Improving Service Quality using Bayesian networks

Kiran Kaipa

kiran.kaipa@gmail.com

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

Service Quality AnalysisBayesian 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

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.

Service Specific Network Route Selection Traditional 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

Service Specific Network Route Selection Using 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)

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 - 10 Gbps

Line B - 10 Gbps

Source Destination

Line A

Jitter Latency Packet Drops Real Time Transactional

High High High 50% 50%

Low 10 90

Low High 60 40

Low 30 70

Low High High 90 10

Low 60 40

Low High 90 10

Low 50 50

Packet Drops

High Low

50% 50%Latency

High Low

50% 50%

Jitter

Latency

High Low

High 70% 30%

Low 40% 60%

Marginal probability distribution

Line A

Packet Drops

Latency

Jitter

Service Specific Network Route Selection Using Bayesian Network Prediction Models (3)

Line A

Jitter Latency Packet Drops Real Time Transactional

High High High 50 50

Low 10 90

Low High 60 40

Low 30 70

Low High High 90 10

Low 60 40

Low High 90 10

Low 50 50

Packet Drops

High Low

50 50Latency

High Low

50 50

Jitter

Latency

High Low

High 70 30

Low 40 60

Marginal probability distribution

Conditional probability distribution

Line A

Packet Drops

Latency

Jitter

There is a 70% chance of

experiencing high jitter

when there is high latency

There is a 90% chance of Line A being suited for transactional services

when there is high latency and jitter and low packet

loss

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 Gbps

Line B - 10 Gbps

Source Destination

Service specific international roaming list prioritization 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

Service specific international roaming list prioritization (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

Average Call Duration

High Low

50% 50%Manual Network

Selections

High Low

50% 50%

Frequent Call Attempts

High Low

70% 30%

Marginal probability distribution

Operator A Voice Quality

Avg. Call Duration

Manual Network Selections

Frequent Call Attempts

Conditional probability distribution

Service specific international roaming list prioritization (3) We can compute the conditional probability of

Operator A’s voice quality by taking evidence of the marginal 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

Low High 10 50 40

Low 10 20 70

Low High High 90 10 0

Low 60 30 10

Low High 70 20 10

Low 33 34 33

Average Call Duration

High Low

50% 50%Manual Network

Selections

High Low

50% 50%

Frequent Call Attempts

High Low

70% 30%

Operator A Voice Quality

Avg. Call Duration

Manual Network Selections

Frequent Call Attempts

There is a 90% chance of Operator A’s voice quality being good when there is

high ACD and FCA

Service specific international roaming list prioritization (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.

.

.

.

.

Operator Voice Quality

Good Average Poor

B 70 20 10

C 55 25 20

A 10 30 60

… … …

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)