Patent Publication Number: US-11397636-B2

Title: Triggering diagnostic data generation and aggregation across multiple systems to reduce computing resources

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present application is a divisional of and claims priority of U.S. patent application Ser. No. 16/548,323, filed Aug. 22, 2019, the content of which is hereby incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     Computer systems are currently in wide use. Some computer systems operate in a client/server architecture. In such an architecture, servers in a remote server environment provide services that can be accessed by client computing systems. 
     In these types of computing systems, it is not uncommon for the service to perform in a way that is problematic. For instance, there may be bugs or other malfunctions in the server logic that implement the performance of the service. In such scenarios, end users often provide an input indicating that a problem has occurred. For instance, an end user may actuate a link that can be used to report a problem to a support engineer or other support person. 
     The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. 
     SUMMARY 
     A server implementing a service receives a request from a client computing system and stores the request in a cache, along with a correlation identifier. The server returns a response to the request, along with a correlation identifier (ID). When a trigger input is received, indicating that a problem has been encountered at the client computing system, a diagnostic data generation system on the service accesses the cache and generates diagnostic data for the request corresponding to the correlation ID. The diagnostic data corresponding to the correlation ID is then provided to a support data storage system where it can be combined with diagnostic data from the client computing system. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of one example of a computing system architecture. 
         FIG. 2  is a block diagram showing the architecture illustrated in  FIG. 1 , in more detail. 
         FIGS. 3A and 3B  (collectively referred to herein as  FIG. 3 ) show one example of the operation of the architecture illustrated in the previous figures. 
         FIG. 4  is a block diagram showing one example of the architecture illustrated in the previous figures, deployed in a cloud computing architecture. 
         FIG. 5  is a block diagram showing one example of a computing environment that can be used in the architectures shown in the previous figures. 
     
    
    
     DETAILED DESCRIPTION 
     As discussed above, in a client/server architecture, it is not uncommon for a client computing system. to experience a problem in using the service. The problem may be caused by bugs in the service, logic malfunction in the service, or for other reasons. This may be detected by an end user or an automated system. In such cases, the end user (or an automated component on the client computing system) may provide an input indicating that the performance of the service is in some way inadequate. For instance, the end user may actuate a link indicating a request to report an issue to a support engineer or other support person. 
     In order to address the reported issue, the support person often needs to access a variety of different data stores where diagnostic data is stored. For instance, it may be that the server that is implementing the service generates server diagnostic data and stores it in one data store. It may be that the client computing system, however, generates client diagnostic data and stores it in a different data store. Similarly, there may be a plurality of different services that have performed operations in response to the same request. Each of those services may generate server diagnostic data and store it in yet other data stores. Thus, when the support user (e.g., support engineer) wishes to analyze the diagnostic data to address the issue reported by the client, the support user must often access a plurality of different data stores in an attempt to piece together the diagnostic data corresponding to the reported issue. This can be cumbersome, time consuming, and error prone. 
     To address this problem, some current systems, the server generates diagnostic data every time it responds to a client request. It sends the diagnostic data to the client, embedded in the payload of the client&#39;s data that is returned in response to the request. However, the diagnostic data can be of a significant size. Therefore, when the diagnostic data is sent with every request (regardless of whether a problem is encountered) this unnecessarily consumes bandwidth and also uses computing overhead to generate the diagnostic data, when it will never be used. However, in current systems, where the diagnostic data is not generated by the server, then by the time the user reports a problem with the previous request, the server has already lost the ability to generate at least some of the diagnostic data. 
     The present description thus proceeds with respect to a system in which the server generates a correlation identifier (ID), corresponding to a request received from a client computing system and stores the request and correlation ID in cache. The server then generates a response to the request and returns the response, along with the correlation ID, to the client computing system. If a problem is encountered, the client computing system sends an indication to the server, indicating that a problem was encountered, along with the correlation ID. The server then accesses the cache and identifies the request corresponding to the correlation ID and generates diagnostic data for that request. The diagnostic data is then sent to a support data storage system, where it can be stored along with (or indexed by) the correlation ID. Similarly, the client computing system sends its diagnostic data, along with the correlation ID, to the support data storage system. The client diagnostic data is thus stored along with the server diagnostic data, as well as the correlation ID. Therefore, a support user (such as a support engineer) can access a single data store (e.g., the support data storage system) using the correlation ID and obtain an aggregated view of both the client diagnostic data and the server diagnostic data. This saves computing resources, such as processing time, memory utilization and/or network bandwidth. 
