Leveraging the Big Data Produced by the Network to Take Intelligent Decisions on Flow Management | Zendy

Shafaan Khaliq Bhatti | Zendy; M. Ikram Ullah Lali | Zendy; Basit Shahzad | Zendy; Farooq Javid | Zendy; Fakhar Ullah Mangla | Zendy; Muhammad Ramzan | Zendy

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Leveraging the Big Data Produced by the Network to Take Intelligent Decisions on Flow Management

Author(s) -

Shafaan Khaliq Bhatti,

M. Ikram Ullah Lali,

Basit Shahzad,

Farooq Javid,

Fakhar Ullah Mangla,

Muhammad Ramzan

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2808358

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Software-defined network (SDN) offers a very advantageous feature of programming the network at run time by decoupling the control plane from data plane to have centralized and better forwarding decisions. To attain the maximum throughput and relatively less latency within a large network, we have captured the real-time big data produced by the network. Big data is revolutionizing the modern computer science world to analyze the extremely large datasets to predict the future requirements. Therefore, it would be useful to embed intelligence in the systems. With the power of SDN, we programmed our network at run time using Ryu controller to dump the network traffic and engineer it accordingly. We proposed a methodology for taking intelligent decisions with traffic engineering in SDN. Results have shown that SDN leverages the big data to decrease the latency time by adding programs in the controller of SDN. An algorithm is presented to maximize the bandwidth utilization to possess higher throughput after shaping the traffic. It is found through the results that our proposed methodology of applying intelligence on traffic management in SDN outperforms those without intelligence decision making.

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