NDLI: Performance analysis of data intensive cloud systems based on data management and replication: a survey

Content Provider	Springer Nature Link
Author	Malik, Saif Ur Rehman Khan, Samee U. Ewen, Sam J. Tziritas, Nikos Kolodziej, Joanna Zomaya, Albert Y. Madani, Sajjad A. Min Allah, Nasro Wang, Lizhe Xu, Cheng Zhong Malluhi, Qutaibah Marwan Pecero, Johnatan E. Balaji, Pavan Vishnu, Abhinav Ranjan, Rajiv Zeadally, Sherali Li, Hongxiang
Copyright Year	2015
Abstract	As we delve deeper into the ‘Digital Age’, we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis that originated from myriad of sources and applications including mobile devices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, etc. Such ‘Data Explosions’ has led to one of the most challenging research issues of the current Information and Communication Technology era: how to optimally manage (e.g., store, replicated, filter, and the like) such large amount of data and identify new ways to analyze large amounts of data for unlocking information. It is clear that such large data streams cannot be managed by setting up on-premises enterprise database systems as it leads to a large up-front cost in buying and administering the hardware and software systems. Therefore, next generation data management systems must be deployed on cloud. The cloud computing paradigm provides scalable and elastic resources, such as data and services accessible over the Internet Every Cloud Service Provider must assure that data is efficiently processed and distributed in a way that does not compromise end-users’ Quality of Service (QoS) in terms of data availability, data search delay, data analysis delay, and the like. In the aforementioned perspective, data replication is used in the cloud for improving the performance (e.g., read and write delay) of applications that access data. Through replication a data intensive application or system can achieve high availability, better fault tolerance, and data recovery. In this paper, we survey data management and replication approaches (from 2007 to 2011) that are developed by both industrial and research communities. The focus of the survey is to discuss and characterize the existing approaches of data replication and management that tackle the resource usage and QoS provisioning with different levels of efficiencies. Moreover, the breakdown of both influential expressions (data replication and management) to provide different QoS attributes is deliberated. Furthermore, the performance advantages and disadvantages of data replication and management approaches in the cloud computing environments are analyzed. Open issues and future challenges related to data consistency, scalability, load balancing, processing and placement are also reported.
Starting Page	179
Ending Page	215
Page Count	37
File Format	PDF
ISSN	09268782
Journal	Distributed and Parallel Databases
Volume Number	34
Issue Number	2
e-ISSN	15737578
Language	English
Publisher	Springer US
Publisher Date	2015-03-14
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Replication Data management Cloud computing systems Performance gradation Data intensive computing Database Management Data Structures Information Systems Applications (incl. Internet) Operating Systems Memory Structures
Content Type	Text
Resource Type	Article
Subject	Information Systems and Management Information Systems Software Hardware and Architecture

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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