       FIG. 1  is a block diagram showing one example of a computing system architecture  100 . Architecture  100  shows that a plurality of service computing systems  102 - 104  can be accessed by one or more different client computing systems  106 - 108 , over network  110 . Each of the service computing system  102 - 104  may implement a different service that can be used by the different computing systems  106 - 108 . Thus, each service computing system  102 - 104  may expose an interface that can be accessed by the client computing systems  106 - 108  over network  110 . Network  110  can thus be any of a wide variety of different types of networks, such as a wide area network, a local area network, a near field communication network, a cellular communication network, or a wide variety of other networks or combinations of networks. 
       FIG. 1  also shows that, in one example, client computing system  106  can generate user interfaces  111 , that have user input mechanisms  112 , for interaction by user  114 . User  114  thus interacts with user input mechanisms  112  on user interfaces  111  in order to control and manipulate client computing system  106 , and some portions of the service computing systems  102 - 104  that it accesses. 
     Similarly, client computing system  108  illustratively generates user interfaces  116 , with user input mechanisms  118 , for interaction by user  120 . User  120  illustratively interacts with user interface mechanisms  118  in order to control and manipulate client computing system  108  and parts of the service computing systems  102 - 104 , with which it interacts. 
     It can happen that a user  114  is dissatisfied with the performance of a service provided by one of the service computing systems  102  or  104  (or both). For instance, assume that user  114  has interacted with user input mechanisms  112  in order to cause client computing system  106  to generate a request of a service implemented by service computing system  102 . After receiving a response to that request, it may be that user  114  is dissatisfied with the performance of service computing system  102 . Thus, user  114  may provide an input through a user input mechanism  112 , indicating that a problem has occurred. For instance, it may be that user  114  actuates an actuator on the user input mechanism  112 , indicating that user  114  wishes to report an issue to a support user (such as a support engineer). Also, a problem can be automatically detected and reported as well. 
     Thus,  FIG. 1  shows that a support data storage system  122  exposes an interface  123  so that system  122  is accessible by client computing systems  106 - 108  and service computing systems  102 - 104 , and a support computing system  124 , over network  110 . Similarly, support computing system  124  can generate support user interfaces  126  for interaction by a support user  128 . Support user  128  may be a support engineer, for example. 
     Briefly, by way of overview, when a service computing system (such as system  102 ) generates a response to a request received from a client computing system (such as system  106 ), the service computing system  102  also generates a correlation identifier and returns that identifier to client computing system  106 , along with the response it generated in response to the request that it received from client computing system  106 . Then, when user  114  provides an input indicating that a problem has occurred, client computing system  106  provides an indication of this, along with the correlation ID which corresponds to the request for which the problem occurred, to service computing system  102 . Service computing system  102  then generates service diagnostic data, corresponding to that correlation ID, and provides the service diagnostic data to support data storage system  122 , along with the correlation ID. Client computing system  106  also generates client diagnostic data corresponding to the request and provides the client diagnostic data, along with the correlation ID, to the support data storage system  122 . Support data storage system  122  thus stores the client diagnostic data and the service diagnostic data in a data store, and indexes it with the correlation ID. The support user  128  can then use support user interfaces  126  and interfaces  123  to access both the client diagnostic data and the service diagnostic data, in support data storage system  122 , using the correlation ID. Thus, support user  128  obtains an aggregated view of all of the diagnostic data for the reported problem, by only accessing a single data storage system  122 . 
     Also, when client computing system  106  submits a request to a service, it may be that multiple different service computing systems  102 - 104  perform operations in responding to that request. The correlation ID for the request will be shared among all service computing systems  102 - 104  so that diagnostic data for the corresponding correlation ID can be generated from all of the service computing systems  102 - 104  and provided to support data storage system  122 , where it can all be stored and indexed by the same correlation ID. 
