InSTechAH: Cost-effectively autoscaling smart computing hadoop cluster in private cloud

2017 ◽  
Vol 80 ◽  
pp. 1-16 ◽  
Author(s):  
Zhihui Lu ◽  
Xueying Wang ◽  
Jie Wu ◽  
Patrick C.K. Hung
Keyword(s):  
Private Cloud ◽  
Hadoop Cluster ◽  
Smart Computing

scholarly journals Basic Prototype Design and Verification of Hadoop Cluster based on Private Cloud Infrastructure for SMB

2013 ◽  
Vol 17 (2) ◽  
pp. 225-233 ◽  
Author(s):  
Byung-Rae Cha ◽  
Hyeong-Gyun Kim ◽  
Dae-Gue Kim ◽  
Jong-Won Kim ◽  
Yong-Il Kim
Keyword(s):  
Cloud Infrastructure ◽  
Private Cloud ◽  
Hadoop Cluster ◽  
Prototype Design

RANCANG BANGUN CLOUD COMPUTING DI LABORATORIUM KOMPUTER TEKNIK ELEKTRO UNIVERSITAS BANGKA BELITUNG

2014 ◽  
Vol 1 (1) ◽  
pp. 14-22
Author(s):  
Ghiri Basuki Putra
Keyword(s):  
Cloud Computing ◽  
Software As A Service ◽  
Private Cloud ◽  
Private Cloud Computing

Cloud computing telah menjadi hal yang menarik untuk dibahas dikarenakan perkembangannya yang begitu pesat sejak pertama kali diperkenalkan mulai tahun 2000. Pemanfaatan cloud computing kepada penyimpanan data, pemakaian software secara bersama- sama serta penggunaan infrastruktur dan hardware pada jaringan atau komputer yang tergabung dalam sebuah cloud computing. Dengan cloud computing diharapkan adanya efesiensi dan kemudahan dalam  sumber daya baik software, data maupun hardware agar dapat digunakan bersama – sama. Perancangan cloud computing untuk laboratorium komputer Teknik Elektro Universitas Bangka Belitung bertujuan sebagai rancangan awal untuk pengembangan laboratorium komputer serta sebagai pusat pembelajaran dan penelitian cloud computing bagi mahasiswa Teknik Elektro. Perancangan cloud computing ini menggunakan metode Software as a Service (SaaS) dimana SaaS adalah layanan dari Cloud Computing dimana memakai software (perangkat lunak) yang telah disediakan sehingga tidak perlu setiap komputer di laboratorium menginstall software yang diperlukan selama tersedia di layanan Cloud Computing. Rancangan cloud computing di laboratorium menggunakan Private Cloud Computing merupakan pemodelan Cloud Computing yang memberikan lingkup yang lebih kecil untuk dapat memberikan layanan kepada pengguna tertentu misalnya pada sebuah jaringan komputer  lokal maupun pada skala perusahaan kecil maupun menengah.

PEMANFAATAN PRIVATE CLOUD STORAGE SEBAGAI MEDIA PENYIMPANAN DATA E-LEARNING PADA LEMBAGA PENDIDIKAN

2017 ◽  
Vol 10 (2) ◽  
Author(s):  
Irfan Santiko ◽  
Rahman Rosidi ◽  
Seta Agung Wibawa
Keyword(s):  
Cloud Storage ◽  
Private Cloud ◽  
E Learning

Arcus Cloud A Private Cloud Establishment

2018 ◽  
Vol 6 (3) ◽  
pp. 283-291
Author(s):  
Vishva Patel ◽  
◽  
Dhara Patel ◽  
Sunit Parmar ◽  
◽  
...  
Keyword(s):  
Private Cloud

Big Data Security Challenges and Solution of Distributed Computing in Hadoop Environment: A Security Framework

2020 ◽  
Vol 13 (4) ◽  
pp. 790-797
Author(s):  
Gurjit Singh Bhathal ◽  
Amardeep Singh Dhiman
Keyword(s):  
Big Data ◽  
Data Security ◽  
Data Sets ◽  
Security Framework ◽  
Hadoop Distributed File System ◽  
Current Scenario ◽  
Hadoop Cluster ◽  
Ciphertext Policy ◽  
In Transit ◽  
Hadoop Framework

Background: In current scenario of internet, large amounts of data are generated and processed. Hadoop framework is widely used to store and process big data in a highly distributed manner. It is argued that Hadoop Framework is not mature enough to deal with the current cyberattacks on the data. Objective: The main objective of the proposed work is to provide a complete security approach comprising of authorisation and authentication for the user and the Hadoop cluster nodes and to secure the data at rest as well as in transit. Methods: The proposed algorithm uses Kerberos network authentication protocol for authorisation and authentication and to validate the users and the cluster nodes. The Ciphertext-Policy Attribute- Based Encryption (CP-ABE) is used for data at rest and data in transit. User encrypts the file with their own set of attributes and stores on Hadoop Distributed File System. Only intended users can decrypt that file with matching parameters. Results: The proposed algorithm was implemented with data sets of different sizes. The data was processed with and without encryption. The results show little difference in processing time. The performance was affected in range of 0.8% to 3.1%, which includes impact of other factors also, like system configuration, the number of parallel jobs running and virtual environment. Conclusion: The solutions available for handling the big data security problems faced in Hadoop framework are inefficient or incomplete. A complete security framework is proposed for Hadoop Environment. The solution is experimentally proven to have little effect on the performance of the system for datasets of different sizes.

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