Modeling downtime severity of telecommunication networks using discrete time Markov Chains

doi:10.19101/ijatee.2022.10100435

IJATEE

2023

DOI: 10.19101/ijatee.2022.10100435

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Modeling downtime severity of telecommunication networks using discrete time Markov Chains

Abstract: Planned and unplanned outages are considered part of the outage measurement unless the system is allocated a maintenance window during which the system is not required to be in service (even though this is an uncommon phenomenon in regular telecom operations) [7].

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Modeling the Causes of Power-Related Network Outages Using Discrete-Time Markov Chains

Gedel,

Iddrisu

2023

Wireless Communications and Mobile Computing

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In this paper, we model the causes of power-related network outages in Ghana using discrete-time Markov chains. We used data consisting of 2,756 small-scale carrier telecommunications outages occurring in Ghana, with accompanying root causes over a period of 5 years and 8 months, from August 2015 to April 2021. The results indicate that the majority (n = 1,404) of the network outages were caused by the generators while the least number (18) of outages were caused by a communication equipment. However, longer network outages were caused by fuel issues with an average outage time of 1,027.82 min over the study period. The transition probability matrix obtained from the data revealed that regardless of the present cause of the network outage, the probability that the next network outage will be caused by the generators is higher than the probability that the outage will be attributable to any other cause. The steady-state distribution indicates that in the long run (n ≥ 16), 51% of the network outages will be caused by the “Generators” while 10.8% of the network outages will be caused by the “Batteries.” We also checked and simulated the probabilities of a network outage caused by any of the 12 possible root causes for 12 steps. It seemed apparent from the simulations that generators are the most likely cause of network outages from Step 1 up to Step 7, irrespective of what the initial cause of the network outage is. With these findings, players in the telecommunications industry can clearly plan better to reduce future network outages.

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Modeling the Causes of Power-Related Network Outages Using Discrete-Time Markov Chains

Gedel,

Iddrisu

2023

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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Modeling downtime severity of telecommunication networks using discrete time Markov Chains

Abstract: Planned and unplanned outages are considered part of the outage measurement unless the system is allocated a maintenance window during which the system is not required to be in service (even though this is an uncommon phenomenon in regular telecom operations) [7].

Cited by 1 publication

References 38 publications

Modeling the Causes of Power-Related Network Outages Using Discrete-Time Markov Chains

Modeling the Causes of Power-Related Network Outages Using Discrete-Time Markov Chains

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