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

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• 作者：Saif Ur Rehman Malik ; Samee U. Khan ; Sam J. Ewen…
• 关键词：Replication ; Data management ; Cloud computing systems ; Performance gradation ; Data intensive computing
• 刊名：Distributed and Parallel Databases
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：34
• 期：2
• 页码：179-215
• 全文大小：924 KB
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• 作者单位：Saif Ur Rehman Malik (1)
  Samee U. Khan (2)
  Sam J. Ewen (2)
  Nikos Tziritas (3)
  Joanna Kolodziej (4)
  Albert Y. Zomaya (5)
  Sajjad A. Madani (1)
  Nasro Min-Allah (6)
  Lizhe Wang (7)
  Cheng-Zhong Xu (8)
  Qutaibah Marwan Malluhi (9)
  Johnatan E. Pecero (10)
  Pavan Balaji (11)
  Abhinav Vishnu (12)
  Rajiv Ranjan (13)
  Sherali Zeadally (14)
  Hongxiang Li (15)
  
  1. COMSATS Institute of Information Technology, Islamabad, Pakistan
  2. North dakota State University, Fargo, USA
  3. Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China
  4. University of Bielsko-Biala, Bielsko-Biala, Poland
  5. University of Sydney, Sydney, Australia
  6. University of Dammam, Dammam, Saudi Arabia
  7. Chinese Academy of Sciences, Beijing, China
  8. Wayne State University, Detroit, MI, USA
  9. Qatar University, Doha, Qatar
  10. University of Luxembourg, Walferdange, Luxembourg
  11. Argonne National Laboratory, Lemont, IL, USA
  12. Pacific Northwest National Laboratory, Richland, USA
  13. CSIRO ICT Center, Marsfield, NSW, Australia
  14. University of the District of Columbia, Washington, DC, 20008, USA
  15. University of Louisville, Louisville, Kentucky
  
• 刊物类别：Computer Science
• 刊物主题：Database Management
  Data Structures
  Information Systems Applications and The Internet
  Operating Systems
  Memory Structures
  
• 出版者：Springer Netherlands
• ISSN：1573-7578

文摘

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.