Patent Publication Number: US-2022217061-A1

Title: Processing performance data of a content delivery network

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 16/735,052 filed on Jan. 6, 2020, which is a continuation of U.S. patent application Ser. No. 14/970,179 (now U.S. Pat. No. 10,567,246), filed on Dec. 15, 2015. All sections of the aforementioned application(s) and/or patent(s) are incorporated herein by reference in their entirety. 
    
    
     FIELD OF THE DISCLOSURE 
     The present disclosure is generally related to processing performance data of a content delivery network. 
     BACKGROUND 
     Content delivery networks are configured to deliver multimedia content to user devices, such as residential gateways. To monitor conditions of a content delivery network, various performance data may be measured by devices of the content delivery network. The performance data may be stored in a database or other memory for analysis by a technician or other operator. However, the performance data may be too voluminous to enable the technician to quickly analyze the performance data and identify a relevant performance metric that relates to a service issue. For example, the technician may not be able to view an output of the performance data and quickly identify a location of a service issue within the content delivery network. 
     In addition, the technician may analyze performance data of a particular device, such as a particular residential gateway, when a service call indicates a service issue for the particular device. However, analysis of the performance data for the particular device may not indicate whether other devices of the content delivery network are experiencing similar or related issues. Thus, a technician may spend time trying to correct a service issue at the particular device without realizing that the service issue is a symptom of another service issue within the content delivery network. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a particular implementation of a system for analyzing performance data and generating a display based on a performance metric; 
         FIG. 2  is a diagram illustrating a particular example of a display that visually represents at least one component of a content delivery network, a performance metric, and geographic data; 
         FIG. 3  is a diagram illustrating a particular implementation of a user interface displayed by a network analysis device; 
         FIG. 4  is a flowchart of an illustrative implementation of a method of analyzing performance data and generating a display based on a performance metric; 
         FIG. 5  is a flowchart of an illustrative implementation of a method of generating a display based on a performance metric; and 
         FIG. 6  is a block diagram of an illustrative implementation of a computer system supporting various aspects of method(s), system(s), apparatus(es), and/or computer-readable media disclosed herein. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure describes systems, apparatuses, and methods of processing performance data of devices of a content delivery network to identify a performance metric associated with devices of the content delivery network that are within a particular geographic region. The performance metric may indicate a service issue with the devices. A location associated with the performance metric may be identified, and a display may be generated that visually represents the performance metric, at least one component of the content delivery network, and geographic data representing the particular geographic region. The display may provide useful information for a technician to address the performance metric (e.g., to address the service issue). 
     To illustrate, a technician may input a search query into a mobile network analysis device. The search query may include an address (e.g., a customer address) and a time period. The mobile network analysis device may access a database (or a network analyzer that accesses the database) based on the search query and may retrieve performance data measured during the time period and associated with a device at the address, such as a residential gateway. The database may store performance data that is measured periodically (or continually) from the devices of the content delivery network. In addition to the performance data for the device associated with the address, performance data may be retrieved for devices associated with nearby addresses within a geographic region that includes the address. The performance data for the devices may indicate counts of error corrections (e.g., forward error corrections), counts of re-initializations, counts of code violations, counts of error seconds, counts of unavailable seconds, other performance data, or a combination thereof. 
     Based on the performance data, a performance metric is determined. The performance metric may be determined by analyzing the performance data to identify measurement values that exceed (or fail to exceed) a threshold, measurement values that are statistical outliers compared to the rest of the measurements for the geographic region, or a combination thereof. After determining the performance metric, the mobile network analysis device may generate a display that visually represents geographic data representing the particular geographic region (e.g., the device may display a satellite photo of the particular geographic region), at least one component of the content delivery network within the particular geographic region (e.g., a network cable, a device, etc.), and the performance metric. For example, the display may depict an image of the particular geographic region (e.g., a satellite image, an image of a map, etc.), a graphical illustration of network cables and connections (between cables, devices, or both), and values of the performance metric for each device (or a subset of devices) in the geographic region. In addition, visual icons (e.g., indicators), such as pins, may be depicted at locations of devices that may be experiencing a service issue. To further illustrate, devices having measurement values that exceed (or fail to exceed) a threshold, or measurement values that represent statistical outliers as compared to measurement values of the other devices, may be represented by pins in the display. Different color pins may be used to indicate different types of potential service issues. For example, a first color pin may represent devices having a number of re-initializations that exceeds a re-initialization threshold for the time period, and a second color pin may represent devices having a number of error corrections that are outliers as compared to the other devices of the geographic regions. 
     Displaying the geographic data, the at least one component, and the performance metric(s) may enable a technician to efficiently react to a service issue without spending a large amount of time analyzing performance data from a database. The database may be configured to store performance data associated with thousands or more devices over multiple months, and manually analyzing this performance data (of the entire database) to identify performance metrics that indicate service issues may be inefficient. The visual display of the geographic data, the at least one component, and the performance metrics may enable a technician to quickly and efficiently identify a potential service issue. Additionally, the technician may be able to enter a particular address for which a service call has been received, and based on the information presented in the display, the technician may identify whether a potential service issue is specific to the particular address or whether other devices in the geographic region may be experiencing the service issue. To illustrate, the technician may identify multiple devices of the content delivery network that are exhibiting similar performance metrics. Based on a display of components, such as network cables, of the content delivery network, the technician may determine additional information related to each of the devices that is experiencing the similar performance metrics. For example, the technician may realize that the devices that are experiencing the similar performance metrics are coupled to a particular network cable. Using this information, the technician may determine that the same service issue is likely effecting the network cable, or an upstream component from the network cable, and not just the device at the particular address. The technician may focus efforts to address the service issue to the network cable (or the upstream component) instead of just to the device at the particular address. Thus, the present disclosure enables a technician to identify correlations in performance metrics (that may indicate a service issue) and network connections within a geographic region that would otherwise be difficult to identify. 
     In a particular aspect, a method includes accessing a database to retrieve performance data associated with a content delivery network. The method includes determining, based on the performance data, a performance metric for components of the content delivery network that are within a particular geographic region. The method further includes generating a display that visually represents geographic data representing the particular geographic region, at least one component of the content delivery network within the particular geographic region, and the performance metric. The communications network  125  includes a plurality of network elements (NE)  150 ,  152 ,  154 ,  156 , etc. for facilitating the broadband access  110 , wireless access  120 , voice access  130 , media access  140  and/or the distribution of content from content sources  175 . The communications network  125  can include a circuit switched or packet switched network, a voice over Internet protocol (VoIP) network, Internet protocol (IP) network, a cable network, a passive or active optical network, a 4G, 5G, or higher generation wireless access network, WIMAX network, UltraWideband network, personal area network or other wireless access network, a broadcast satellite network and/or other communications network. 
     In another particular aspect, an apparatus includes a processor, a display device, and a memory that is accessible to the processor. The memory includes instructions executable by the processor to perform operations. The operations include accessing a database to retrieve performance data associated with a content delivery network. The operations include determining, based on the performance data, a performance metric for components of the content delivery network that are within a particular geographic region. The operations further include generating, at the display device, a display that visually represents geographic data representing the particular geographic region, at least one component of the content delivery network within the particular geographic region, and the performance metric. In various embodiments, the base station or access point  122  can include a 4G, 5G, or higher generation base station, an access point that operates via an 802.11 standard such as 802.11n, 802.11ac or other wireless access terminal. The mobile devices  124  can include mobile phones, e-readers, tablets, phablets, wireless modems, and/or other mobile computing devices. 
     In another particular aspect, a computer-readable storage device stores instructions that, when executed by a processor, cause the processor perform operations. The operations include obtaining performance data associated with a content delivery network. The operations include determining, based on the performance data, a performance metric for components of the content delivery network that are within a particular geographic region. The operations further include generating a display that visually represents geographic data representing the particular geographic region, at least one component of the content delivery network within the particular geographic region, and the performance metric. In various embodiments, the media terminal  142  can include a cable head-end or other TV head-end, a satellite receiver, gateway or other media terminal  142 . The display devices  144  can include televisions with or without a set top box, personal computers and/or other display devices. 
     Referring to  FIG. 1 , a block diagram of a particular implementation of a system for analyzing performance data and generating a display based on a performance metric is shown and generally designated  100 . The system  100  includes a database  102 , a network analyzer  104 , a mobile network analysis device  108 , a content server  114 , a first node  116 , a second node  118 , a first residential gateway (RG)  120 , a second RG  122 , a third RG  124 , a fourth RG  126 , and a fifth RG  128 . A content delivery network  112  may include the content server  114 , the first node  116 , the second node  118 , the RGs  120 - 128 , and/or other components. The content delivery network  112  may be any network that is operable to provide (e.g., transmit or propagate) media content from at least one component to at least one other component. As non-limiting examples, the content delivery network  112  may include or correspond to a cable network, a broadcast television network, an internet protocol (IP) network, a satellite network, a cellular network, a long-term evolution (LTE) network, or other networks. In  FIG. 1 , media content may be provided from the content server  114  via the content delivery network  112  to components of the content delivery network  112 , such as the first node  116 , the second node  118 , and the RGs  120 - 128 . In various embodiments, the communications network  125  can include wired, optical and/or wireless links and the network elements  150 ,  152 ,  154 ,  156 , etc. can include service switching points, signal transfer points, service control points, network gateways, media distribution hubs, servers, firewalls, routers, edge devices, switches and other network nodes for routing and controlling communications traffic over wired, optical and wireless links as part of the Internet and other public networks as well as one or more private networks, for managing subscriber access, for billing and network management and for supporting other network functions. 
