A Review on Big Data Movement with Various Approaches
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Abstract
With the growth of technologies and applications, a large amount of data has been produced at an increasing rate from various resources such as social media networks, sensor devices, and other information serving devices. This large collection of massive, complex, and exponential growth of dataset is called big data. As we know very well, almost every business relies heavily on big data to drive decision making and improve operations. The traditional database systems cannot store and process such data due to large and complexity. Therefore, many enterprises have already adopted cloud computing for better storage and processing since it can provide a pool of resources for servers and storage. However, it is significant issue for moving large amounts of data to and from the cloud can indeed present several challenges including bandwidth limitation, latency, and network congestion. To optimize the performance of big data transfer to cloud computing, this paper extensively reviews the previous research works, discusses research issues, summarizes key findings and approaches for dealing with big data movement. From the literature, researchers proposed various network techniques which can be applicable for future use that can help improve the transfer of big data to and from the cloud and ensure for efficient storage and processing of large datasets.
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References
Abdullah, A. O. (2008). Towards a scalable Scientific Data Grid model and services. In International Conference on Computer and Communication Engineering.
Antony, T., & Paul, S. (2014, February). Addressing big data with Hadoop. International Journal of Computer Science and Mobile Computing, 3(2), 459-462.
Big Data. (n.d.). Retrieved from Big Data: http://en.wikipedia.org/wiki/Big_data
Brown, P., Zhu, M., Wu, Q., Yun, D., & Zurawski, J. (2012). Exploring the optimal strategy for large-scale data movement in high-performance networks. In Performance Computing and Communications Conference (IPCCC), (pp. 181-182).
Chard, K. C., Caton, S.J., Rana, O.F., & Katz, D.S. (2012). A social content delivery network for scientific cooperation: Vision, design, and architecture. In Conference: Third International Workshop on Data Intensive Computing in the Clouds (DataCloud 2012) (in conjunction with SC12). DOI:10.1109/SC.Companion.2012.128
Data Analysis Challenge. (2008). The MITRE Corporation.
Fall, K. (2003). A delay-tolerant network architecture for challenged Internets. In Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications (SIGCOMM '03), (pp. 27-34). https://doi.org/10.1145/863955.863960
Garrett, B. (2013). Big data is changing your world...more than you know. Atlantic Council. Retrieved from https://econvue.com/sites/default/files/Big_Data_is_Changing_Your_World.pdf
Gorcitz, R. A., Jarma, Y., Spathis, P., de Amorim, M. D., Wakikawa, R., Whitbeck, J., Conan, V., & Fdida, S. (2012). Vehicular carriers for big data transfers. In Vehicular Networking Conference (VNC). Retrieved from https://hal.science/hal-00739361/document
Kanagavelu, R. L. (2013). Software defined network based adaptive routing for data replication in Data Centers. In 19th IEEE International Conference on Networks (ICON).
Laoutaris, N. S. (2009). Delay tolerant bulk data transfers on the internet. In ACM SIGMETRICS Performance Evaluation Review, 37(1).
Ochiai, H. I., Ishizuka, H., Kawakami, Y., & Esaki, H. (2011). A DTN-based sensor data gathering for agricultural applications. IEEE Sensors Journal, 11(11), 2861-2868.
Rawat, R., & Yadav, R. (2021). Big data: Big data analysis, issues and challenges and technologies. In IOP Conference Series: Materials Science and Engineering, 1022(1), 012014. DOI:10.1088/1757-899X/1022/1/012014
Sim, A. (2010). Bulk data movement for climate dataset: Efficient data transfer management with dynamic transfer adjustment. Lawrence Berkeley National Laboratory.
Soi, S. (2013, October). Big data and methodology - A review. International Journal of Advanced Research in Computer Science and Software Engineering, 3(10), 991-995.
Song, Y.-S. (2012, December). Storing big data-the rise of the storage cloud. Retrieved from www.seamicro.com
Upadhyay, S., Manwani, R., Varshney, S., & Jain, S. (2021). Analytics and storage of big data. In Presented at the International Semantic Intelligence Conference (ISIC 2021).