Patent Publication Number: US-2007115916-A1

Title: Method and system for optimizing a network based on a performance knowledge base

Description:
TECHNICAL FIELD OF THE INVENTION  
      The present disclosure relates generally to distributed networks and, more specifically, to a method and system for optimizing a network based on a performance knowledge base.  
     BACKGROUND OF THE INVENTION  
      As modern telecommunications networks have become more and more distributed, many network entities in the network are inter-related with each other and work together to provide telecommunication services to network users. At the same time, the traffic load generated by network users is becoming more complicated as more innovative and demanding services are added to network service portfolios. In addition, user mobility has increased with the advances in wireless technologies. These factors lead to telecom networks that require advanced optimization methods. Therefore, there is a need in the art for improved optimization methods to achieve optimized network operations.  
     SUMMARY OF THE INVENTION  
      A method for optimizing a network based on a performance knowledge base is provided. The network comprises a plurality of network elements. According to an advantageous embodiment of the present disclosure, the method includes analyzing raw performance data for each of the network elements in real-time to generate processed data and optimizing the network based on the processed data.  
      According to one embodiment of the present disclosure, the method also includes generating optimization policies based on the processed data in real-time and optimizing the network based on the processed data comprises provisioning the network elements based on the optimization policies.  
      According to another embodiment of the present disclosure, the method also includes collecting the raw performance data from each of the network elements in real-time and storing the raw performance data in a performance knowledge base.  
      According to still another embodiment of the present disclosure, the method also includes receiving optimization rules and network policies from an operator and storing the optimization rules and network policies in the performance knowledge base.  
      According to yet another embodiment of the present disclosure, the method also includes storing the processed data in the performance knowledge base.  
      According to a further embodiment of the present disclosure, the method also includes generating optimization policies based on the processed data and on the optimization rules and network policies in real-time and storing the optimization polices in the performance knowledge base.  
      According to still a further embodiment of the present disclosure, the method also includes generating engineering data based on the processed data and on the optimization policies and storing the engineering data in the performance knowledge base, and optimizing the network based on the processed data comprises provisioning the network elements based on the engineering data.  
      Before undertaking the DETAILED DESCRIPTION OF THE INVENTION below, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document: the terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation; the term “or,” is inclusive, meaning and/or; the term “each” means every one of at least a subset of the identified items; the phrases “associated with” and “associated therewith,” as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, or the like; and the term “controller” means any device, system or part thereof that controls at least one operation, such a device may be implemented in hardware, firmware or software, or some combination of at least two of the same. It should be noted that the functionality associated with any particular controller may be centralized or distributed, whether locally or remotely. Definitions for certain words and phrases are provided throughout this patent document, those of ordinary skill in the art should understand that in many, if not most instances, such definitions apply to prior, as well as future uses of such defined words and phrases.  
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
      For a more complete understanding of the present disclosure and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, in which like reference numerals represent like parts:  
       FIG. 1  illustrates an exemplary distributed network that is capable of being optimized based on a performance knowledge base according to an embodiment of the present disclosure;  
       FIG. 2  illustrates details of the performance knowledge base of  FIG. 1  according to an embodiment of the present disclosure; and  
       FIG. 3  is a flow diagram illustrating a method for optimizing the network of  FIG. 1  based on the performance knowledge base of  FIG. 2  according to an embodiment of the present disclosure.  
    
    
     DETAILED DESCRIPTION OF THE INVENTION  
       FIGS. 1 through 3 , discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way to limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged distributed network.  
      To improve operating efficiency of and reduce costs of modern telecommunications networks, service providers are constantly searching for better and more efficient network optimization techniques that are able to handle the more complicated traffic generated by the new services and new mobility capabilities. On the other hand, as new services are added and new technologies are applied, network optimization may require new rules or policies to remain effective.  
      Conventional optimization techniques collect traffic logs and events of a network node, such as a Mobile Switching Center, for example, and then manually analyze the data to predict future behavior of network users. Results from the traffic analysis are then applied to provisioning and planning for future networks. These techniques typically attempt to optimize each network node separately. However, as networks have become more distributed, the behavior of one network node tends to have more of an effect on, and be more affected by, the behavior of other network nodes.  
      Therefore, there is a need in the art for an improved method of optimizing a network. In particular, there is a need for a method of optimizing a distributed network more efficiently in a manner capable of handling increasing traffic loads and more demanding services and that takes into account the behavior of multiple network nodes in optimizing each network node in the network.  
       FIG. 1  illustrates an exemplary distributed network  100  that is capable of being optimized based on a performance knowledge base according to an embodiment of the present disclosure. Distributed network  100  may comprise a telecommunications network or other suitable type of network that comprises distributed components that are operable to function together to provide services for clients.  
