• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Big Data Based Dynamic Flow Aggregation over 5G Network Slicing

Vol. 11, No. 10, October 30, 2017
10.3837/tiis.2017.10.003, Download Paper (Free):


Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloud-based big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach.


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Cite this article

[IEEE Style]
G. Sun, B. Mareri, G. Liu, X. Fang, W. Jiang, "Big Data Based Dynamic Flow Aggregation over 5G Network Slicing," KSII Transactions on Internet and Information Systems, vol. 11, no. 10, pp. 4717-4737, 2017. DOI: 10.3837/tiis.2017.10.003.

[ACM Style]
Guolin Sun, Bruce Mareri, Guisong Liu, Xiufen Fang, and Wei Jiang. 2017. Big Data Based Dynamic Flow Aggregation over 5G Network Slicing. KSII Transactions on Internet and Information Systems, 11, 10, (2017), 4717-4737. DOI: 10.3837/tiis.2017.10.003.

[BibTeX Style]
@article{tiis:21567, title="Big Data Based Dynamic Flow Aggregation over 5G Network Slicing", author="Guolin Sun and Bruce Mareri and Guisong Liu and Xiufen Fang and Wei Jiang and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2017.10.003}, volume={11}, number={10}, year="2017", month={October}, pages={4717-4737}}