This document discusses using Bayesian networks to improve service quality analysis for communication service providers. It describes how service quality parameters can have multi-dimensional sources and analyzing them can be complex. Bayesian networks provide a well-defined structure to represent relationships between service quality parameters. Two example use cases are described: network route selection and international roaming list prioritization. It provides details on how Bayesian networks can be used for service-specific network route selection to choose routes best suited for different service types like video based on historical performance data.
Gardenia Telco is a leading telecom solutions provider in the Middle East and Africa region. It has offices across Jordan, Saudi Arabia, Egypt, Iraq, and UAE. Gardenia's vision is to become the top provider of fixed, mobile, and cloud-based telecom and software solutions across the MEA region. It offers a wide range of telecom solutions including networking solutions, mobile apps development, consulting services, and value-added services for mobile and fixed line operators.
Gardenia Telco is a leading telecom solutions provider in the Middle East and Africa region. It offers a wide range of telecom consulting, solutions and services including regulatory consulting, value added services, common policy and service management platforms, WiFi solutions, mobile apps development, and cloud security services. Gardenia's vision is to be the top provider of fixed, mobile, and converged network solutions across the MEA region.
Quality of Service (QoS) is an important concept in any network which ultimately leads to network efficiency and customer satisfaction. In this PPT, we deal mainly with the Quality of Service aspects relating to Femto Access Point (FAP) of UMTS technology. PPT mainly deals with the Guaranteed Bit Rate (GBR) implementations.
The document discusses Quality of Service (QoS) techniques used to prioritize certain types of network traffic over others. It covers QoS concepts like classification, marking, queuing, and congestion avoidance. It also provides examples of how to implement QoS in Cisco devices and deploy QoS enterprise-wide.
Tandem Transit LLC is a peering company founded in 2008 that allows telecommunications carriers to exchange off-net traffic without relying solely on RBOC transit providers. It provides an all IP network that supports voice calls with protocol conversion and gateway functionality. Tandem Transit aims to launch a global tandem solution in 2011 called "Tandem in the Cloud" that will allow carriers to exchange traffic via IP protocols.
This document provides an introduction to fundamentals of quality of service (QoS) in computer networks. It defines key QoS metrics like bandwidth, latency, jitter and packet loss. It describes the DiffServ model where packets are marked to indicate required QoS and network devices apply QoS based on these markings. It also discusses signaling QoS requirements using markers like Type of Service, Class of Service and DSCP. It covers topics like per-hop behavior, configuration of class maps and policy maps, queuing and discard algorithms, and traffic shaping algorithms like leaky bucket and token bucket.
Cisco Webex dictado por el Cisco Learning Partner en Fundación Proydesa a más de 20 Academias Locales del país, Bolovia y Paraguay. Realizada en marco del acuerdo entre Fundación Proydesa y la filial Argentina de SLS LATAM, con el objeto de investigar, desarrollar y promover la formación en y con tecnología. Más info. en http://proydesa.org/portal/
Gardenia Telco is a leading telecom solutions provider in the Middle East and Africa region. It has offices across Jordan, Saudi Arabia, Egypt, Iraq, and UAE. Gardenia's vision is to become the top provider of fixed, mobile, and cloud-based telecom and software solutions across the MEA region. It offers a wide range of telecom solutions including networking solutions, mobile apps development, consulting services, and value-added services for mobile and fixed line operators.
Gardenia Telco is a leading telecom solutions provider in the Middle East and Africa region. It offers a wide range of telecom consulting, solutions and services including regulatory consulting, value added services, common policy and service management platforms, WiFi solutions, mobile apps development, and cloud security services. Gardenia's vision is to be the top provider of fixed, mobile, and converged network solutions across the MEA region.
Quality of Service (QoS) is an important concept in any network which ultimately leads to network efficiency and customer satisfaction. In this PPT, we deal mainly with the Quality of Service aspects relating to Femto Access Point (FAP) of UMTS technology. PPT mainly deals with the Guaranteed Bit Rate (GBR) implementations.
The document discusses Quality of Service (QoS) techniques used to prioritize certain types of network traffic over others. It covers QoS concepts like classification, marking, queuing, and congestion avoidance. It also provides examples of how to implement QoS in Cisco devices and deploy QoS enterprise-wide.