     For example, media content may be received from the content delivery network  112  at the first node  116 , and the first node  116  may provide the media content to the first RG  120 , the second RG  122 , the third RG  124 , or a combination thereof. As another example, media content may be received from the content delivery network  112  at the second node  118 , and the second node  118  may provide the media content to the fourth RG  126 , the fifth RG  128 , or both. Each of the RGs  120 - 128  may be configured to provide media content to devices within a corresponding residence. As a non-limiting example, the first RG  120  may be configured to provide media content to media playback devices within a first residence, such as a set-top box device, a cable box, a television, a digital video recorder (DVR), a DVD player, a Blu-ray player, a television, a mobile device (e.g., a wireless telephone, a tablet computer, a laptop computer, a computerized watch, etc.), a desktop computer, or other devices that are configured to receive and to display media content. Although particular components of the content delivery network  112  are illustrated, the content delivery network  112  is not so limited. In other implementations, the content delivery network  112  may include additional components (that are not illustrated in  FIG. 1 ), such as servers, routers, nodes, gateways, switches, head-end components, wired components, wireless components, upstream components, downstream components, other devices, or a combination thereof. Additionally or alternatively, the content delivery network  112  may include fewer or more components than illustrated in  FIG. 1 . 
     In the particular implementation of  FIG. 1 , components of the content delivery network  112  may be located within different geographic locations. To illustrate, the first node  116 , the first RG  120 , the second RG  122 , and the third RG  124  may be located within a geographic region  170 . The geographic region  170  may represent a neighborhood, a town, a city, a county, a state, a multi-state area, or a different size region. In a particular implementation, the geographic region  170  corresponds to a neighborhood. Additionally or alternatively, the geographic region  170  may be defined based on a configuration of the content delivery network  112 . For example, the geographic region  170  may represent a region containing components that are connected to a particular portion or element (e.g., a particular network cable or wire, a particular device, etc.) of the content delivery network  112 . In the implementation illustrated in  FIG. 1 , the second node  118 , the fourth RG  126 , and the fifth RG  128  may be located within a different geographic region than the geographic region  170 . 
     The components (e.g., devices) of the content delivery network  112  may be configured to generate various performance measurements on a periodic or continuous basis. The performance measurements for the components may be referred to as performance data  130 . The performance data  130  may include measurements indicative of performance of the components of the content delivery network  112 , such as the RGs  120 - 128 , the nodes  116  and  118 , or other components or devices. For example, the performance data  130  may indicate a count of error corrections, such as forward error corrections (FECs), a count of re-initializations, a count of code violations, a count of error seconds, a count of unavailable seconds, other measurements, or a combination thereof, associated with the components of the content delivery network  112 . In a particular implementation, the performance data  130  includes measurements associated with hundreds, thousands, or millions of components, and the performance data  130  may be too voluminous to be analyzed manually by a technician. As used herein, performing an FEC refers to a component of the content delivery network  112  determining that an error has occurred with receipt of a packet of media content, and, in response to the error, retrieving at least one copy of the packet from a media stream to perform an error correction operation. As used herein, re-initializations refer to a component of the content delivery network  112  performing a re-initialization operation (e.g., a reboot operation). 
     The components of the content delivery network  112  may be configured to provide the performance data  130  via the content delivery network  112  to the network analyzer  104 . In a particular implementation, the network analyzer  104  stores the performance data  130  in the database  102 . Alternatively, another component may store the performance data  130  in the database  102 , or the performance data  130  may be provided directly to the database  102 . The database  102  may be accessible to the network analyzer  104  and may be configured to store the performance data  130  and to provide the performance data  130 , or a portion thereof, based on a search query, as further described herein. In a particular implementation, because the components of the content delivery network  112  are periodically (or continuously) providing new performance measurements, the database  102  may be periodically erased or overwritten with new performance data. In another particular implementation, the performance data  130  in the database  102  may be overwritten as new performance measurements are received (e.g., in a first-in, first-out (FIFO) manner). Thus, the database  102  may be configured to temporarily store the performance data  130 . Although the performance data  130  is described herein as being stored (at least temporarily) in the database  102 , in an alternate implementation, the performance data  130  may be processed by the network analyzer  104  as the performance data  130  is received, and the processed performance data may be used as described further herein. 
     The network analyzer  104  may be configured to process the performance data  130  and to communicate via a network  110  with the mobile network analysis device  108  to initiate generation of a display based on the performance data  130 , as further described herein. The network analyzer  104  may include a processor (or multiple processors) and a memory that is accessible to the processor and that stores instructions that are executable by the processor to perform the various operations described herein. The network analyzer  104  may also include other components, such as an interface, a transmitter, a receiver, a transceiver, or a combination thereof, that enables the network analyzer  104  to send and receive data via the content delivery network  112  and the network  110 . 
     The mobile network analysis device  108  may be carried by a technician and may be configured to provide information related to the content delivery network  112 . The mobile network analysis device  108  may be a mobile device, such as a mobile computing device, a mobile telephone, a tablet computer, a laptop computer, or another computerized device that is able to communicate wirelessly with the network analyzer  104  via the network  110 . Additionally or alternatively, the mobile network analysis device  108  may be a computer, such as a desktop computer. In other implementations, the mobile network analysis device  108  may be mounted within a vehicle or integrated within a component of a vehicle, such as a control unit or a navigation system. In other implementations, the system  100  may include a device, such as a computer, a vehicle-mounted device, or a device integrated within a component of a vehicle, that is configured to perform the operations of the mobile network analysis device  108 . The mobile network analysis device  108  may include a processor (or multiple processors) and a memory that is accessible to the processor and that stores instructions that are executable by the processor to perform the various operations described herein. The mobile network analysis device  108  may also include other components, such as an interface, a transmitter, a receiver, a transceiver, or a combination thereof, that enables the network analyzer  104  to send and receive data via the network  110 . 
     The network analyzer  104  and the mobile network analysis device  108  may be configured to receive input information from the technician and to display information related to the content delivery network  112  based on the input information. To illustrate, the technician may be aware that a service call (or other indicator of a service issue) has been received for a particular address. The technician may use the mobile network analysis device  108  to determine performance metrics associated with a component located at the particular address, as well as performance metrics for nearby components. The mobile network analysis device  108  may be configured to receive a user input from the technician indicating an address  152  and a time period  154 . For example, the mobile network analysis device  108  may include a keypad, a touch screen, or another user input device, and the technician may input a user input that includes the address  152  and the time period  154 . The address  152  and the time period  154  may be selected and adjusted via a user interface, as further described with reference to  FIG. 3 . 
     The mobile network analysis device  108  may generate a search query  150  that indicates the address  152  and the time period  154 , and the mobile network analysis device  108  may transmit the search query  150  to the network analyzer  104  via the network  110 . In some implementations, the user input may include additional information or alternative information, such as a particular performance metric, a particular geographic region to analyze, an analysis parameter, other information, or a combination thereof, and the additional information or alternative information may also be indicated by the search query  150 . In a particular implementation, the search query  150  indicates a particular event (e.g., a service call, a re-initialization, a particular metric exceeding (or failing to satisfy) a threshold, a particular metric being a statistical outlier, etc.), a particular location (e.g., an address of a customer), a particular component of the content delivery network  112  (e.g., a RG of a customer, a network node, a particular connection between a network cable and a device, etc.), a particular grouping of components of the content delivery network  112  (e.g., a neighborhood, a grouping based on a network configuration, a city, a region, etc.), a component type (e.g., nodes, residential gateways, head-ends, etc.), a time period (e.g., the time period  154 ), or a combination thereof. 
     The network analyzer  104  may be configured to receive the search query  150  and to access the database  102  based on the search query  150 . For example, the network analyzer  104  may retrieve a portion of the performance data  130  based on the search query  150 . To illustrate, the network analyzer  104  may access the database  102  using a Structured Query Language (SQL) query or another type of database query. The network analyzer  104  may retrieve a portion of the performance data  130  based on the search query  150 . For example, the network analyzer  104  may retrieve a portion of the performance data  130  that includes data for a component (or components) associated with the address  152 . The retrieved performance data may also be associated with the time period  154  (e.g., the network analyzer  104  may retrieve performance measurements that have been generated during the time period  154 ). 
     Additionally, the network analyzer  104  may determine additional addresses within a geographic region based on a distance to the address or a logical grouping of components of the content delivery network  112 . For example, the network analyzer  104  may determine additional addresses within a particular proximity (e.g., distance) of the address  152 . As another example, the network analyzer  104  may determine additional addresses on the same street as the address  152 , in the same residential area as the address  152 , in the same town or city as the address  152 , in the same county as the address  152 , in the same state as the address  152 , or based on some other geographic or political region that includes the address  152 . As another example, the network analyzer may determine additional addresses that are associated with a common logical grouping of components of the content delivery network  112 . A logical grouping of components may refer to a plurality of components that are connected to a common component of the content delivery network  112 , such as a network cable (or a portion of a network cable), a node, another upstream component, or some other component, or to a grouping of components that are commonly identified in data that represents the content delivery network  112 . The portion of the performance data  130  retrieved by the network analyzer  104  may include data associated with the additional addresses within the geographic region. For example, the network analyzer  104  may determine components that are located nearby (e.g., within a short distance) to the address  152 , and the network analyzer  104  may retrieve a portion of the performance data  130  that is associated with the nearby components in addition to the portion of the performance data  130  that is associated with the component located at the address  152 . 
     If the search query  150  includes additional parameters, the performance data  130  may be filtered based on the additional parameters. For example, if the search query  150  indicates a particular measurement, the network analyzer  104  may filter the retrieved portion of the performance data  130  by discarding data that is not associated with the particular measurement. As another example, if the search query  150  indicates a particular neighborhood (or other geographic region), the portion of the performance data  130  that is retrieved includes data for components located within the neighborhood. Additional details regarding the search query  150  and retrieving the performance data  130  are described with reference to  FIG. 3 . 
