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Capacity Planner

 

Capacity Planner


Problem and Motivation


Mobile data traffic grew 82 percent between Q1 2018 and Q1 2019. Traffic growth is being driven by both the rising number of smartphone subscriptions and an increasing average data volume per subscription, fueled primarily by more viewing of video content. The graph shows total global monthly data and voice traffic from Q3 2013 to Q1 2019, along with the year-on-year percentage change for mobile data traffic.


                 In Q1 2019, mobile data traffic grew 82 percent year-on-year. The high growth rate was mainly influenced by the increased number of smartphone subscriptions in India and increased data traffic per smartphone per month in China. The quarter-on-quarter growth for Q1 was 9 percent.

 

 Network Capacity Planning

 

Mobile Operators are in a constant balancing act of utilizing a fixed capital budget to proactively maintain network and user performance against growing network traffic.

 

 Typical Challenges

 

  • Optimal use of capital budget (what, where, and when to spend?)

  • Proactive planning keeping pace with growth

  • Traffic forecasting and predicting future bottlenecks/performance impacts

  • Technology to deployment (3G , 4G , 5G)

  • What is the timing & priority of capacity augmentation?

 

Capacity Planner – Module

 


Protocol for Capacity Planner

 

Capacity Planner will use 2 protocol to collect data from mobile network and process in server with AI , machine learning

 

1.Protocol which use for collect data usage information from mobile network “ DUC : Data Usage Collection” DUC will have DUC send ( to request data from mobile network ) and DUC receive ( to receive data usage information from mobile network )

 

2.Protocol which use to transfer information from Data collection server to AI , Machine learning server “ IDE : Internal Data Exchange “

 

Capacity Planner – Interface

 

Choose input information , forecast method , new sites map location ( prediction )

 

Reference : www.ericsson.com


 


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นาย ชาญวุฒิ อัศวศิริศิลป์ รหัสนักศึกษา 6217680020


หลักสูตรวิทยาศาสตรมหาบัณฑิต สาขาวิชาเทคโนโลยีสารสนเทศ เเขนงการวิเคราะห์ข้อมูลขนาดใหญ่ (Big Data Analytics)


 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Thursday, 20 February 2020