KSII Transactions on Internet and Information Systems (TIIS)

  • 제11권10호
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  • Pages.4717-4737
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  • 2017
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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Big Data Based Dynamic Flow Aggregation over 5G Network Slicing

  • Sun, Guolin (School of Computer Science and Engineering, University of Electronic Science and Technology of China) ;
  • Mareri, Bruce (School of Computer Science and Engineering, University of Electronic Science and Technology of China) ;
  • Liu, Guisong (School of Computer Science and Engineering, University of Electronic Science and Technology of China) ;
  • Fang, Xiufen (School of Mathematical Sciences, University of Electronic Science and Technology of China) ;
  • Jiang, Wei (German Research Center for Artificial Intelligence (DFKI GmbH))
  • 투고 : 2017.02.16
  • 심사 : 2017.06.01
  • 발행 : 2017.10.31
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초록

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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참고문헌

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