     The network analyzer  104  may determine, based on the retrieved portion of the performance data  130 , a performance metric  132  (or performance metrics) for components of the content delivery network  112  that are within a particular geographic region that includes the address  152 . The performance metric  132  may include a measurement or measurements, such as one or more of the measurements represented by the performance data  130 . Additionally or alternatively, the retrieved portion of the performance data  130  may be processed to generate additional metrics, and the performance metric  132  may include the additional metrics. The one or more measurements may be indicative of a service issue within the particular geographic region. 
     The network analyzer  104  may identify a service issue within the particular geographic region based on a statistical analysis of portions of the performance data  130 . To illustrate, the network analyzer  104  may be configured to perform a statistical analysis on the retrieved portion of the performance data  130  to determine the performance metric  132 . For example, the network analyzer  104  may analyze the retrieved portion of the performance data  130  to determine whether a measurement for the component associated with the address  152  indicates that the component may be experiencing a service issue, and the network analyzer  104  may use the measurement as the performance metric  132 . As a particular example, the performance metric  132  may be one of the measurements within the portion of the performance data  130  that has a particular value, such as a value that exceeds (or fails to exceed) a threshold, a value that is a statistical outlier as compared to values associated with other components located within the particular geographic region, or another value indicative of a service issue. 
     The network analyzer  104  may also generate weighted performance data  134  by weighting the data associated with the additional addresses (e.g., addresses that are located within a threshold distance of the address  152 , as one example) based on a proximity of the additional addresses to the address  152 . For example, a first subset of components may be located on the same street as the address  152 , a second subset of components may be located on other streets, and the network analyzer  104  may weight first data associated with the first subset higher than second data associated with the second subset based on the proximity of the first subset to the address  152 . Data for other components may be weighted based on a distance between the address  152  and the corresponding locations of the other components. As another example, a third subset of components may be connected to the same portion of a network cable (or wire) as the component associated with the address  152 , a fourth subset of components may be connected to other portions of the network cable, and the network analyzer  104  may weight third data associated with the third subset higher than fourth data associated with the fourth subset. 
     The network analyzer  104  may analyze the weighted performance data  134  to generate the performance metric  132 . As one example, the network analyzer  104  may identify that an average count of re-initializations for components of the particular geographic region (e.g., a geographic region that includes the address  152 ) during the time period  154  is potentially indicative of a service issue, and the network analyzer  104  may weight re-initialization counts for components that are nearby to the particular component higher than re-initialization counts for components that are farther away from the particular component. If the average count of re-initializations, which is determined based on the weighted values, exceeds a particular value, the network analyzer  104  may identify the average count of re-initializations as indicative of a service issue, and the performance metric  132  may include the average count of re-initializations. In other implementations, other measurements may be weighted and processed to generate the performance metric  132 . 
     Performing the statistical analysis on the retrieved portion of the performance data  130  (or the weighted performance data  134 ) may include performing a “threshold analysis.” To illustrate, the network analyzer  104  may store thresholds corresponding to measurements included within the performance data  130 . The network analyzer  104  may analyze the retrieved portion of the performance data  130  (or the weighted performance data  134 ) to determine whether any of the measurements satisfy a corresponding threshold. In a particular implementation, the network analyzer  104  determines whether any of the measurements exceeds a corresponding threshold, and the performance metric  132  may indicate the measurements that exceed the corresponding threshold. To illustrate, a threshold associated with re-initializations may be ten during a particular time frame, and the network analyzer  104  may identify any components within the particular geographic region that have performed more than ten re-initializations during the particular time frame. In an alternate implementation, the network analyzer  104  determines whether any of the measurements fails to exceed a corresponding threshold. In this implementation, the performance metric  132  may indicate (e.g., represent) the measurements that fail to exceed the corresponding threshold. In some implementations, the thresholds may be related to a duration of the time period  154 . As an example, if the time period  154  is an hour, the threshold associated with re-initializations may be two, and if the time period  154  is a day, the threshold associated with re-initializations may be ten. In other examples, the thresholds may have other values depending on the duration of the time period  154 . 
     Performing the statistical analysis on the retrieved portion of the performance data  130  may also include performing an “outlier analysis.” To illustrate, the network analyzer  104  may analyze the retrieved portion of the performance data  130  to identify measurements having values that are statistical outliers as compared to other values within the retrieved portion of the performance data  130 . In a particular example, the retrieved portion of the performance data  130  may indicate that a count of error corrections for most of the components within the particular geographic region is within a range from one to five error corrections. In this example, at least one component may have a count of error corrections that is within a range of fifteen to twenty-five error corrections, which represents a statistical outlier as compared to the values associated with the other components within the particular geographic region. Because the network analyzer  104  determines that a count of error corrections represents at least one statistical outlier, the performance metric  132  may include the count of error corrections for the components of the particular geographic region. 
     In a particular implementation, determining the performance metric  132  may include analyzing the retrieved portion of the performance data  130  (or the weighted performance data  134 ) to identify time-correlated events. The performance metric  132  may indicate a number of occurrences of an event at a subset of components, times at which the event occurred, or a combination thereof. The event may include a service call, a customer request, a performance measurement that satisfies a threshold, a performance measurement that is a statistical outlier as compared to performance measurements of other components within a particular geographic region, or a combination thereof. The network analyzer  104  may be configured to identify measurements that are time-correlated. 
     For example, the network analyzer  104  may identify a first component within the particular geographic region that experiences a particular event based on the retrieved portion of the performance data  130 . The event may be related to a performance measurement, such as a re-initialization by a component, or the event may be more significant, such as a service call that is associated with the component. The number of occurrences of the event may be determined by the network analyzer  104  for a specified time period, such as the time period  154 . The network analyzer  104  may identify a similar event that occurs at a nearby component within a short time frame. For example, the network analyzer  104  may identify a re-initialization that is performed at another component two seconds later, as a non-limiting example. The network analyzer  104  may determine that the two events are related, and the performance metric  132  may indicate the time-correlated events (e.g., the re-initializations that occur at nearby components in a short time frame). In some implementations, the performance metric  132  may include information indicating a temporal relationship between the events (e.g., a duration of time between when the events occurred, which event occurred first, etc.). As another example, the network analyzer  104  may be configured to identify simultaneous (or nearly simultaneous) occurrence of a particular type of event at multiple devices. To illustrate, the network analyzer  104  may identify three RGs within the particular geographic region that concurrently perform re-initializations, or that perform re-initializations within a short time frame. 
     The network analyzer  104  may also be configured to process the retrieved portion of the performance data  130  (or the weighted performance data  134 ) to identify a performance trend associated with the retrieved portion of the performance data  130 . For example, the network analyzer  104  may determine whether an event is occurring with a relatively stable (e.g., constant) frequency during the time period  154 , or whether a frequency of occurrence of the event is changing (e.g., increasing or decreasing). If the network analyzer  104  identifies a performance trend (e.g., a statistically significant change in a frequency of occurrence of an event), the performance metric  132  may include the measurement subject to the performance trend. For example, if the number of error corrections performed at a particular component is increasing during the time period  154 , the performance metric  132  may indicate a rate of change of the number of error corrections. Identifying performance trends may indicate when a service issue originated. For example, if a particular component begins performing error corrections with an increasing frequency at a particular time, the network analyzer  104  may determine that a service issue is being experienced at the particular component at the particular time. 
     Additionally, the network analyzer  104  may be configured to determine whether processing of the retrieved portion of the performance data  130  (or the weighted performance data  134 ) indicates that a service issue is being experienced by the components that are located within the particular geographic region. Based on the determination, the network analyzer  104  may determine where (or whether) to store the retrieved portion of the performance data  130  (or the weighted performance data  134 ). To illustrate, the network analyzer  104  may store the retrieved portion of the performance data  130  (or the weighted performance data  134 ) in long-term storage  136  when the processing of the performance data  130  indicates that a service issue is being experienced by the components that are located within the particular geographic region. The long-term storage  136  may be a memory location that is designated to store data without being periodically erased or overwritten. Additionally or alternatively, the long-term storage  136  may be configured to store data for a long period of time. As one example, the long-term storage  136  may include a non-volatile memory. In a particular implementation illustrated in  FIG. 1 , the long-term storage  136  is within the network analyzer  104  (e.g., within a memory of the network analyzer  104 ). Alternatively, the long-term storage  136  may be a memory location that is external to and accessible to the network analyzer  104 . 
     The network analyzer  104  may store the retrieved portion of the performance data  130  (or the weighted performance data  134 ) in temporary storage  138  when the processing of the performance data  130  fails to indicate that a service issue is being experienced by the components that are located within the particular geographic region. The temporary storage  138  may be a memory location that is designated to temporarily store data until the data is periodically overwritten. In a particular implementation illustrated in  FIG. 1 , the temporary storage  138  is within the network analyzer  104  (e.g., within a memory of the network analyzer  104 ). Alternatively, the temporary storage  138  may be a memory location that is external to the network analyzer  104 . In a particular implementation, the temporary storage  138  may be included in (or part of) the database  102 , and the database  102  may be periodically overwritten with updated performance data from the components of the content delivery network  112 . In an alternate implementation, the network analyzer  104  may discard the retrieved portion of the performance data  130  when the processing of the performance data  130  fails to indicate that a service issue is being experienced by the components that are located within the particular geographic region. 
