Dynamic Estimation Algorithm for Markovian Model for Packet Loss

In this Paper, we worked on the modeling of packet loss in EUMEDConnect Network (network connects 6 Arab countries) from the Palestinian side. This research exploited a data set of 72 hours. each country was expressed by a randomly selected 12-hour dataset, each dataset was divided into two-hour segments, each segment was modeled as a binary time series. From the 36 segments, 26 segments were found stationary. For Stationary segments, the research investigated the segment correlation and used it as a modeling reference. 11 segments were modeled using Bernoulli model, 12 segments were modeled using 2-state Markov chain and 5 segments showed k-th order Markov chain tendencies with orders 2, 3, 8, 27, 38. The models were built under 0.05 threshold in average filter condition of stationary and with confidence of 95% for lag dependency selection. After modeling each segment independently, an average loss model for each country was calculated using its modeled segments. confidence of these models were 95%. Afterward, we suggested a cumulative modeling algorithm through making a higher segmentation on shorter intervals less than 2 hours and give expectation for the models value for a given segment dynamically and cumulatively, this was achieved with error less than 0.001 for single segment.