Improving service quality using Bayesian networks


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Abstract examples explaining how Bayesian network models can help predict service quality

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Improving service quality using Bayesian networks

  1. 1. Improving Service Quality using Bayesian networks Kiran Kaipa
  2. 2. Problem description, trends and challenges Today’s challenge for Communication Service Providers (CSPs) is to deliver high quality service with low operating costs With services not being limited to delivering basic connectivity services, i.e. voice and data, the number of service quality parameters to be measured has also increased making analysis a complex and time consuming task This gets compounded with the fact that service quality parameters have multi-dimensional sources  Network  IT infrastructure  Applications  Subscribers
  3. 3. Service Quality Analysis Bayesian Network Approach Mathematically proven Bayesian network algorithm can be used to analyze service quality where  There is a lot of data (Big data)  As well as, missing data Bayesian networks provide a well defined structure (as Directed Acyclic Graphs) to represent the problem domain  The nodes represent the variables and the arcs represent the relationships  Information flow is omnidirectional  From Service quality perspective  Nodes represent the parameters  Arcs represent their relationships
  4. 4. Bayesian Networks for Service Quality AnalysisExample Use Cases Service specific network route selection  In this use case, service quality parameter data is available Service specific international roaming list prioritization  In this use case, there are several service quality parameters but there is a lack of data  Here we need to take service quality indicators as parameters.
  5. 5. Service Specific Network Route SelectionTraditional Approach Real time services such as Video on Demand (VoD) require dedicated bandwidth to the subscriber for the defined period When subscriber requests VoD service, the service manager application requests the network management layer to assign the bandwidth to provide the service Traffic Engineering protocols like Resource Reservation Protocol (RSVP), select the network routes which has less congestion and the required bandwidth required to deliver service However, this doesn’t take into account if the route selected is actually suited (based on past history) for the required service (in our example Video on Demand) This may lead to a low service quality experience if the link selected is not suited for real time services leading to an unhappy subscriber
  6. 6. Service Specific Network Route SelectionUsing Bayesian Network Prediction Models We can deploy a Bayesian model to study the characteristics of links and when required propose the suited resource path based on the target service to be delivered The parameters that define a network line characteristics are  Latency  Jitter  Reliability (packet drops) Network line with high reliability (less packet drops) is more suited for transactional applications e.g., online bank transactions (even if the line faces latency problems) Network line with low latency (and jitter) will be more suited for real time applications like voice and video services (even if the line reliability is not good)
  7. 7. Service Specific Network Route Selection Using Bayesian Network Prediction Models (2)  In the figure below, we need to deliver Video on Demand from source to destination with 2 routes connecting source to destination with equal bandwidth  Let’s represent the Bayesian network for Line A as example with parameters Latency, Jitter and Packet Loss Line A Line A - 10 Gbps Jitter Latency Packet Drops Real Time Transactional High High High 50% 50% Low 10 90Source Destination Low High 60 40 Line A Low 30 70 Line B - Low High High 90 10 10 Gbps Low 60 40 Low High 90 10 Jitter Jitter Low 50 50 Latency High Low Packet Drops High 70% 30% Packet Drops Low 40% 60% High Low 50% 50% Latency High Low Latency Marginal probability 50% 50% distribution
  8. 8. Service Specific Network Route Selection Using Bayesian Network Prediction Models (3) There is a 90% chance of Line A being suited for transactional services when there is high latency and jitter and low packet loss Line A Jitter Latency Packet Drops Real Time Transactional Conditional probability High High High 50 50There is a 70% distribution Low 10 90 chance of experiencing Low High 60 40high jitter when Line A Low 30 70 there is high latency Low High High 90 10 Low 60 40 Jitter Low High 90 10 Jitter Low 50 50 Latency High Low Packet Drops High 70 30 Low 40 60 Packet Drops High Low Latency 50 50 High Low Latency Marginal probability 50 50 distribution
  9. 9. Service Specific Network Route Selection Using Bayesian Network Prediction Models (4)  When such Bayesian network models are deployed for each line, the models learn through evidences from the network monitoring applications; the probabilities for the parameters change based on usage experience  Thus, when a service is requested from end users, the network is better informed to make the right resource selection thereby providing a predictable Quality of Service Line A - 10 GbpsSource Destination Line B - 10 Gbps
  10. 10. Service specific international roaming listprioritization Mobile operators are facing a continuous decline in Average Revenue Per User (ARPU) With deregulations, competition is increasing and so is subscriber churn Operators look to focus on protecting high value subscribers and look to offer high service quality for their premium base International roaming being a high revenue and a key service, roaming steering optimization is one of the challenges operators face due to lack of quality data  Operators cannot tap network data from foreign networks their customers have visited and connected  Operators apply business rules to prioritize international roaming lists Bayesian Belief Network models provide a good platform where we can work with lack of data to predict the most preferred roaming list
  11. 11. Service specific international roaming listprioritization (2) In this example, we build a Bayesian network for Operator A’s voice quality Due to lack of roaming network quality data, we use the following indicators  Frequent Call Attempts (FCA) – by gauging the Call Detail Records (CDRs), this can be used as an indication of multiple attempts to make a call due to network problems (coverage, handovers,…)  Manual Network Selection (MNS) – if users select a network which is not as per the prioritized roaming list, it can be an indication that users prefer the selected network quality over the suggested network while roaming  Average Call Duration (ACD) – the average call duration can be a good indicator when you compare the subscriber’s home network average call duration to the roaming call duration Operator A Voice Quality Conditional probability distribution Frequent Call Frequent Call Attempts Attempts Avg. Call Duration High Low Average Call Duration 70% 30% High Low 50% 50% Manual Network Selections High Low Manual Network Selections Marginal probability 50% 50% distribution
  12. 12. Service specific international roaming listprioritization (3) There is a 90% chance of We can compute the conditional probability of Operator Operator A’s voice quality being A’s voice quality by taking evidence of the marginal good when there is high ACD and FCA probabilities of the voice quality indicative parameters from home network databases e.g. HLR Operator A Voice Quality FCA MNS ACD Good Average Bad High High High 70 20 10 Low 30 40 30 Operator A Low High 10 50 40 Voice Quality Low 10 20 70 Low High High 90 10 0 Low 60 30 10 Low High 70 20 10 Frequent Call Low 33 34 33 Frequent Call Attempts Attempts High Low Avg. Call Duration 70% 30% Average Call Duration High Low 50% 50% Manual Network Selections High Low Manual Network Selections 50% 50%
  13. 13. Service specific international roaming listprioritization (4) Applying such models to the operator list, we can derive a dynamic roaming steering list based on probabilities learnt from the Bayesian network models about operator’s service quality indicators. Voice Quality Operator Good Average Poor B 70 20 10 C 55 25 20 . . A 10 30 60 . … … … .
  14. 14. Conclusion The examples in this concept presentation illustrate the generic nature of Bayesian network algorithm and it’s applications to various data driven analysis Both examples show how using Bayesian network models can help predict service quality in cases where there is a lot of evidence data and where there is missing data Deploying such prediction models with existing applications, both datacom and telecom operators can leverage the data analysis to improve service quality (rather predict service quality)