      For one embodiment, network  100  comprises a network operations center  105  and a plurality of network elements (NEs)  110 - 114 . For the illustrated embodiment, network operations center  105  is coupled to an operator interface  120  and comprises an optimization module  125 . However, it will be understood that optimization module  125  may be implemented in any suitable component of network  100  or may be implemented independently of any other component without departing from the scope of the present disclosure.  
      Network operations center  105  may comprise a computer or any other suitable device capable of monitoring and controlling a number of geographically dispersed network elements  110 - 114 . According to one embodiment, network elements  110 - 114  may comprise base transceiver stations, controllers, routers, switches, service creation points, protocol converters, interface cards, channel cards, transcoders, radios and/or any other suitable network elements. Network operations center  105 , and therefore optimization module  125 , and network elements  110 - 114  are operable to communicate with each other over communication links  130 , which may comprise T 1  lines, Internet Protocol (IP) links through the Internet and/or any other suitable type of communication links.  
      Operator interface  120  is operable to provide an interface between optimization module  125  and an operator of optimization module  125 . Thus, using operator interface  120 , an operator may interact with optimization module  125  and prompt optimization module  125  to perform optimization functions. The operator may also provide to optimization module  125  rules and policies that may be used in optimizing network  100 . In addition, optimization module  125  is operable to provide optimization information to the operator using operator interface  120 . It will be understood that operator interface  120  may also be operable to provide an interface between network operations center  105  and an operator of network operations center  105 .  
      For the illustrated embodiment, optimization module  125  comprises a performance knowledge base  150 , a network analyzer  155 , a policy generator  160 , a network optimizer  165 , and a knowledge base engine  170 . As described in more detail below in connection with  FIG. 2 , performance knowledge base  150  comprises any suitable data store, such as a database, that is operable to store optimization data for use in optimizing network  100  and optimization rules and policies for optimizing network  100 .  
      Although illustrated and described as four separate components, it will be understood that any combination of two or more of network analyzer  155 , policy generator  160 , network optimizer  165  and knowledge base engine  170  may be implemented together as a single component without departing from the scope of the present disclosure.  
      Network analyzer  155  is operable to analyze raw performance data stored in performance knowledge base  150  to generate processed data and may be operable to store the processed data in performance knowledge base  150 . Policy generator  160  is operable to generate optimization policies for network  100  based on optimization rules and network policies stored in performance knowledge base  150  and may be operable to store the optimization policies in performance knowledge base  150 .  
      Network optimizer  165  is operable to generate network engineering data for provisioning network  100  based on the network optimization policies generated by policy generator  160  and based on the processed data analyzed by network analyzer  155 . Network optimizer  165  may also be operable to store the engineering data in performance knowledge base  150 .  
      Knowledge base engine  170  is operable to manage data, rules and policies stored in performance knowledge base  150 . Knowledge base engine  170  is also operable to derive new rules and policies according to changes in the stored data. For one embodiment, knowledge base engine  170 , instead of network analyzer  155 , policy generator  160  and network optimizer  165 , may be operable to store the processed data, optimization policies and engineering data in performance knowledge base  150 .  
       FIG. 2  illustrates details of performance knowledge base  150  according to an embodiment of the present disclosure. For the illustrated embodiment, performance knowledge base  150  comprises raw performance data  205 , processed data  210 , optimization rules and network policies  215 , optimization policies  220 , and engineering data  225 . It will be understood that performance knowledge base  150  may store additional types of data without departing from the scope of the present disclosure.  
      In addition, although each of raw performance data  205 , processed data  210 , optimization rules and network policies  215 , optimization policies  220 , and engineering data  225  may be stored separately in segmented portions of performance knowledge base  150 , it will be understood that any or all of these sections of data  205 ,  210 ,  215 ,  220  and  225  may be stored together in performance knowledge base  150  and identified as distinct types of data  205 ,  210 ,  215 ,  220  and/or  225  in any suitable manner without departing from the scope of the present disclosure.  
      Raw performance data  205  comprises information pertinent to network operation and performance that is collected by knowledge base engine  170  in real-time from network elements  110 - 114 . Thus, network performance data, such as the number of calls processed, processing costs in terms of CPU cycles for a particular call and/or other suitable performance data, is collected from network elements  110 - 114  and stored in raw performance data  205  as network  100  is operating. Based on the manner in which the phrase is used, it will be understood that “raw performance data  205 ” may refer to the actual raw performance data stored in performance knowledge base  150  or to the portion of performance knowledge base  150  in which the raw performance data is stored.  
      Processed data  210  comprises information that is used for network operation and optimization tasks. Processed data  210  is generated by network analyzer  155  in real-time based on raw performance data  205 . Based on the manner in which the phrase is used, it will be understood that “processed data  210 ” may refer to the actual processed data stored in performance knowledge base  150  or to the portion of performance knowledge base  150  in which the processed data is stored.  
