Big Data Based Self-optimization Networking: A Novel Approach Beyond Cognition |
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Authors: | Amin Mohajer Morteza Barari Houman Zarrabi |
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Affiliation: | a Department of Information, Communications & Security, Malek Ashtar University of Technology, Tehran, Iran;b Integrated Network Management Group, Iran Telecommunication Research Center (ITRC), Tehran, Iran |
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Abstract: | It is essential to satisfy class-specific QoS constraints to provide broadband services for new generation
wireless networks. A self-optimization technique is introduced as the only viable solution for controlling
and managing this type of huge data networks. This technique allows control of resources and key
performance indicators without human intervention, based solely on the network intelligence. The
present study proposes a big data based self optimization networking (BD-SON) model for wireless
networks in which the KPI parameters affecting the QoS are assumed to be controlled through a multidimensional decision-making process. Also, Resource Management Center (RMC) was used to allocate
the required resources to each part of the network based on made decision in SON engine, which
can satisfy QoS constraints of a multicast session in which satisfying interference constraints is the
main challenge. A load-balanced gradient power allocation (L-GPA) scheme was also applied for the
QoS-aware multicast model to accommodate the effect of transmission power level based on link
capacity requirements. Experimental results confirm that the proposed power allocation techniques
considerably increase the chances of finding an optimal solution. Also, results confirm that proposed
model achieves significant gain in terms of quality of service and capacity along with low complexity
and load balancing optimality in the network. |
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Keywords: | Wireless multicast Quality of service (QoS) Power control Big Data Load Balanced Gradient power allocation (L-GPA) |
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