Tandem Transit LLC is a peering company founded in 2008 that allows telecommunications carriers to exchange off-net traffic without relying solely on RBOC transit providers. It provides an all IP network that supports voice calls with protocol conversion and gateway functionality. Tandem Transit aims to launch a global tandem solution in 2011 called "Tandem in the Cloud" that will allow carriers to exchange traffic via IP protocols.
This document provides an introduction to fundamentals of quality of service (QoS) in computer networks. It defines key QoS metrics like bandwidth, latency, jitter and packet loss. It describes the DiffServ model where packets are marked to indicate required QoS and network devices apply QoS based on these markings. It also discusses signaling QoS requirements using markers like Type of Service, Class of Service and DSCP. It covers topics like per-hop behavior, configuration of class maps and policy maps, queuing and discard algorithms, and traffic shaping algorithms like leaky bucket and token bucket.
Cisco Webex dictado por el Cisco Learning Partner en Fundación Proydesa a más de 20 Academias Locales del país, Bolovia y Paraguay. Realizada en marco del acuerdo entre Fundación Proydesa y la filial Argentina de SLS LATAM, con el objeto de investigar, desarrollar y promover la formación en y con tecnología. Más info. en http://proydesa.org/portal/
This document summarizes a research paper on simulating and analyzing an admission control mechanism for MPLS Differential Services-Traffic Engineering (DS-TE). It begins with an introduction to MPLS, QoS, and DS-TE. It then describes the proposed admission control mechanism, which uses Bandwidth Agents and multiple queues/tunnels to ensure adequate resources for different traffic classes. The mechanism is evaluated through NS-2 simulations of four scenarios, measuring metrics like throughput, delay, jitter and packet loss. The results show the mechanism improves performance for high-priority traffic by dedicating queues and bandwidth. In conclusion, the admission control mechanism helps preserve end-to-end QoS when transmitting DiffServ traffic over an MPL
This document discusses automation of next generation networks (NGNs) to deliver multicast services. It covers planning issues for deploying multicast across inter-domain networks, including using path computation elements (PCEs) and hierarchical PCEs. Extensions to RSVP signaling are presented as a solution for point-to-multipoint transport across domains. The use of PCEs can offload complex path computations and consider constraints to efficiently deliver services using multicast trees.
Diameter is the next generation Authentication, Authorization and Accounting (AAA) protocol that consists of a base specification and applications. It allows for roaming between networks by authenticating users through their home network and granting access on remote networks. Several organizations are working on Diameter specifications and applications to support different access technologies and services. Further work is still needed to complete specifications for Mobile IP, SIP, and other network applications.
This document discusses e-governance initiatives by Indian Railways, the world's largest rail network under single management. It highlights key IT projects like the Freight Operations Information System (FOIS) and the successful Passenger Reservation System (PRS), which allows reservations from any location. The PRS implementation in the 1980s was a major success, processing over 8.82 lakh seats daily. It improved customer service, resource utilization, and provided real-time reports. Overall, e-governance has helped Indian Railways improve operational efficiency, responsiveness, and adaptability through intelligent use of information technology.
The document discusses the evolution of networks towards Next Generation Networks (NGNs) and describes some of the key components of NGN architectures including IP services, control plane architectures, VoIP, mobility support, QoS, IPv6 migration, and potential local and core network topologies. Reference network architectures are presented for traditional IP networks and the introduction of NGN services in circuit-switched and packet-switched domains.
This document discusses and compares layer-3 and layer-2 approaches to implementing IP/MPLS-based VPNs. MPLS layer-3 VPNs use a routed approach defined in RFC 2547, where customer routes are exchanged between provider edge (PE) routers using BGP. MPLS layer-2 VPNs can provide point-to-point or multi-point connectivity using virtual circuits or virtual private LAN service. The document evaluates aspects of each approach like supported traffic, scalability, and complexity to help service providers determine the best fit for their network.
This document provides an overview of Quality of Service (QoS) in computer networks. It discusses several key IP QoS mechanisms including resource reservation using RSVP, admission control with bandwidth brokers, packet classification and marking, queuing disciplines like priority queuing and weighted fair queuing, traffic shaping using leaky bucket and token bucket algorithms, and policing. It also describes QoS frameworks like IntServ and DiffServ that systematically apply these mechanisms. Finally, it covers QoS in wireless networks, focusing on support in 802.11 networks and interactions with mobility protocols.