      Optimization rules and network policies  215  comprise rules and policies stored in performance knowledge base  150  by an operator using operator interface  120 . These rules and policies  215  govern how network optimization policies  220  are produced. Based on the manner in which the phrase is used, it will be understood that “optimization rules and network policies  215 ” may refer to the actual optimization rules and network policies stored in performance knowledge base  150  or to the portion of performance knowledge base  150  in which the optimization rules and network policies are stored.  
      Optimization policies  220  comprise policies generated by policy generator  160  based on processed data  210  and optimization rules and network policies  215 . Based on the manner in which the phrase is used, it will be understood that “optimization policies  220 ” may refer to the actual optimization policies stored in performance knowledge base  150  or to the portion of performance knowledge base  150  in which the optimization policies are stored.  
      Engineering data  225  comprises information used for provisioning network  100  such that network  100  is operated in a manner that achieves the goals set for network optimization. Engineering data  225  is generated by network optimizer  165  based on processed data  210  and optimization policies  220  and is used to provision network elements  110 - 114 . Based on the manner in which the phrase is used, it will be understood that “engineering data  225 ” may refer to the actual engineering data stored in performance knowledge base  150  or to the portion of performance knowledge base  150  in which the engineering data is stored.  
      Raw performance data  205  and processed data  210 , as well as optimization rules and network policies  215 , may be managed by knowledge base engine  170 . In addition, knowledge base engine  170  may derive new rules and policies according to changes in raw performance data  205  and processed data  210 .  
       FIG. 3  is a flow diagram illustrating a method  300  for optimizing network  100  based on raw performance data  205  according to an embodiment of the present disclosure. Initially, optimization module  125  receives optimization rules and network policies  215  and knowledge base engine  170  stores those rules and policies  215  in performance knowledge base  150  (process step  305 ). For one embodiment, optimization module  125  may receive the optimization rules and network policies  215  from an operator through operator interface  120 .  
      Knowledge base engine  170  collects raw performance data  205  from network elements  110 - 114  in real-time while network  100  is operating (process step  310 ) and stores the raw performance data  205  in performance knowledge base  150  (process step  315 ). Network analyzer  155  then analyzes the raw performance data  205  stored in performance knowledge base  150  in real-time to generate processed data  210  (process step  320 ). Knowledge base engine  170  may then store the processed data  210  in performance knowledge base  150  (process step  325 ). Alternatively, network analyzer  155  may store the processed data  210  in performance knowledge base  150 .  
      Policy generator  160  then generates optimization policies  220  in real-time based on the processed data  210  and the optimization rules and network policies  215  stored in performance knowledge base  150  (process step  330 ). Knowledge base engine  170  may then store the optimization policies  220  in performance knowledge base  150  (process step  335 ). Alternatively, policy generator  160  may store the optimization policies  220  in performance knowledge base  150 .  
      Network optimizer  165  then generates engineering data  225  based on the processed data  210  and optimization policies  220  stored in performance knowledge base  150  (process step  340 ). Knowledge base engine  170  may then store the engineering data  225  in performance knowledge base  150  (process step  345 ). Alternatively, network optimizer  165  may store the engineering data  225  in performance knowledge base  150 .  
      Finally, optimization module  125  provisions network elements  110 - 114  based on the engineering data  225  stored in performance knowledge base  150  (process step  350 ). While the method is being performed and/or after provisioning network  100 , knowledge base engine  170  may continue to collect additional raw performance data  205  in real-time from network elements  110 - 114  (process step  310 ). It will be understood that optimization module  125  may receive changes to and/or additional optimization rules and network policies  215  (process step  305 ) from an operator through operator interface  120  at any suitable time.  
      In this way, as new services and traffic conditions are applied to network  100 , new traffic characteristics are extracted by optimization module  125  in real-time. These characteristics are then used by policy generator  160 , also in a real-time manner, to generate new optimization policies  220  that adapt to the new traffic conditions. These optimization policies  220  may then be used to generate engineering data  225  for provisioning network  100  in such a way as to ensure that the optimization goals are consistently achieved. Thus, network  100  may be optimized based on data provided by each network element  110 - 114 , thereby taking into account any effects from surrounding network elements  110 - 114  on each other network element  110 - 114 , and based on a real-time analysis of the data.  
      While several embodiments have been provided in the present disclosure, it should be understood that the disclosed systems and methods may be embodied in many other specific forms without departing from the spirit or scope of the present disclosure. The exemplary embodiments disclosed are to be considered as illustrative and not restrictive, and the intention is not to be limited to the details given herein. It is intended that the disclosure encompass all alternate forms within the scope of the appended claims along with their full scope of equivalents.