     In having the service computing systems  102 - 104  only generate diagnostic data when a problem has occurred, this saves expensive processing overhead which would otherwise be needed to generate the diagnostic data for every client request, regardless of whether it would ever be used (e.g., regardless of whether a problem was ever reported for that request). Similarly, because the diagnostic data is only generated and provided to support data storage system  122  when a problem is reported, this saves bandwidth that would otherwise be consumed in the architecture  100  in sending the diagnostic data back and forth between the service computing system and the client computing system, when the diagnostic data will never be used. In addition, because both the client diagnostic data and the service diagnostic data are stored together and indexed using a single correlation ID in support data storage system  122 , this greatly eases the burden of support user  128  in accessing the data. Support user  128  need only access the single support data storage system  122 , where the aggregated diagnostic data can be surfaced for support user  128 . 
       FIG. 2  shows a block diagram of architecture  100 , with some of the items in architecture  100  shown in more detail. Some of the items in  FIG. 2  are similar to those shown in  FIG. 1 , and they are similarly numbered. Before describing the overall operation of architecture  100  in more detail, a brief description of some of the additional items shown in architecture  100 , and their operation, will first be provided. 
     Service computing system  102  illustratively includes one or more processors or servers  130 , service execution logic  133  (which can include a frontend system  132  and a backend system  134 ), data store  136 , correlation ID generator logic  138 , cache interaction logic  140 , cache  142 , diagnostic data generation system  144 , and it can include a wide variety of other items  146 . Cache  142  can include a plurality of records  148 - 150 , and other items  152 . Each of the records  148 - 150  can include a request received from a client computing system, along with a correlation identifier that was generated by correlation ID generator logic  138 , for that request. 
     Client computing system  106  can include one or more processors or servers  156 , data store  158 , support trigger detector  160 , service interaction logic  162 , diagnostic data generation system  164 , communication system  166 , user interface logic  168 , and it can include other items  170 . 
     Support data storage system  122  can also include one or more processors or servers  172 , to generate interfaces  123  and it can include support data store  174 . Support data store  174  can include a plurality of different support records  176 - 178 , and it can include other items  180 . Each support record  176  can include a correlation ID  182 , server diagnostic data  184 , corresponding to the correlation ID  182 , client diagnostic data  186  corresponding to the correlation ID  182 , and it can include other items  188 . 
     Briefly, by way of overview, frontend system  132  and service computing system  102  can expose an interface that can be called by the service interaction logic  162  in client computing system  106 . Logic  162  can thus submit a request  190  to the frontend system  132  over network  110 . Frontend system  132  can interact with backend system  134  that, itself, interacts with data (e.g., customer data) in data store  136  to generate a response to request  190 . When the request  190  is received, correlation ID generator logic  138  generates a correlation identifier corresponding to the request. It may be a GUID or other unique identifier. Cache interaction logic  140  then stores the request, along with the corresponding correlation ID, as a record  148 - 150 , in cache  142 . 
     When the backend system  134  has generated the response to request  190 , it provides it to frontend system  132  which appends the correlation ID to the response and returns the response and correlation ID  192 , to the service interaction logic  162  in the client computing system  106  that generated the request. 
     Service interaction logic  162  then surfaces the response (or the results of the request) for user  114 . It may be that user  114 , at some point, determines that there is a problem with the service. This may be due to a bug in the service, due to an execution error or other error in the functionality of the service, etc. In that case, user  114  provides an input through a user input mechanism  112  indicating that a problem has occurred. User interface logic  168  generates the user interfaces  111  and detects user interaction with the user interface mechanisms  112 . When the user provides an input indicating that a problem has occurred, support trigger detector  160  detects that a problem has occurred and controls service interaction logic  162  to provide an input to service computing system  102  indicating that a problem has occurred, and also indicating the correlation ID corresponding the to the problem. This is indicated by block  194  in  FIG. 2 . At the same time, diagnostics data generation system  164  generates any client diagnostic data that can be generated on client computing system  106 , based upon the reported error. Communication system  166  then communicates the client diagnostic data, along with the correlation ID, to support data storage system  122 . Thus, communication system  166  can be configured to communicate over network  110 , using interface  123 . 