This document discusses using fuzzy logic to help manage quality of service (QoS) in satellite communication systems. It outlines some challenges in maintaining service level agreements (SLAs) for QoS in satellite networks due to external factors like weather. The document proposes a fuzzy logic controller to help dynamically tune the parameters of the random early detection (RED) congestion control algorithm used in these networks. This approach aims to make satellite-IP networks more adaptive to changing weather conditions like rain fade that can impact performance.
This document discusses quality of service (QoS) techniques for prioritizing different types of network traffic such as voice over IP. It describes several QoS mechanisms including weighted fair queuing, priority queuing, class-based weighted fair queuing, IP precedence, policy routing, and resource reservation protocol. These mechanisms allow administrators to classify and manage network traffic to ensure sufficient bandwidth and latency for applications like VoIP that have sensitive network requirements.
Capgemini is expanding its UK Nearshore Delivery Centers to better serve customers in regards to data privacy, location advantages, and staff retention. Some key benefits of the UK locations include being subject to EU data privacy laws, ease of travel to customer sites, and a staff attrition rate of only 10.7% in 2011. Capgemini can also achieve cost savings of up to 20% at the UK locations compared to other sites. The document outlines Capgemini's existing UK delivery center locations and client base, as well as the services and training programs available at the centers.
The document discusses QoS models and differentiated services model features. It provides an overview of MPLS QoS, including mapping IP precedence to MPLS experimental bits, supporting DiffServ over MPLS using E-LSPs and L-LSPs, and examples of configuring MPLS QoS on PE routers including classification, policy maps, and attaching policies to interfaces.
The document discusses the operations of the Mumbai Dabbawala network. It provides an overview of the history and operations of the Nutan Mumbai Tiffin Box Suppliers Association. Key details include their coding system for accurate deliveries, supply chain processes, performance metrics, costs, revenues, sustainability factors, and future concerns around changing customer needs and workforce retention.
Mr Marcus Wiko Consult Peering Qos Price and Quality IDATE DigiWorld
This document discusses peering, quality of service (QoS), and price differentiation on the internet. It covers peering agreements between internet service providers (ISPs), declining prices for internet transit, the impact of increasing video traffic on networks, and how QoS requirements vary for different application types like voice, video, and gaming. It also includes charts on global transit prices over time and the relationship between link utilization and queuing delay.
Weighted fair queuing and RSVP are technologies that can help improve the transport of time sensitive traffic like voice and video across networks with limited bandwidth. The document describes how weighted fair queuing prioritizes traffic by separating it into parallel queues and giving some queues higher priority. RSVP allows endpoints to reserve bandwidth for applications by identifying traffic streams so routers can give them priority. The author tests these technologies on their network and finds that using weighted fair queuing and RSVP improves the quality of their LiveLAN video conferencing sessions during periods of network congestion.
This document describes aspects of web services including service description, endpoints, bindings, contracts, operations, messages, and the service host. It outlines the layers involved in processing messages including the message layer, service model layer, and dispatch operation. Behaviors, error handling, message inspectors, and the dispatch runtime are also summarized.
The document compares key characteristics of PSTN, Internet, and NGN networks. It shows that NGN networks provide the highest levels of multimedia capability, quality of service, network intelligence, intelligent customer premises equipment, packet transport, distinct architecture, integrated control, service reliability, and evolvability/modularity. NGN also provides the shortest time to market while enabling more systematic service creation and the highest levels of ease of use and architecture openness.
Financial Markets Signal Detection with Bayesian Networks - Phd DREAMT - Work...Alessandro Greppi
In order to model and explain the dynamics and the signals of the market, financial operators should take into account different kind and source of information.
Unfortunately, standard tools are not always able to summarize in a signal the big amount of information available. We propose to use Bayesian Networks as a quantitative financial tool for this aim. By exploiting the network, we can combine
in the model, both market variables and sentiment ones. Bayesian Networks can be used to show the relationship among the variables belonging to different areas, and to identify in a mouse-click time the configuration that provide an operative signal. An application to the analysis of S&P 500 in the periods 1994-2003 and 2004-2015
is presented.
The document discusses Bayesian belief networks (BBNs), which represent probabilistic relationships between variables. BBNs consist of a directed acyclic graph showing the dependencies between nodes/variables, and conditional probability tables quantifying the effects. They allow representing conditional independence between non-descendant variables given parents. The document provides an example BBN modeling a home alarm system and neighbors calling police. It then shows calculations to find the probability of a burglary given one neighbor called police using the network. Advantages are handling incomplete data, learning causation, and using prior knowledge, while a disadvantage is more complex graph construction.
