Network performance management

• US 7,929,457 B2
• Filed: 11/12/2009
• Issued: 04/19/2011
• Est. Priority Date: 11/07/2005
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Schornstein, Stefan, Hausammann, Rolf Karl, Healy, Desmond
• Primary Examiner(s): Wong; Warner
• Application Number:
  US12/616,936
• Publication Number:
  US 20100054149A1
• Time in Patent Office: 523 Days
• Field of Search: 370/230, 370/244, 370/252, 370/395.21, 718/104
• US Class Current: 370/252
• CPC Class Codes: H04L 41/18 Delegation of network management function, e.g. customer network management [CNM]
• CPC Class Codes: H04L 41/5003 Managing SLA; Interaction between SLA and QoS
• CPC Class Codes: H04L 41/5067 Customer-centric QoS measurements
• CPC Class Codes: H04W 28/18 Negotiating wireless communication parameters

Abstract

The performance of a network can be managed by receiving a set of key quality parameters, wherein a key quality parameter represents a characteristic associated with at least one network component; identifying a service quality mapping module based on one or more network components associated with providing an end customer service, wherein the service quality mapping module corresponds to at least one service instance and defines a correlation between the set of key quality parameters and a set of customer quality parameters; applying the service quality mapping module to the set of key quality parameters to generate the set of customer quality parameters; and outputting a signal representing at least one customer quality parameter included in the set of customer quality parameters. Further, the service quality mapping module can define a linear dependency between a customer quality parameter and at least one key quality parameter.

Claims

1. A method comprising:

measuring performance characteristics of multiple, individual network components that are used to provide a customer service to an end customer, the customer service including one or more service instances that each use a different end-to-end chain of the network components;

generating trigger signals that include values representing the measured performance characteristics of the individual network components of an end-to-end chain;

selecting a mapping model from among multiple mapping models, the selected mapping model including at least first through third correlation functions that each define one or more linkages between parameters of neighboring parameter levels, and one or more weighting factors;

applying the first correlation function to the trigger signals to obtain a set of key quality parameters that represent a performance of the individual network component in the end-to-end chain of the network components;

applying the second correlation function to the set of key quality parameters to obtain a set of service quality parameters that represent a measured performance of the end-to-end chain of the network components used by a service instance;

applying the third correlation function to the set of service quality parameters to obtain a customer quality parameter which represents a service quality of the customer service as perceived by the end customer; and

outputting an output signal that includes the customer quality parameter.

2. The method of claim 1, wherein the customer quality parameter reflects a reliability, speed, predictability, satisfaction or efficiency quality of the customer service as perceived by the end customer.

3. The method of claim 1, wherein measuring the performance characteristics of the network components further comprises performing an automated detection, measurement, sensing, or counting of the performance characteristics.

4. The method of claim 1, wherein measuring the performance characteristics of the network components further comprises receiving a manually input measurement of one or more of the performance characteristics.

5. The method of claim 1, wherein applying the first correlation function further comprises applying a normalized grading scheme to the trigger signals.

6. The method of claim 1, wherein the measured performance characteristics comprise two or more of:

an available bandwidth,

a speed of the network,

a delay time of the components

a response time of the components,

an availability of certain information, and

an error rate during transmission of data.

7. The method of claim 1, wherein one or more of the trigger signals comprises a continuous trigger signal specifying continuous values for the measured performance characteristics.

8. The method of claim 1, wherein applying the third correlation function further comprises:

applying a first weighting factor to a first service quality parameter;

applying a second weighting factor to a second service quality parameter; and

combining a result of applying the first and second weighting factors to generate a first customer quality parameter.

9. The method of claim 1, wherein applying the second correlation function further comprises:

applying a first weighting factor to a first key quality parameter;

applying a second weighting factor to a second key quality parameter; and

combining a result of applying the first and second weighting factors to generate a first service quality parameter.

10. The method of claim 1, wherein applying the first correlation function further comprises identifying two or more trigger signals that upwardly correlate to a single key quality parameter.

11. The method of claim 1, wherein applying the second correlation function further comprises identifying two or more key quality parameters that upwardly correlate to a single service quality parameter.

12. The method of claim 1, wherein applying the third correlation function further comprises identifying two or more service quality parameters that upwardly correlate to a single customer quality parameter.

13. The method of claim 1, further comprising:

determining that the customer quality parameter deviates from a predetermined value by more than a predetermined threshold,

wherein the output signal is output when determining that the customer quality parameter deviates from the predetermined value by more than the predetermined threshold.

14. The method of claim 1, wherein the customer service comprise a web surfing via a first device service instance, and a file downloading via a second device service instance.

15. The method of claim 1, wherein the performance characteristics comprise key performance indicators, process performance indicators, and operational performance indicators.

16. The method of claim 1, further comprising:

allocating an infrastructure investment based on the output signal.

20. The method of claim 1, wherein the network components of each end-to-end chain include one or more end user device components, one or more network access components, one or more core network components, and one or more application components.

17. A system comprising:

one or more computers; and

a non-transitory computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising;

measuring performance characteristics of multiple, individual network components that are used to provide a customer service to an end customer, the customer service including one or more service instances that each use a different end-to-end chain of the network components;

generating trigger signals that include values representing the measured performance characteristics of the individual network components of an end-to-end chain;

selecting a mapping model from among multiple mapping models, the selected mapping model including at least first through third correlation functions that each define one or more linkages between parameters of neighboring parameter levels, and one or more weighting factors;

applying the first correlation function to the trigger signals to obtain a set of key quality parameters that represent a performance of the individual network component in the end-to-end chain of the network components;

applying the second correlation function to the set of key quality parameters to obtain a set of service quality parameters that represent a measured performance of the end-to-end chain of the network components used by a service instance;

applying the third correlation function to the set of service quality parameters to obtain a customer quality parameter which represents a service quality of the customer service as perceived by the end customer; and

outputting an output signal that includes the customer quality parameter.

18. The system of claim 17, wherein applying the third correlation function further comprises:

applying a first weighting factor to a first service quality parameter;

applying a second weighting factor to a second service quality parameter; and

combining a result of applying the first and second weighting factors to generate a first customer quality parameter.

19. A non-transitory computer storage medium encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising:

measuring performance characteristics of multiple, individual network components that are used to provide a customer service to an end customer, the customer service including one or more service instances that each use a different end-to-end chain of the network components;

generating trigger signals that include values representing the measured performance characteristics of the individual network components of an end-to-end chain;

selecting a mapping model from among multiple mapping models, the selected mapping model including at least first through third correlation functions that each define one or more linkages between parameters of neighboring parameter levels, and one or more weighting factors;

applying the first correlation function to the trigger signals to obtain a set of key quality parameters that represent a performance of the individual network component in the end-to-end chain of the network components;

applying the second correlation function to the set of key quality parameters to obtain a set of service quality parameters that represent a measured performance of the end-to-end chain of the network components used by a service instance;

applying the third correlation function to the set of service quality parameters to obtain a customer quality parameter which represents a service quality of the customer service as perceived by the end customer; and

outputting an output signal that includes the customer quality parameter.