A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data
Abstract The Management of mobile networks has become so complex due to a huge number of devices, technologies and services involved. Network optimization and incidents management in mobile networks determine the level of the quality of service provided by the communication service providers (CSPs)....
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2019-02-01
|
| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40537-019-0173-8 |
| _version_ | 1818041454742732800 |
|---|---|
| author | Maluambanzila Minerve Mampaka Mbuyu Sumbwanyambe |
| author_facet | Maluambanzila Minerve Mampaka Mbuyu Sumbwanyambe |
| author_sort | Maluambanzila Minerve Mampaka |
| collection | DOAJ |
| description | Abstract The Management of mobile networks has become so complex due to a huge number of devices, technologies and services involved. Network optimization and incidents management in mobile networks determine the level of the quality of service provided by the communication service providers (CSPs). Generally, the down time of a system and the time taken to repair [mean time to repair (MTTR)] has a direct impact on the revenue, especially on the operational expenditure (OPEX). A fast root cause analysis (RCA) mechanism is therefore crucial to improve the efficiency of the operational team within the CSPs. This paper proposes a quadri-dimensional approach (i.e. services, subscribers, handsets and cells) to build a service quality management (SQM) tree in a Big Data platform. This is meant to speed up the root cause analysis and prioritize the elements impacting the performance of the network. Two algorithms have been proposed; the first one, to normalize the performance indicators and the second one to build the SQM tree by aggregating the performance indicators for different dimensions to allow ranking and detection of tree paths with the worst performance. Additionally, the proposed approach will allow CSPs to detect the mobile network dimensions causing network issues in a faster way and protect their revenue while improving the quality of the service delivered. |
| first_indexed | 2024-12-10T08:30:41Z |
| format | Article |
| id | doaj.art-dbc387d369be4ae3933f7a29e65e1385 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-10T08:30:41Z |
| publishDate | 2019-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-dbc387d369be4ae3933f7a29e65e13852022-12-22T01:56:07ZengSpringerOpenJournal of Big Data2196-11152019-02-016111510.1186/s40537-019-0173-8A quadri-dimensional approach for poor performance prioritization in mobile networks using Big DataMaluambanzila Minerve Mampaka0Mbuyu Sumbwanyambe1Department of Electrical and Mining Engineering, University of South AfricaDepartment of Electrical and Mining Engineering, University of South AfricaAbstract The Management of mobile networks has become so complex due to a huge number of devices, technologies and services involved. Network optimization and incidents management in mobile networks determine the level of the quality of service provided by the communication service providers (CSPs). Generally, the down time of a system and the time taken to repair [mean time to repair (MTTR)] has a direct impact on the revenue, especially on the operational expenditure (OPEX). A fast root cause analysis (RCA) mechanism is therefore crucial to improve the efficiency of the operational team within the CSPs. This paper proposes a quadri-dimensional approach (i.e. services, subscribers, handsets and cells) to build a service quality management (SQM) tree in a Big Data platform. This is meant to speed up the root cause analysis and prioritize the elements impacting the performance of the network. Two algorithms have been proposed; the first one, to normalize the performance indicators and the second one to build the SQM tree by aggregating the performance indicators for different dimensions to allow ranking and detection of tree paths with the worst performance. Additionally, the proposed approach will allow CSPs to detect the mobile network dimensions causing network issues in a faster way and protect their revenue while improving the quality of the service delivered.http://link.springer.com/article/10.1186/s40537-019-0173-8Big DataQoSQoEMTTRRoot cause analysisSQM |
| spellingShingle | Maluambanzila Minerve Mampaka Mbuyu Sumbwanyambe A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data Journal of Big Data Big Data QoS QoE MTTR Root cause analysis SQM |
| title | A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data |
| title_full | A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data |
| title_fullStr | A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data |
| title_full_unstemmed | A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data |
| title_short | A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data |
| title_sort | quadri dimensional approach for poor performance prioritization in mobile networks using big data |
| topic | Big Data QoS QoE MTTR Root cause analysis SQM |
| url | http://link.springer.com/article/10.1186/s40537-019-0173-8 |
| work_keys_str_mv | AT maluambanzilaminervemampaka aquadridimensionalapproachforpoorperformanceprioritizationinmobilenetworksusingbigdata AT mbuyusumbwanyambe aquadridimensionalapproachforpoorperformanceprioritizationinmobilenetworksusingbigdata AT maluambanzilaminervemampaka quadridimensionalapproachforpoorperformanceprioritizationinmobilenetworksusingbigdata AT mbuyusumbwanyambe quadridimensionalapproachforpoorperformanceprioritizationinmobilenetworksusingbigdata |