     When service computing system  102  receives the problem notice and correlation ID  194 , cache interaction logic  140  obtains the request corresponding to that correlation ID, from cache  142  and provides it to server diagnostic data generation system  144 . Server diagnostic data generation system  144  then generates server diagnostic data that can be generated by service computing system  102 , based upon that request. In one example, it resubmits the request to service execution logic  133  and obtains information based upon the operation of service computing system  102  in responding to the request. It can generate diagnostic data in a wide variety of other ways as well. Server diagnostic data generation system  144  then provides the service diagnostic data, along with a correlation ID, to support data storage system  122 , through interface  123 . 
     When the client diagnostic data and the service diagnostic data are received at support data storage system  122 , processors and/or servers  172  determine that the diagnostic data corresponds to the same correlation ID (and thus corresponds to the same problem that was reported by user  114 ). It thus stores the diagnostic data, as a support record (e.g., support record  176 ) in support data store  174 . Each support record  176  thus illustratively includes the correlation ID  182 , the server diagnostic data  184  corresponding to that correlation ID, and the client diagnostic data  186  corresponding to that correlation ID. 
     Support computing system  124  may also receive a notification from client computing system  106  indicating that support trigger detector  160  has detected an input to trigger a support operation. The notification will illustratively include the correlation ID so that support user  128  can use support user interface  126  and support computing system  124  to access the support record  176  in support data store  174  (via interface  123 ), corresponding to the correlation ID. In this way, both the server diagnostic data  184  and the client diagnostic data  186 , for the same problem, can easily be accessed by, and surfaced for, support computing system  124  so that the data can be analyzed by support user  128 , in addressing the reported problem. 
     It will be noted that interface  123  may actually include a plurality of different interfaces (e.g., the interface for read operations from support user  128  may be different than the interface for write operation from other computing systems). These and other interface configurations and implementations for the interfaces discussed herein are contemplated. 
       FIGS. 3A and 3B  (collectively referred to herein as  FIG. 3 ) show a flow diagram illustrating one example of the operation of architecture  100 , in more detail. It is first assumed that the client/server architecture illustrated in  FIGS. 1 and 2  is operational so that client computing systems  106 - 108  can make requests of one or more service computing systems  102 - 104  and obtain responses to those requests. This is indicated by block  200  in the flow diagram of  FIG. 3 . 
     The present discussion will proceed with respect to client computing system  106  submitting a request to service computing system  102 . It will be noted, though, that the other client computing systems can also submit requests to the service computing systems, and/or multiple service computing systems can generate responses to a request submitted by a client computing system (e.g., the services can be pipelined or can generate responses simultaneously, etc.). 
     At some point, client computing system  106  submits a request  190  to service computing system  102 . This is indicated by block  202  in the flow diagram of  FIG. 3 . As discussed above, frontend system  132  can expose an interface that is called by client computing system  106  (e.g., by service interaction logic  162  in client computing system  106 ) to submit the request. Submitting the request through an exposed interface, that was exposed by frontend system  132 , is indicated by block  204 . The request can be submitted in other ways as well, and this is indicated by block  206 . Frontend system  132  then provides an indication to correlation ID generator logic  138  that a request has been received. Logic  138  generates a correlation ID corresponding to the request. This is indicated by block  208 . The correlation ID can be any of a variety of unique identifiers that can be used to identify different requests, and differentiate them from one another. Cache interaction logic  140  then stores the correlation ID, along with the request  190 , in cache  142 . This is indicated by block  210 . Cache  142  can operate in a variety of different ways. It can store records for a predetermined or default, or configurable, amount of time. It can store a given number of records, in a first-in-first-out manner so that the oldest received records are removed from the cache as new records are stored, or it can store records with configurable retention in other ways. 
     Frontend system  132  also provides request  190  to backend system  134  which generates a response to the request. In doing so, it may access user data in data store  136 , or it may perform other operations, based upon the request  190 . Generating the response to the request is indicated by block  212  in the flow diagram of  FIG. 3 . 
     The frontend system  132  then provides the response and the correlation ID  192  back to the requesting client computing system  106 . This is indicated by block  214 . 
