internet have become increasingly growing, occurring in big data. For these problems, there are many software frameworks used to increase the performance of the distributed system. This software is used for available ample data storage. One of the most beneficial software frameworks used to utilize data in distributed systems is Hadoop. This software creates machine clustering and formatting the work between them. Hadoop consists of two major components: Hadoop Distributed File System (HDFS) and Map Reduce (MR). By Hadoop, we can process, count, and distribute each word in a large file and know the number of affecting for each of them. The HDFS is designed to effectively store and transmit colossal data sets to high-bandwidth user applications. The differences between this and other file systems provided are relevant. HDFS is intended for low-cost hardware and is exceptionally tolerant to defects. Thousands of computers in a vast cluster both have directly associated storage functions and user programmers. The resource scales with demand while being cost-effective in all sizes by distributing storage and calculation through numerous servers. Depending on the above characteristics of the HDFS, many researchers worked in this field trying to enhance the performance and efficiency of the addressed file system to be one of the most active cloud systems. This paper offers an adequate study to review the essential investigations as a trend beneficial for researchers wishing to operate in such a system. The basic ideas and features of the investigated experiments were taken into account to have a robust comparison, which simplifies the selection for future researchers in this subject. According to many authors, this paper will explain what Hadoop is and its architectures, how it works, and its performance analysis in a distributed systems. In addition, assessing each Writing and compare with each other.

Using Hadoop Distributed and Deduplicated File System (HD2FS) in Astronomy

2019 ◽  
Vol 15 (S367) ◽  
pp. 464-466
Author(s):  
Paul Bartus
Keyword(s):  
Data Storage ◽  
Storage Capacity ◽  
File System ◽  
Storage Systems ◽  
Storage System ◽  
File Systems ◽  
Distributed File System ◽  
Distributed File Systems ◽  
Output Performance ◽  
Hadoop Distributed File System

AbstractDuring the last years, the amount of data has skyrocketed. As a consequence, the data has become more expensive to store than to generate. The storage needs for astronomical data are also following this trend. Storage systems in Astronomy contain redundant copies of data such as identical files or within sub-file regions. We propose the use of the Hadoop Distributed and Deduplicated File System (HD2FS) in Astronomy. HD2FS is a deduplication storage system that was created to improve data storage capacity and efficiency in distributed file systems without compromising Input/Output performance. HD2FS can be developed by modifying existing storage system environments such as the Hadoop Distributed File System. By taking advantage of deduplication technology, we can better manage the underlying redundancy of data in astronomy and reduce the space needed to store these files in the file systems, thus allowing for more capacity per volume.

scholarly journals File Systems and Hadoop Distributed File System in Big Data

IJARCCE ◽  
2016 ◽  
Vol 5 (12) ◽  
pp. 36-40 ◽  
Author(s):  
G Fayaz Hussain ◽  
Tarakeswar T
Keyword(s):  
Big Data ◽  
File System ◽  
File Systems ◽  
Distributed File System ◽  
Hadoop Distributed File System

Improving downloading performance in hadoop distributed file system

2010 ◽  
Vol 30 (8) ◽  
pp. 2060-2065 ◽  
Author(s):  
Ning CAO ◽  
Zhong-hai WU ◽  
Hong-zhi LIU ◽  
Qi-xun ZHANG
Keyword(s):  
File System ◽  
Distributed File System ◽  
Hadoop Distributed File System

scholarly journals Data protection on hadoop distributed file system by using encryption algorithms: a systematic literature review

2020 ◽  
Vol 1444 ◽  
pp. 012012
Author(s):  
Meisuchi Naisuty ◽  
Achmad Nizar Hidayanto ◽  
Nabila Clydea Harahap ◽  
Ahmad Rosyiq ◽  
Agus Suhanto ◽  
...  
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Data Protection ◽  
File System ◽  
Distributed File System ◽  
Hadoop Distributed File System ◽  
Encryption Algorithms
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