This document summarizes a research paper on simulating and analyzing an admission control mechanism for MPLS Differential Services-Traffic Engineering (DS-TE). It begins with an introduction to MPLS, QoS, and DS-TE. It then describes the proposed admission control mechanism, which uses Bandwidth Agents and multiple queues/tunnels to ensure adequate resources for different traffic classes. The mechanism is evaluated through NS-2 simulations of four scenarios, measuring metrics like throughput, delay, jitter and packet loss. The results show the mechanism improves performance for high-priority traffic by dedicating queues and bandwidth. In conclusion, the admission control mechanism helps preserve end-to-end QoS when transmitting DiffServ traffic over an MPL
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Diameter is the next generation Authentication, Authorization and Accounting (AAA) protocol that consists of a base specification and applications. It allows for roaming between networks by authenticating users through their home network and granting access on remote networks. Several organizations are working on Diameter specifications and applications to support different access technologies and services. Further work is still needed to complete specifications for Mobile IP, SIP, and other network applications.
This document discusses e-governance initiatives by Indian Railways, the world's largest rail network under single management. It highlights key IT projects like the Freight Operations Information System (FOIS) and the successful Passenger Reservation System (PRS), which allows reservations from any location. The PRS implementation in the 1980s was a major success, processing over 8.82 lakh seats daily. It improved customer service, resource utilization, and provided real-time reports. Overall, e-governance has helped Indian Railways improve operational efficiency, responsiveness, and adaptability through intelligent use of information technology.
The document discusses the evolution of networks towards Next Generation Networks (NGNs) and describes some of the key components of NGN architectures including IP services, control plane architectures, VoIP, mobility support, QoS, IPv6 migration, and potential local and core network topologies. Reference network architectures are presented for traditional IP networks and the introduction of NGN services in circuit-switched and packet-switched domains.
This document discusses and compares layer-3 and layer-2 approaches to implementing IP/MPLS-based VPNs. MPLS layer-3 VPNs use a routed approach defined in RFC 2547, where customer routes are exchanged between provider edge (PE) routers using BGP. MPLS layer-2 VPNs can provide point-to-point or multi-point connectivity using virtual circuits or virtual private LAN service. The document evaluates aspects of each approach like supported traffic, scalability, and complexity to help service providers determine the best fit for their network.
This document provides an overview of Quality of Service (QoS) in computer networks. It discusses several key IP QoS mechanisms including resource reservation using RSVP, admission control with bandwidth brokers, packet classification and marking, queuing disciplines like priority queuing and weighted fair queuing, traffic shaping using leaky bucket and token bucket algorithms, and policing. It also describes QoS frameworks like IntServ and DiffServ that systematically apply these mechanisms. Finally, it covers QoS in wireless networks, focusing on support in 802.11 networks and interactions with mobility protocols.
This document discusses using fuzzy logic to help manage quality of service (QoS) in satellite communication systems. It outlines some challenges in maintaining service level agreements (SLAs) for QoS in satellite networks due to external factors like weather. The document proposes a fuzzy logic controller to help dynamically tune the parameters of the random early detection (RED) congestion control algorithm used in these networks. This approach aims to make satellite-IP networks more adaptive to changing weather conditions like rain fade that can impact performance.
This document discusses quality of service (QoS) techniques for prioritizing different types of network traffic such as voice over IP. It describes several QoS mechanisms including weighted fair queuing, priority queuing, class-based weighted fair queuing, IP precedence, policy routing, and resource reservation protocol. These mechanisms allow administrators to classify and manage network traffic to ensure sufficient bandwidth and latency for applications like VoIP that have sensitive network requirements.
Capgemini is expanding its UK Nearshore Delivery Centers to better serve customers in regards to data privacy, location advantages, and staff retention. Some key benefits of the UK locations include being subject to EU data privacy laws, ease of travel to customer sites, and a staff attrition rate of only 10.7% in 2011. Capgemini can also achieve cost savings of up to 20% at the UK locations compared to other sites. The document outlines Capgemini's existing UK delivery center locations and client base, as well as the services and training programs available at the centers.