     In a particular implementation, the network analyzer  104  may be configured to receive the performance data  130  from the components of the content delivery network  112  and to process the performance data  130  as it is received instead of accessing the database  102  to retrieve the performance data  130 . In this implementation, the network analyzer  104  may process the performance data  130  to identify that performance metric  132  without receiving the search query  150 . Instead, the network analyzer  104  may be configured to process the performance data  130  on a region-by-region basis, such as a neighborhood-by-neighborhood basis, to identify the performance metric  132  that is indicative of a service issue. If the network analyzer  104  identifies a service issue, the network analyzer  104  may generate and transmit the message  140  to the mobile network analysis device  108  for use in initiating the display  160 . Processing the performance data  130  on a region-by-region basis may be more time and resource intensive than retrieving a portion of the performance data  130  based on the search query  150 . However, processing the performance data  130  on a region-by-region basis may enable the network analyzer  104  to identify service issues that would otherwise be missed (e.g., if a technician does not enter an address within a particular geographic region to trigger processing of the performance data for the particular geographic region). 
     After generating the performance metric  132 , the network analyzer  104  may generate a message  140  that includes the performance metric  132 , geographic data  142 , and network data  144 . The network analyzer  104  may transmit the message  140  to the mobile network analysis device  108  via the network  110 . The network  110  may include a wired network, a wireless network, a packet-switched network, a cable network, a cellular telephone network, a satellite network, a client-server network, a peer-to-peer or ad hoc network, another type of network or a combination thereof. 
     The geographic data  142  represents the particular geographic region. For example, the geographic data  142  may include image data of a satellite photo of the particular geographic region, image data of a street map of the particular geographic region, other image data, or a combination thereof. The geographic data  142  may also include other data, such as addresses of locations and global positioning satellite (GPS) coordinates of locations, as non-limiting examples. 
     The network data  144  indicates information about components of the content delivery network  112  within the particular geographic region. The network data  144  may indicate locations of network cables (or wires) of the content delivery network  112  within the particular geographic region, locations of other components (e.g., RGs, nodes, hubs, wireless transmitters, taps, etc.), interconnections between the components of the content delivery network  112 , or a combination thereof. For example, the network data  144  may indicate locations of network cables within the particular region, taps or other interconnections between customer components (e.g., RGs, routers, etc.) and the network cables, network nodes, interconnections between the network nodes and the network cables, other components, and interconnections between the other components and the network cables. In a particular implementation, the network data  144  indicates whether the network cables and buried or above-ground. Information about the network cable type (e.g., buried or above-ground) may be used in generating a display, as further described herein. 
     The geographic data  142  and the network data  144  may be stored at, or may be accessible to, the network analyzer  104 , and the network analyzer  104  may be configured to select the geographic data  142  and the network data  144  that are related to the particular geographic region based on the search query  150 . For example, the network analyzer  104  may determine the particular geographic region that includes the address  152 , and the network analyzer  104  may retrieve the geographic data  142  and the network data  144  that are associated with the particular geographic region. After retrieving the geographic data  142  and the network data  144 , the network analyzer  104  may include the geographic data  142  and the network data  144  in the message  140  with the performance metric  132 . 
     The mobile network analysis device  108  may be configured to receive the message  140  from the network analyzer  104  and to generate a display  160  based on the message  140 . The mobile network analysis device  108  may include a display device (e.g., a screen, a touch screen, or another display device that is integrated within or coupled to the mobile network analysis device  108 ) that is configured to display information, including information based on the message  140 . The mobile network analysis device  108  may be configured to generate the display  160  based on the message  140  and initiate the display  160  at the display device. 
     To illustrate, the mobile network analysis device  108  may generate the display  160  that visually represents the geographic data  142 , at least one component  162  of the content delivery network  112  (based on the network data  144 ), and the performance metric  132 . The display  160  may visually represent the geographic data  142  by displaying an image or rendering of the particular geographic region. For example, the display  160  may depict a visual representation of buildings, streets, landmarks, terrain, other visual representations of geographic data, or a combination thereof. In a particular implementation, the visual representation of the geographic data  142  may be an image of the particular region or a satellite image (e.g., photo) of the particular region. Additionally or alternatively, the display  160  may depict information, such as addresses, GPS coordinates, street names, street addresses, zip codes, city names, county names, state names, other geographic information, or a combination thereof, based on the geographic data  142 . 
     The display  160  may visually represent the network data  144  by displaying a visual representation of the at least one component  162  of the content delivery network  112  within the particular geographic region. The at least one component  162  may include a network cable a residential gateway, a node, a hub, a wireless transmitter, a tap, an interconnection between components (or between components and network cables), another component, or a combination thereof. For example, as illustrated in  FIG. 1 , the display  160  may depict a visual representation of various network cables of the content delivery network  112  that are located within the particular geographic region. In some implementations, the display  160  may indicate a type of network cable. For example, above-ground network cables may be represented differently than buried network cables, as further described with reference to  FIG. 2 . Additionally or alternatively, the display  160  may depict information, such as network cable or component names, labels, GPS coordinates, other information, or a combination thereof. 
     The display  160  may visually represent the performance metric  132  by displaying values  164  of the performance metric  132  for the components of the content delivery network  112  (or for a subset of the components) that are located within the particular geographic region. For example, a value  164  of a performance measurement (or values of multiple performance measurements) may be displayed adjacent to the locations of the components of the content delivery network  112  in the display  160 . In a particular implementation, the performance metric  132  indicates a number of occurrences of an event (e.g., forward error corrections (FECs) in the example illustrated in  FIG. 1 ) at a subset of the components, and the number of occurrences (e.g., the values  164 ) for each component of the subset is overlaid on the geographic data  142  at a location of the corresponding component. Additionally, in some implementations, the performance metric  132  may be visually represented by an icon (e.g., an indicator) that is overlaid on the geographic data  142  at locations corresponding to a set of components associated with time-correlated events. In the example illustrated in  FIG. 1 , the values  164  corresponding to two components (e.g., fifteen FECs and seventeen FECs) may exceed a threshold (e.g., ten FECs) during the time period  154 , and thus the performance metric  132  indicates a time-correlated event for the two components. In this example, a first icon  166  and a second icon  168  may be overlaid at locations of the two components, and no icon may be overlaid at a third component (e.g., a component having the value  164  of two FECs, which fails to exceed the threshold). 
     I n a particular implementation, the display  160  visually represents a combination of the geographic data  142 , the at least one component  162 , and the performance metric  132 . For example, the visual representation of the at least one component  162  may be an icon representing a particular component, such as a component associated with the address  152 , and the performance metric  132  may be displayed near the visual representation of the at least one component  162 . Other performance metrics corresponding to other components may be displayed near identifiers of the other components. Additionally, geographic information, such as a city name, a county name, a neighborhood name, addresses, GPS coordinates, or other information based on the geographic data  142  may be displayed. Alternatively, an image or a map based on the geographic data  142  may be displayed. The various information (e.g., the geographic information, the visual representation of the at least one component  162 , and the performance metric  132 ) may be displayed in separate portions of the display  160 . Alternatively, one or more of the visual representations of the geographic data  142 , the at least one component  162 , and the performance metric  132  may be combined in a single portion of the display  160 . 
     In another particular implementation, the visual representations of the at least one component  162  and the performance metric  132  may be overlaid on the visual representation of the geographic data  142 . As illustrated in  FIG. 1 , the visual representation of the at least one component  162  (e.g., the network cables) and the values  164  of the performance metric  132  are overlaid on the visual representation of the geographic data  142  (e.g., overlaid on the map illustrated in  FIG. 1 ). By providing information related to a configuration of the content delivery network  112  (e.g., a visual representation of the network data  144 ) and the performance metric  132  overlaid on a visual representation of the geographic data  142 , the display  160  may enable a technician to quickly understand and to determine how to address a service issue for components within the particular geographic region. 
     In a particular implementation, the network analyzer  104  or another computing device may be configured to perform some or all of the operations of the mobile network analysis device  108 . For example, the network analyzer  104  may include a user input device that is configured to receive user inputs to generate a search query to the database  102 . Additionally, the network analyzer  104  may include (or be coupled to) a display device that is configured to generate the display  160 , as described above. Thus, in some implementations, any of the operations described with reference to the mobile network analysis device  108  may also or in the alternative be performed by the network analyzer  104 . In these implementations, generation and transmission of the message  140  may be omitted. In another particular implementation, the mobile network analysis device  108  may be configured to perform the operations of the network analyzer  104 . In this implementation, the mobile network analysis device  108  is configured to access the database  102  and to process the retrieved portion of the performance data  130  to generate the performance metric  132 , and the network analyzer  104  may be omitted from the system  100 . 
     During operation, a technician may generate a search query. For example, the technician may enter the address  152  and the time period  154  into the mobile network analysis device  108  via the user input device. The address  152  may correspond to an address of the first RG  120 , and the technician may wish to see if a service issue that is being experienced by the first RG  120  is being experienced by other components within the geographic region  170  (e.g., a neighborhood that includes the address  152 ). The mobile network analysis device  108  may generate the search query  150  that indicates the address  152  and the time period  154 , and the mobile network analysis device  108  may transmit the search query  150  to the network analyzer  104  via the network  110 . 
     The network analyzer  104  may receive the search query  150  and may access the database  102  based on the search query  150  to retrieve a portion of the performance data  130 . For example, the network analyzer  104  may retrieve a portion of the performance data  130  that corresponds to the first RG  120  and to other components (e.g., the second RG  122 , the third RG  124 , and the first node  116 ) that are located within the geographic region  170 . The retrieved portion of the performance data  130  may include measurements generated during the time period  154 . In a particular implementation, the search query  150  may include additional parameters (as further described with reference to  FIG. 3 ), and the portion of the performance data  130  may be retrieved and filtered based on the additional parameters. 
     The network analyzer  104  may process the received portion of the performance data  130  to determine the performance metric  132 . In a particular implementation, the network analyzer  104  may generate the weighted performance data  134  by weighting the retrieved portion of the performance data  130  based on proximity of the corresponding components to the address  152 , and the weighted performance data  134  may be processed to generate the performance metric  132 . The performance metric  132  may include measurements that exceed (or fail to exceed) a threshold, measurements representing statistical outliers, or a combination thereof. Additionally or alternatively, the performance metric  132  may indicate time-correlated events or a performance trend. The network analyzer  104  may store the retrieved portion of the performance data  130  (or the weighted performance data  134 ) in the long-term storage  136  if the processing of the retrieved portion of the performance data  130  indicates that a service issue is being experienced by components within the geographic region  170 , and the network analyzer  104  may store the retrieved portion of the performance data  130  (or the weighted performance data  134 ) in the temporary storage  138  if the processing of the retrieved portion of the performance data  130  fails to indicate that a service issue is being experienced by components within the geographic region  170 . In an alternate implementation, instead of storing the retrieved portion of the performance data  130  in the temporary storage  138 , the retrieved portion of the performance data  130  may be discarded. 
     After determining the performance metric  132 , the network analyzer  104  may generate the message  140  that includes the performance metric  132 , the geographic data  142 , and the network data  144 . The geographic data  142  and the network data  144  may correspond to the geographic region  170 . The network analyzer  104  may transmit the message  140  to the mobile network analysis device  108  via the network  110 . In an alternate implementation, the network analyzer  104  may indicate locations of the performance metric  132 , the geographic data  142 , and the network data  144  in the message  140 . In response to receiving the message  140 , the mobile network analysis device  108  may retrieve the performance metric  132 , the geographic data  142 , and the network data  144  for use in generating the display  160 . As a particular example, the mobile network analysis device  108  may retrieve the performance metric  132  from the database  102  (or from long-term storage  136 ), and the mobile network analysis device  108  may retrieve the geographic data  142  and the network data  144  from other data sources that are external to the mobile network analysis device  108 . 
     The mobile network analysis device  108  may generate the display  160  that visually represents the performance metric  132 , the geographic data  142 , and the network data  144 . In a particular implementation, the display  160  may visually represent a combination of the geographic data  142 , the at least one component  162 , and the performance metric  132 . Alternatively, the performance metric  132  and the at least one component  162  may be overlaid on the visual representation of the geographic data  142 . For example, the display  160  may visually represent a map of the geographic region  170 , and the at least one component  162  (and other information based on the network data  144 ) and the values  164  (based on the performance metric  132 ) may be overlaid on the map. In the example illustrated in  FIG. 1 , locations corresponding to the first RG  120 , the second RG  122 , and the third RG  124  are represented in the display  160 . 
     Thus, the system  100  enables analysis of the performance data  130  to determine the performance metric  132  associated with devices (e.g., components) of the content delivery network  112  within the geographic region  170 . Because the performance data  130  may be weighted and analyzed to determine measurements that exceed (or fail to exceed) thresholds or that are statistical outliers, the performance metric  132  may be indicative of a service issue that likely effects multiple components of the content delivery network  112  within the geographic region  170 . The mobile network analysis device  108  may generate the display  160  that visually represents the geographic data  142 , the network data  144 , and the performance metric  132 . By depicting such information in an easy-to-use visual format, the mobile network analysis device  108  may enable a technician to quickly and easily determine whether a service issue that is effecting the particular component (e.g., the first RG  120 ) located at the address  152  is also likely effecting other components within the geographic region  170 . Using this information, the technician may be able to quickly determine a plan to address the service issue without performing time consuming data analysis of the performance data  130  or performing testing at the address  152 . 
     Referring to  FIG. 2 , a diagram illustrating a particular example of a display that visually represents at least one component of a content delivery network, a performance metric, and geographic data is shown and generally designated  200 . In a particular implementation, the display  200  may correspond to the display  160  that is initiated at the mobile network analysis device  108  and that visually represents the performance metric  132 , the geographic data  142 , and the at least one component  162  of  FIG. 1 . 
     In the implementation illustrated in  FIG. 2 , the performance metrics and the visual representation of at least one component of the content delivery network  112  are overlaid on the geographic data  142 . To illustrate, the display  200  includes a visual representation of a map of a neighborhood (e.g., a particular geographic region). The visual representation includes houses, land plots, streets, intersections, and other geographic information. Additionally, the display  200  may include textual information, such as addresses or GPS coordinates (not shown in  FIG. 2 ). Each of the locations (e.g., the houses) in  FIG. 2  may be associated with a component of the content delivery network  112 , such as a RG, a router, a cable modem, or another component. 
     The performance metrics and the visual representation of the network data  144  may be overlaid on the visual representation of the geographic data  142 . For example, a first set of lines and a second set of lines in  FIG. 2  represent network cables of the content delivery network  112 , and the sets of lines may be displayed on an image of the neighborhood (e.g., the visual representation of the geographic data  142 ). In the example illustrated in  FIG. 2 , a first line type indicates above-ground network cables, and a second line type indicates buried (e.g., underground) network cables. In other implementations, the different types of network cables may be indicated in other ways, such as by using different colors, different formats or styles, different icons, different labels, or some other way of indicating differences between the types of network cables. The network cables may also be labeled. For example, the display  200  depicts an illustrative label  208  (“T-10”) of a particular network cable. In  FIG. 2 , the network cables are labeled T-9 through T-14. The labels may indicate different portions of network cables, and may assist a technician in determining which network cable (or other component) a particular component is connected to. 
     The performance metrics (or a subset of the performance metrics) may also be overlaid on the visual representation of the geographic data  142 . For example, an illustrative first performance metric  202  and an illustrative second performance metric  204  may be displayed on the image (e.g., the visual representation of the geographic data  142 ) at a location corresponding to a particular component. Each of the other components may have a corresponding first performance metric and a corresponding second performance metric, as illustrated in  FIG. 2 . In other implementations, only a subset of the components within the particular neighborhood (e.g., the particular geographic region) may have the performance metrics displayed. For example, performance metrics may not be displayed if the performance metrics are not indicative of a service issue (e.g., performance metrics that fail to exceed a threshold or that are not statistical outliers may not be displayed). In  FIG. 2 , two performance metrics are illustrated for convenience. In other implementations, more than two or fewer than two performance metrics may be illustrated. In a particular implementation, the performance metrics may be interspersed with other information, such as addresses, customer identifiers, network component identifiers, and other information related to the location of the component, the customer, the configuration of the network, or a combination thereof. 
     The performance metrics may indicate a number of occurrences of an event. For example, in  FIG. 2 , the first performance metric  202  is a count of forward error corrections (FECs), and the second performance metric  204  is a count of re-initializations (R-I). In other examples, the performance metrics may indicate a number of occurrences of other events, such as code violations, error seconds, unavailable seconds, service calls, or other events. In addition to the performance metrics, the display  200  may also depict icons at locations that may be experiencing a service issue. For example, the display  200  depicts an illustrative icon  206  at a location that may be experiencing a service issue. In a particular implementation, the icon  206  may be a pin or a pushpin. In other implementations, the icon  206  may be a different type of indicator. In some implementations, a color of the icon  206  may be based on which performance metric is used in the determination that the component at that location is experiencing a service issue. For example, the icon  206  may have a first color if the first performance metric  202  indicates that the corresponding component is experiencing a service issue, and the icon  206  may have a second color if the second performance metric  204  indicates that the corresponding component is experiencing a service issue. 
     I n the example illustrated in  FIG. 2 , the components that are experiencing a service issue may be identified based on whether the count of FECs exceeds a threshold (e.g., fifteen FECs) or whether the count of re-initializations is a statistical outlier. To illustrate, a subset of components in the particular geographic region (e.g., components at the locations associated with the three pushpin icons in  FIG. 2 ) may each have a count of FECs that exceeds the threshold of fifteen FECs. For example, the subset of components may have FECs of eighteen, twenty, and seventeen, which each exceed the threshold of fifteen FECs. The threshold may be set automatically (e.g., by the network analyzer  104  or the mobile network analysis device  108 ), set by a technician, or set based on a default (e.g., preprogrammed) value. Additionally, the subset of components may each have a count of re-initializations that are statistical outliers as compared to other components in  FIG. 2 . For example, the subset of components may have eight, five, and twelve re-initializations, which are statistical outliers as compared to the other components, which each have zero, one, or two re-initializations. In some implementations, the outliers may be determined based on comparisons to measurements associated with other components in the particular geographic region. Because the other components in the particular geographic region may be similarly situated to the component at a particular address, determining an outlier based on the measurements associated with the other components in the geographic region may quickly indicate a service issue that is effecting a subset of components in the geographic region. In other implementations, the outliers may be determined based on comparisons to measurements associated with other components in a larger area. For example, an outlier may be determined when based on a standard deviation determined for all components of the content delivery network  112  (e.g., a nationwide network). Based on the counts of FECs exceeding the threshold, the counts of re-initializations being statistical outliers, or both, the display  200  may depict the icon at locations of the subsets of components. 