The document discusses QoS models and differentiated services model features. It provides an overview of MPLS QoS, including mapping IP precedence to MPLS experimental bits, supporting DiffServ over MPLS using E-LSPs and L-LSPs, and examples of configuring MPLS QoS on PE routers including classification, policy maps, and attaching policies to interfaces.
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Mr Marcus Wiko Consult Peering Qos Price and Quality IDATE DigiWorld
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This document describes aspects of web services including service description, endpoints, bindings, contracts, operations, messages, and the service host. It outlines the layers involved in processing messages including the message layer, service model layer, and dispatch operation. Behaviors, error handling, message inspectors, and the dispatch runtime are also summarized.
The document compares key characteristics of PSTN, Internet, and NGN networks. It shows that NGN networks provide the highest levels of multimedia capability, quality of service, network intelligence, intelligent customer premises equipment, packet transport, distinct architecture, integrated control, service reliability, and evolvability/modularity. NGN also provides the shortest time to market while enabling more systematic service creation and the highest levels of ease of use and architecture openness.
Financial Markets Signal Detection with Bayesian Networks - Phd DREAMT - Work...Alessandro Greppi
In order to model and explain the dynamics and the signals of the market, financial operators should take into account different kind and source of information.
Unfortunately, standard tools are not always able to summarize in a signal the big amount of information available. We propose to use Bayesian Networks as a quantitative financial tool for this aim. By exploiting the network, we can combine
in the model, both market variables and sentiment ones. Bayesian Networks can be used to show the relationship among the variables belonging to different areas, and to identify in a mouse-click time the configuration that provide an operative signal. An application to the analysis of S&P 500 in the periods 1994-2003 and 2004-2015
is presented.
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The document provides an example of alpha-beta pruning, an algorithm that prunes branches from a game tree during a depth-first search. It demonstrates how the algorithm works by showing the values of alpha and beta passed between nodes and updated at max and min nodes. Branches are pruned when alpha is greater than or equal to beta, indicating the current path will not provide a better solution than one already found. The example shows how alpha-beta pruning reduces the number of nodes that must be evaluated to determine the optimal move in a game.
Understanding your data with Bayesian networks (in Python) by Bartek Wilczyns...PyData
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This document provides an overview of artificial intelligence (AI), including its history, current applications, and potential future. It discusses early developments in AI from the 1940s through 1990s and its increasing use today. Current applications covered are expert systems, natural language processing, speech recognition, computer vision, robotics, and automatic programming. The document considers both positive potential futures where AI assists humans and negative risks like autonomous robots harming people. It concludes that AI has increased understanding of intelligence while revealing its complexity.
The document discusses the Minimax algorithm and its application to game trees. It explains that Minimax is an optimal decision-making procedure for two-player zero-sum games where one player tries to maximize their score and the other tries to minimize it. It provides examples of how Minimax can be applied to games like Tic-Tac-Toe, Chess, Poker, and Monopoly to find the best move assuming the opponent plays optimally.
Game playing in artificial intelligent technique syeda zoya mehdi
The document discusses game artificial intelligence and techniques used to generate intelligent behavior in non-player characters in computer and video games. It covers topics like machine learning, reinforcement learning, pathfinding algorithms, and different data structures used to represent game boards and chess positions. Game AI aims to create behavior that feels natural to the player while obeying the rules of the game. Various computer science disciplines are required to develop effective game AI, and different types of games require different AI techniques.
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Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence
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Bayesian Networks - A Brief IntroductionAdnan Masood
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This document discusses genetic algorithms and their applications. It explains key concepts like genetic crossover, genetic algorithm steps to solve optimization problems, and how genetic algorithms mimic biological evolution. Examples are provided of genetic algorithms being used for tasks like predicting protein structure, automotive design optimization, and generating musical variations. Advantages and limitations of genetic algorithms are also summarized.
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Deep dive network requirementsfor enterprise video conferencingInterop
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Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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. 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. Bayesian Networks for Service Quality Analysis
Example 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. 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
6. 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)
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 90
Source 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. 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 50
There is a 70% distribution Low 10 90
chance of
experiencing Low High 60 40
high 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. 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
Source Destination
Line B -
10 Gbps
10. 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
11. 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
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. Service specific international roaming list
prioritization (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. 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.
Voice Quality
Operator
Good Average Poor
B 70 20 10
C 55 25 20
.
. A 10 30 60
. … … …
.
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)