     A technician may view the display  200  and may identify components that are likely experiencing the same service issue. To illustrate, if the technician entered an address that corresponds to the location in  FIG. 2  with the illustrative icon  206 , the technician is presented with enough visual information to quickly realize that two other components of the content delivery network  112  are experiencing the same performance metrics, and likely experiencing the same service issue. Additionally, the technician may identify a relationship between the performance metrics (which are indicative of the service issue) and the configuration of the content delivery network  112 . In the example illustrated in  FIG. 2 , because the network data  144  (e.g., the graphical illustrations of the network cables and the labels of the network cables) are displayed with the visual representation of the geographic data  142 , the technician may be able to quickly ascertain that the components that are likely experiencing the same service issue are all connected to a particular network cable (e.g., the T-09 network cable in  FIG. 2 ). Based on this information, the technician may be able to determine a plan to address the service issue, such as performing repairs to the particular network cable (e.g., the T-9 network cable). Thus, the display  200  of  FIG. 2  may enable a technician to quickly determine a plan to address a service issue without performing time-consuming processing of the performance data  130  and without performing time-consuming testing at a particular address or testing on a network card associated with the particular network cable. 
     Referring to  FIG. 3 , a diagram illustrating a particular implementation of a user interface displayed by a network analysis device (such as the mobile network analysis device  108  of  FIG. 1 ) is shown and generally designated  300 . In a particular implementation, the user interface  300  may be generated and displayed via the mobile network analysis device  108  or the network analyzer  104  of  FIG. 1 . The user interface  300  is configured to display information to the technician and to enable the technician to select parameters, to adjust values, and to initiate data processing of performance data. For example, the technician may use a user input device of the mobile network analysis device  108  to select parameters or to adjust values displayed via the user interface  300 . In some implementations, initiating processing of the performance data may cause the mobile network analysis device  108  to generate the display  160  of  FIG. 1  or the display  200  of  FIG. 2 . Additionally, initiating processing of the performance data may cause the mobile network analysis device  108  to generate a report. 
     The user interface  300  may include multiple selectable parameters for use in generating a search query used to retrieve performance data. In the example of  FIG. 3 , the parameters include a location parameter (e.g., “Just the Neighborhoods I Loaded”), a time threshold (e.g., “Limit Data to Last 48 Hours”), an outlier parameter (e.g., “Outlier Threshold: 3 Standard Deviations for Bursty FECs”), a measurement parameter (e.g., “Target Stat Threshold: 0 Limits Applicable Query to Results Above This Value”), and a unit parameter (e.g., “Size of Time Bucket: 5 Hours for Neighborhood Re-Initializations”). The location parameter enables a technician to limit the retrieval and processing of performance data to neighborhoods that are specifically loaded by the technician (e.g., using a different tab of the user interface  300 ). The time threshold enables the technician to limit the retrieval and processing of performance data to performance data that is measured during a particular time period, such as the time period  154  of  FIG. 1 . The outlier parameter enables the technician to select a number of standard deviations used to define statistical outliers. The measurement parameter enables the technician to set a threshold for a particular measurement. The unit parameter enables the technician to select a time unit for use in displaying results of the data processing. Although the example illustrated in  FIG. 3  shows five parameters, in other implementations fewer than five or more than five parameters may be included in the user interface  300 . In the example illustrated in  FIG. 3 , each of the parameters are selectable using a check box, and each value is adjustable using up and down arrows. In other implementations, the parameters may be selected and adjusted using other techniques. 
     Additionally, the user interface  300  may include one or more selectable indicators (e.g., buttons) that enable the technician to select a particular measurement. The retrieved performance data is to be filtered by the particular measurement. In the example illustrated in  FIG. 3 , the user interface  300  includes a selectable indicator for “Bursty FECs,” a selectable indicator for “Neighborhood Re-Initializations,” and a selectable indicator for “Dump All” (e.g., an indicator to export the retrieved performance data into a spreadsheet for by-hand analysis). If the technician selects one of the selectable indicators, the mobile network analysis device  108  may generate a search query (e.g., the search query  150 ) based on the information received via the user interface  300 , and the search query may be used to access the database  102  to retrieve a portion of the performance data  130 , as further described with reference to  FIG. 1 . The retrieved portion of the performance data  130  may be used to generate a display, as further described with reference to  FIGS. 1 and 2 . Additionally or alternatively, the retrieved portion of the performance data  130  may be used to generate a report in accordance with the parameters and values selected using the user interface  300 . Although three selectable indicators are illustrated in  FIG. 3 , in other implementations, the user interface  300  may include more than three or fewer than three selectable indicators, and the selectable indicators may be associated with different options (e.g., different performance measurements). 
     In a particular implementation, the user interface  300  may use other input to generate a search query. For example, a search query may be based on a GPS location of the mobile network analysis device  108  or data provided to a dispatch application that indicates information related to service calls. Additionally or alternatively, the search query may be generated based on other data or other options. In other implementations, the search query may be generated based on more options or fewer options than described with reference to  FIG. 3 . 
     Thus, the user interface  300  of  FIG. 3  enables a technician to quickly and easy generate a search query based on the technician&#39;s needs. For example, the technician may select a particular address associated with a service call, and for a geographic region that includes the particular address, the technician may be provided with a report (and a visual display) of selected performance metrics for a selected time period. Such information may enable the technician to quickly and easily identify whether a service issue effecting a component at the particular address is also likely effecting other components in the geographic region. 
     Referring to  FIG. 4 , a particular illustrative example of a method of analyzing performance data and generating a display based on a performance metric is depicted and generally designated  400 . The method  400  may be performed at one or more devices of a content delivery network. As an illustrative, non-limiting example, the method  400  may be performed by the network analyzer  104 , the mobile network analysis device  108 , or both, of  FIG. 1 . 
     The method  400  may include receiving a search query, at  402 . For example, with reference to  FIG. 1 , the network analyzer  104  may receive the search query  150  from the mobile network analysis device  108 . The search query  150  may indicate the address  152  and the time period  154 , which may be entered by a technician into the mobile network analysis device  108  in response to the user interface  300  of  FIG. 3 . 
     The method  400  may include accessing a database associated with a content delivery network based on the search query, at  404 . For example, with reference to  FIG. 1 , the network analyzer  104  may access the database  102  based on the search query  150  to retrieve a portion of the performance data  130 . In some implementations, the method  400  includes searching the database based on an address, at  406 . For example, the network analyzer  104  may search the performance data  130  for performance data related to a component (or components) of the content delivery network  112  that is located at (or associated with) the address  152 . Additionally, the network analyzer  104  may search the performance data  130  for performance data related to components that are associated with nearby addresses in a geographic region. For example, if the address  152  is an address of the first RG  120 , the network analyzer  104  may search for performance data related to the components (e.g., the first RG  120 , the second RG  122 , the third RG  124 , and the first node  116 ) that are located within the geographic region  170 . 
     In a particular implementation, the network analyzer  104  may determine the nearby addresses based on physical distance from the address. In another particular implementation, the network analyzer  104  may identify the nearby addresses based on location in a common region, such as a neighborhood, a sub-division, a city, a county, a state, or another region. Additionally or alternatively, the network analyzer  104  may determine the nearby addresses based on a logical distance from the address. For example, the network analyzer  104  may determine the nearby addresses based on a hop count from the component associated with the address, based on a connection to a common network component (e.g., a node, a particular network cable, etc.), based on a grouping defined in the network data  144 , or some other logical distance. The determination may be based on a default setting of the network analyzer  104  or may be set by the technician. In some implementations, the method  400  includes retrieving performance data associated with the address and associated with nearby addresses in a geographic region, at  408 . For example, the network analyzer  104  may retrieve performance data related to components (e.g., the first RG  120 , the second RG  122 , the third RG  124 , and the first node  116 ) that are associated with the geographic region  170 . 
     The method  400  may include determining a performance metric based on the performance data, at  410 . For example, with reference to  FIG. 1 , the network analyzer  104  may determine the performance metric  132  based on the retrieved portion of the performance data  130 . In some implementations, the method  400  includes weighting the performance data associated with the nearby addresses based on proximity, at  412 . For example, the network analyzer  104  may weight the retrieved portion of the performance data  130  to generate the weighted performance data  134 . As a particular example, the network analyzer  104  may weight the data associated with components that are nearby to the address  152  higher than data associated with components that are farther away from the address  152 . In some implementations, the method  400  includes analyzing the weighted data to generate the performance metric, at  414 . For example, the network analyzer  104  may analyze the weighted performance data  134  to generate the performance metric  132 . As described with reference to  FIG. 1 , analyzing the retrieved portion of the performance data  130  (or the weighted performance data  134 ) may include performing a threshold analysis, performing an outlier analysis, identifying time-correlated events, identifying a performance trend, or a combination thereof. 
     The method  400  may include generating a display that visually represents geographic data of the geographic region, at least one component of the content delivery network, and the performance network, at  416 . For example, the network analyzer  104  may transmit the message  140  to the mobile network analysis device  108 . The message  140  may include the performance metric  132 , the geographic data  142 , and the network data  144 . Alternatively, the message  140  may indicate (e.g., may point to) locations of the performance metric  132 , the geographic data  142 , and the network data  144 , and the mobile network analysis device  108  may retrieve the performance metric  132 , the geographic data  142 , and the network data  144  based on the message  140 . The mobile network analysis device  108  may generate the display  160  that visually represents the geographic data  142 , the at least one component  162  (based on network data  144 ), and the performance metric  132 . An example of the display is illustrated in  FIG. 2 . 
     The method  400  may include determining whether processing of the performance data indicates a service issue, at  418 . For example, with reference to  FIG. 1 , the network analyzer  104  may determine whether the performance metric  132  is associated with a measurement that exceeds a threshold or that is a statistical outlier as compared to other measurements, which may indicate that components within the geographic region  170  are experiencing a service issue. When the processing indicates that the components are experiencing a service issue, the method  400  continues to  420 , where the performance data is stored in long term storage. For example, the network analyzer  104  may store the retrieved portion of the performance data  130  in the long-term storage  136  when the processing indicates that the components of the geographic region  170  are experiencing a service issue. The retrieved portion of the performance data  130  may be stored in the long-term storage  136  for use in additional processing. When the processing indicates that the components fail to experience a service issue, the method  400  continues to  422 , where the performance data is stored in temporary storage (or the performance data is discarded). For example, the network analyzer  104  may store the retrieved portion of the performance data  130  in the temporary storage  138  (or the retrieved portion of the performance data  130  is discarded) when the processing indicates that the components of the geographic region  170  fail to experience a service issue. 
     Thus, the method  400  of  FIG. 4  processes performance data to identify a performance metric and initiates a display to a technician that visually represents geographic data, at least one component of a content delivery network, and the performance metric. Processing of the performance data may indicate whether components of a content delivery network that are within a geographic region are experiencing a service issue. The display may enable the technician to quickly and easily identify whether a service issue effecting a component at a particular address is also likely effecting other components in the geographic region. Additionally, performance data that is indicative of a service issue may be stored in long-term storage, while performance data that is not indicative of a service issue may be stored in temporary storage (or discarded) to make room for new performance data. 
     Referring to  FIG. 5 , a particular illustrative example of a method of generating a display based on a performance metric is depicted and generally designated  500 . The method  500  may be performed at one or more devices of a content delivery network. As an illustrative, non-limiting example, the method  500  may be performed by the network analyzer  104 , the mobile network analysis device  108 , or both, of  FIG. 1 . 
     The method  500  may include accessing a database to retrieve performance data associated with a content delivery network, at  502 . For example, with reference to  FIG. 1 , the network analyzer  104  may access the database  102  to retrieve a portion of the performance data  130  associated with the content delivery network  112 . The performance data  130  may indicate a count of error corrections, a count of re-initializations, a count of code violations, a count of error seconds, a count of unavailable seconds, or a combination thereof, for components of the content delivery network  112 . 
     The method  500  may include determining, based on the performance data, a performance metric for components of the content delivery network that are within a particular geographic region. For example, with reference to  FIG. 1 , the network analyzer  104  may determine, based on the retrieved portion of the performance data  130 , the performance metric  132 . The performance metric  132  may be associated with components of the content delivery network  112  that are located within the geographic region  170 . In a particular implementation, determining the performance metric includes identifying a service issue within the particular geographic region based on a statistical analysis of portions of the performance data. For example, a service issue may be identified for the components that are within the geographic region  170  based on a statistical analysis of the retrieved portion of the performance data  130 . 
     The method  500  may further include generating a display, at  506 . The display visually represents geographic data representing the particular geographic region, at least one component of the content delivery network within the particular geographic region, and the performance metric. To illustrate, the mobile network analysis device  108  may generate the display  160  that visually represents the geographic data  142 , the at least one component  162 , and the performance metric  132 . 
     In a particular implementation, the at least one component  162  includes a network cable, a residential gateway, a node, a hub, a wireless transmitter, a tap, or a combination thereof. Information associated with the at least one component  162  may be provided by the network data  144 , which may locations of components, such as network cables, of the content delivery network  112  within the geographic region  170 , interconnections between the components, or both. In implementations where the at least one component  162  includes a network cable, the display  160  may indicate whether the network cables are buried or above-ground. To illustrate, with reference to  FIG. 2 , graphical illustrations of network cables of the content delivery network  112  may be illustrated in the display  200 . Formatting of graphical illustrations of above-ground network cables may be different from formatting to graphical illustrations of buried network cables. 
     In a particular implementation, a visual representation of the geographic data  142  may include a satellite image (e.g., photo) of the geographic region  170 . Alternatively, a visual representation of the geographic data  142  may include a map (e.g., a street map) of the geographic region  170 . The geographic data  142  may include image data of a satellite photo of the geographic region  170 , image data of a street map of the geographic region  170 , data that indicates addresses of locations, data that indicates GPS coordinates of locations, or a combination thereof. 
     In a particular implementation, the display  160  visually represents a combination of the geographic data  142 , the at least one component  162 , and the performance metric  132 . For example, visual representations or information corresponding to the geographic data  142 , the at least one component  162 , and the performance metric may be displayed in different portions of the display  160 . Alternatively, the performance metric  132  and a visual representation of the at least one component  162  may be overlaid on an image that visually represents the geographic data  142 . For example, the performance metrics  202  and  204  and the visual representations of the network cables may be overlaid on an image of a neighborhood, as illustrated in  FIG. 2 . 
     In another particular implementation, accessing the database  102  includes searching the database  102  based on the address  152 , determining additional addresses within the geographic region  170  based on a distance to the address  152  or a logical grouping of components of the content delivery network  112 , and retrieving the performance data  130  (e.g., a portion thereof) based on the address  152  and the additional addresses. For example, the network analyzer  104  may search the database  102  for a portion of the performance data  130  that is related to a component associated with the address  152 , and the network analyzer  104  may retrieve the portion of the performance data  130 . The retrieved performance data  130  may include data associated with the address  152  and data associated with nearby addresses within the geographic region  170 . For example, in addition to retrieving a portion of the performance data  130  that is related to the component associated with the address  152 , the network analyzer  104  may also retrieve a portion of the performance data  130  that is related to components associated with nearby addresses (to the address  152 ) or with addresses that are associated with components that have a common logical grouping (e.g., components that are coupled to a same component, such as a network cable, or components that are indicated as related by the network data  144 ). Additionally, the method  500  may include weighting the data associated with the additional addresses based on proximity of the additional addresses to the address  152  (e.g., to generate the weighted performance data  134 ), and analyzing the weighted performance data  134  to generate the performance metric  132 . For example, the network analyzer  104  may perform a statistical analysis on the weighted data (e.g., the retrieved portion of the performance data  130 ) to determine the performance metric  132 , as described with reference to  FIG. 1 . 
     In another particular implementation, the method  500  includes initiating the display  160  at the mobile network analysis device  108 . For example, the network analyzer  104  may transmit the message  140  to the mobile network analysis device  108  to cause the mobile network analysis device  108  to initiate the display  160 . Additionally or alternatively, the performance data  130  is retrieved in response to the search query  150 . For example, the network analyzer  104  may receive the search query  150  from the mobile network analysis device  108 , and the network analyzer  104  may retrieve a portion of the performance data  130  based on the search query  150  (e.g., based on the address  152  and the time period  154  included in the search query  150 ). A parameter of the search query  150  may include a particular event, a particular location (e.g., the address  152 ), a particular component of the content delivery network  112 , a particular component type, or a combination thereof. Parameters of the search query  150  may be selectable or adjustable by a technician using the user interface  300  of  FIG. 3 . 
     In another particular implementation, determining the performance metric  132  includes analyzing the performance data  130  (or a portion thereof) to identify time-correlated events. For example, the network analyzer  104  may analyze the retrieved portion of the performance data  130  to identify time-correlated events. Additionally, the performance metric  132  may be visually represented by an icon (e.g., the first icon  166 , the second icon  168 , or the icon  206 ) that is associated with at least one of the time-correlated events. 
     In another particular implementation, the performance metric  132  indicates a number of occurrences of an event at a subset of the components, and the number of occurrences for each component of the subset is indicated by the display  160  (or the display  200 ). For example, with reference to  FIG. 2 , the first performance metric  202  indicates a number of occurrences of FECs at a particular component, and the number of occurrences (e.g.,  18  FECs) is depicted as shown in  FIG. 2 . The event may include a service call, a customer request, a performance measurement that exceeds a threshold, a performance measurement that is a statistical outlier as compared to performance measurements of other components within the geographic region  170  (or network-wide), or a combination thereof. Additionally, the number of occurrences may be determined for a specified time period. For example, the number of occurrences of an event may be determined for the time period  154 , which is entered by a technician at the mobile network analysis device  108 . 
     In another particular implementation, the method  500  includes processing the performance data  130  (or a portion thereof) to identify a performance trend associated with the performance data  130 , outliers associated with the performance data  130 , data elements of the performance data  130  that exceed a threshold, or a combination thereof. For example, the network analyzer  104  may process the retrieved portion of the performance data  130  to identify measurements that exceed (or fail to exceed) a threshold, as described with reference to  FIG. 1 . As another example, the network analyzer  104  may process the retrieved portion of the performance data  130  to identify measurements that are statistical outliers as compared to measurements of other components within the geographic region  170 , as described with reference to  FIG. 1 . As another example, the network analyzer  104  may identify a performance trend (e.g., a change in an occurrence rate of an event) based on the retrieved portion of the performance data  130 , as described with reference to  FIG. 1 . 
     In another particular implementation, the method  500  includes storing the performance data  130  (e.g., the retrieved portion) at a memory location associated with long-term storage when the processing of the performance data  130  indicates a service issue with the components that are within the geographic region  170 . For example, the network analyzer  104  may store the retrieved portion of the performance data  130  in the long-term storage  136  when the processing of the retrieved portion of the performance data  130  indicates a service issue is being experienced by the components within the geographic region  170 . Additionally or alternatively, the method  500  includes storing the performance data  130  (e.g., the retrieved portion) at a memory location associated with temporary storage when the processing of the performance data  130  fails to indicate a service issue with the components that are within the geographic region  170 . For example, the network analyzer  104  may store the retrieved portion of the performance data  130  in the temporary storage  138  when the processing of the retrieved portion of the performance data  130  fails to indicate that a service issue is being experienced by the components within the geographic region  170 . Alternatively, the network analyzer  104  may discard the retrieved portion of the performance data  130  when the processing of the retrieved portion of the performance data  130  fails to indicate that a service issue is being experienced by the components within the geographic region  170 . 
     Thus, the method  500  of  FIG. 5  generates a display that visually represents geographic data representing a geographic region, at least one component of a content delivery network, and a performance metric. The display enables a technician to quickly and easy identify whether a service issue effecting a component at a particular address is also likely effecting other components in the geographic region. Thus, the technician may make a more effective plan to address the service issue than by performing time consuming testing at the particular address. 
     Referring to  FIG. 6 , an illustrative implementation of a computer system is shown and is designated  600 . The computer system  600  includes a set of instructions that can be executed to cause the computer system  600  to perform any one or more of the methods or computer based functions disclosed herein. The computer system  600  may operate as a standalone device or may be connected (e.g., using a network), to other computer systems or peripheral devices. For example, the computer system  600  may include or be included within any one or more of the network analyzer  104  or the mobile network analysis device  108  of  FIG. 1 . 
     In a networked deployment, the computer system  600  may operate in the capacity of a server or as a client user computer in a server-client user network environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. The computer system  600  may also be implemented as or incorporated into various devices, such as a server, a network device, mobile device, a palmtop computer, a laptop computer, a desktop computer, a communications device, a wireless telephone, a personal computer (PC), a tablet PC, a personal digital assistant (PDA), a set-top box (STB), a media playback device, a customer premises equipment device, an endpoint device, a web appliance, a vehicle (e.g., the computer system  600  may be mounted within the vehicle or incorporated in a component of the vehicle, such as a control display unit), or any other machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. In a particular implementation, the computer system  600  may be implemented using electronic devices that provide video, audio, or data communication. Further, while a single computer system  600  is illustrated, the term “system” shall also be taken to include any collection of systems or sub-systems that individually or jointly execute a set, or multiple sets, of instructions to perform one or more computer functions. 
     As illustrated in  FIG. 6 , the computer system  600  may include a processor  602 , e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both. Moreover, the computer system  600  may include a main memory  604  and a static memory  606 , which can communicate with each other via a bus  608 . As shown, the computer system  600  may further include a video display unit  610 , such as a liquid crystal display (LCD), a light emitting diode (LED) display, a touch screen display, a flat panel display, a solid state display, or a lamp assembly of a projection system. Additionally, the computer system  600  may include an input device  612 , such as a remote control device (e.g., a television remote or a set-top box remote), a keyboard, a joystick, another input device, or combinations thereof. In addition, the computer system  600  may include a cursor control device  614 , such as a mouse. In some embodiments, the input device  612  and the cursor control device  614  may be integrated into a single device, such as a capacitive touch screen input device. The computer system  600  may also include a drive unit  616 , a signal generation device  618 , such as a speaker or remote control, and a network interface device  620 . Some computer systems  600  may not include an input device (e.g., a server may not include an input device). 
     I n a particular implementation, as depicted in  FIG. 6 , the drive unit  616  may include a computer-readable storage device  622  in which a performance metric, geographic data, network data (of a content delivery network), and one or more sets of instructions  624 , e.g. software, can be embedded. The computer-readable storage device  622  may be random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), register(s), solid-state memory, hard disk, a removable disk, a compact disc read-only memory (CD-ROM), other optical disk storage, magnetic disk storage, magnetic storage devices, or any other storage device that can be used to store program code in the form of instructions or data and that can be accessed by a computer and/or a processor. A computer-readable storage device is not a signal. 
     Further, the instructions  624  may embody one or more of the methods or logic as described herein. The instructions  624  may be executable by the processor  602  to perform one or more functions or methods described herein, such as the method  400  of  FIG. 4  of the method  500  of  FIG. 5 . In a particular implementation, the performance metric, the geographic data, the network data, and the instructions  624  may reside completely, or at least partially, within the main memory  604 , the static memory  606 , and/or within the processor  602  during execution by the computer system  600 . The main memory  604  and the processor  602  also may include a computer-readable storage device. The performance metric, the geographic data, and the network data in the drive unit  616 , the main memory  604 , the static memory  606 , the processor  602 , or combinations thereof may be used to generate a display, e.g., at the video display unit  610 , that visually represents the network data and the performance metric overlaid on the geographic data. 
     In a particular implementation, the instructions  624  may be executable by the processor  602  to obtain performance data associated with a content delivery network. The instructions  624  may be executable by the processor  602  to determine, based on the performance data, a performance metric for components of the content delivery network that are within a particular geographic region. The instructions  624  may be further executable by the processor  602  to generate a display (e.g., at the video display unit  610 ) that visually represents the geographic data (representing the particular geographic region), at least one component of the content delivery network within the particular geographic region (based on the network data), and the performance metric. 
     In an alternative implementation, dedicated hardware implementations, such as application specific integrated circuits, programmable logic arrays and other hardware devices, may be constructed to implement one or more of the methods described herein. Various embodiments may include a variety of electronic and computer systems. One or more embodiments described herein may implement functions using two or more specific interconnected hardware modules or devices with related control and data signals that can be communicated between and through the modules, or as portions of an application-specific integrated circuit (ASIC). Accordingly, the present system encompasses software, firmware, and hardware implementations. 
     In accordance with various embodiments of the present disclosure, the methods described herein may be implemented by software programs executable by a computer system, a processor, or a device, which may include forms of instructions embodied as a state machine implemented with logic components in an ASIC or a field programmable gate array (FPGA) device. Further, in an exemplary, non-limiting embodiment, implementations may include distributed processing, component/object distributed processing, and parallel processing. Alternatively, virtual computer system processing may be constructed to implement one or more of the methods or functionality as described herein. It is further noted that a computing device, such as a processor, a controller, a state machine or other suitable device for executing instructions to perform operations may perform such operations directly or indirectly by way of one or more intermediate devices directed by the computing device. 
     The present disclosure includes the computer-readable storage device  622  that stores the instructions  624  or receives, stores and executes the instructions  624 , so that a device connected to a network  628  may communicate voice, video or data over the network  628 . While the computer-readable storage device  622  is shown to be a single device, the computer-readable storage device  622  may include a single device or multiple devices, such as a centralized or distributed database, and/or associated caches and servers that store one or more sets of instructions. The computer-readable storage device  622  is capable of storing a set of instructions for execution by a processor to cause a computer system to perform any one or more of the methods or operations disclosed herein. For example, the computer-readable storage device  622  may store instructions for execution by a processor to cause a computer system to perform the method  400  of  FIG. 4  or the method  500  of  FIG. 5 . 
     In a particular non-limiting, exemplary implementation, the computer-readable storage device  622  may include a solid-state memory such as embedded memory (or a memory card or other package that houses one or more non-volatile read-only memories). Further, the computer-readable storage device  622  may be a random access memory or other volatile re-writable memory. Additionally, the computer-readable storage device  622  may include a magneto-optical or optical device, such as a disk or tapes or other storage device. Accordingly, the disclosure is considered to include any one or more of a computer-readable storage device and other equivalents and successor devices, in which data or instructions may be stored. 
     Although the one or more components and functions may be described herein as being implemented with reference to particular standards or protocols, the disclosure is not limited to such standards and protocols. Such standards are from time-to-time superseded by faster or more efficient equivalents having essentially the same functions. Wireless standards for device detection, short-range communications, and long-range communications can be used by the computer system  600  in selected embodiments. 
     The illustrations of the embodiments described herein are intended to provide a general understanding of the structure of the various embodiments. The illustrations are not intended to serve as a complete description of all of the elements and features of apparatus and systems that utilize the structures or methods described herein. Many other embodiments may be apparent to those of skill in the art upon reviewing the disclosure. Other embodiments may be utilized and derived from the disclosure, such that structural and logical substitutions and changes may be made without departing from the scope of the disclosure. Figures are also merely representational and may not be drawn to scale. Accordingly, the disclosure and the figures are to be regarded as illustrative rather than restrictive. 
     Although specific embodiments have been illustrated and described herein, it should be appreciated that any subsequent arrangement designed to achieve the same or similar purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all subsequent adaptations or variations of various embodiments. 
     Less than all of the steps or functions described with respect to the exemplary processes or methods can also be performed in one or more of the exemplary embodiments. Further, the use of numerical terms to describe a device, component, step or function, such as first, second, third, and so forth, is not intended to describe an order unless expressly stated. The use of the terms first, second, third and so forth, is generally to distinguish between devices, components, steps or functions unless expressly stated otherwise. Additionally, one or more devices or components described with respect to the exemplary embodiments can facilitate one or more functions, where the facilitating (e.g., facilitating access or facilitating establishing a connection) can include less than every step needed to perform the function or can include all of the steps needed to perform the function. 
     In one or more embodiments, a processor (which can include a controller or circuit) has been described that performs various functions. It should be understood that the processor can be implemented as multiple processors, which can include distributed processors or parallel processors in a single machine or multiple machines. The processor can be used in supporting a virtual processing environment. The virtual processing environment may support one or more virtual machines representing computers, servers, or other computing devices. In such virtual machines, components such as microprocessors and storage devices may be virtualized or logically represented. The processor can include a state machine, an application specific integrated circuit, and/or a programmable gate array (PGA) including a FPGA. In one or more embodiments, when a processor executes instructions to perform “operations”, this can include the processor performing the operations directly and/or facilitating, directing, or cooperating with another device or component to perform the operations. 
     The Abstract is provided with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, various features may be grouped together or described in a single embodiment for the purpose of streamlining the disclosure. This disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter may be directed to less than all of the features of any of the disclosed embodiments. Thus, the following claims are incorporated into the Detailed Description, with each claim standing on its own as defining separately claimed subject matter. 
     The above-disclosed subject matter is to be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments, which fall within the scope of the present disclosure. Thus, to the maximum extent allowed by law, the scope of the present disclosure is to be determined by the broadest permissible interpretation of the following claims and their equivalents, and shall not be restricted or limited by the foregoing detailed description.