     If there is no trouble or other issue with the response, as indicated by block  216 , then processing simply reverts to block  202  where other requests can be submitted to the service computing system  102  by client computing system  106 . 
     However, it may be that support trigger detector  160  detects a support trigger at block  216  of  FIG. 3 . The support trigger can be detected in a variety of different ways. For instance, there may be an automated support trigger generator in client computing system  106  that automatically generates a support trigger. By way of example, if the service computing system  102  is taking too long to respond to request  190 , this may generate an automatic trigger to notify support computing system  124  that there may be a problem with service computing system  102 . In another example, the support trigger is detected based upon a user input from user  114 . For instance, user  114  may observe that the performance of service computing system  102  is somehow compromised (e.g., it is taking too long to respond, it is not responding in an expected or correct fashion, etc.). In that case, user  114  may provide an input through user input mechanisms  112  that cause support trigger detector  160  to detect a support trigger. In one example, user  114  can actuate a link (or other user interface actuator) to send a message or otherwise initiate communication with a support user  128  at support computing system  124 . This actuation may be detected by support trigger detector  160  as a support trigger. 
     If, at block  216  (in the flow diagram of  FIG. 3 ) a support trigger is detected, then service interaction logic  162  sends an indication or notice  194  that a problem has occurred (e.g., that a support trigger is detected), along with the correlation ID for the response  192  that corresponds to the reported problem (or for which the support trigger was generated). Sending the problem notice and correlation ID  194  to service computing system  102  is indicated by block  218  in the flow diagram of  FIG. 3 . 
     Also, when support trigger detector  160  detects a support trigger, it indicates this to diagnostic generation system  164  which generates client diagnostic data on client computing system  106 . Generating the client diagnostic data is indicated by block  220  in the flow diagram of  FIG. 3 . There are a wide variety of different types of diagnostic data that can be generated by system  164 . It may depend on the type of service to which request  190  was sent. It may include such things as the latency between when the request  190  was sent and the response  192  was received. It may include a wide variety of other diagnostic information as well. 
     Communication system  166  then sends the client diagnostic data, along with the corresponding correlation ID, to support data storage system  122 . This is indicated by block  222  in the flow diagram of  FIG. 3 . 
     Similarly, when the problem notice  194  and correlation ID are received at service computing system  102 , cache interaction logic  140  accesses cache  142  to obtain the request corresponding to the correlation ID received with the problem notice  194 . It provides that request to diagnostic data generation system  144  which generates diagnostic data corresponding to that request. It can do this by resubmitting the request through service execution logic  133  (e.g., to frontend system  132  and/or backend system  134 ) and recording diagnostic data that is generated during the processing for that request. It can perform a wide variety of other diagnostic data generation operations as well. Generating the server diagnostic data for the request  190  corresponding to the correlation ID is indicated by block  224 . Accessing cache  142  using the correlation ID, to obtain the request, is indicated by block  226 . Replaying the request through the frontend system  132  and/or backend system  134  is indicated by block  228 . Performing other diagnostic operations is indicated by block  230 . The server diagnostic data can be generated in many other ways as well, and this is indicated by block  232 . 
     Diagnostic data generation system  144  then sends the server diagnostic data, along with the corresponding correlation ID, to support data storage system  122 . This is indicated by block  234  in the flow diagram of  FIG. 3 . The processor/server  172  in support data storage system  122  then stores the client diagnostic data and server diagnostic data in a support record in support data store  174 , using the correlation ID corresponding to that diagnostic data. This is indicated by block  236  in the flow diagram of  FIG. 3 . In one example, the client and server diagnostic data in the support record are indexed using the correlation ID. The support records can then be surfaced for access by the support computing system  124 . This is indicated by block  238 . 
     In this way, when a support user  128  receives an indication that user  114  has reported a problem, that indication will illustratively include the correlation ID. User  128  can then obtain the server diagnostic data and client diagnostic data, for that correlation ID, from the appropriate support record in support data store  174  using interface  123 . This information can be surfaced by support computing system  124  on a support user interface  126  for support user  128 . In this way, support user  128  can obtain an aggregated set of diagnostic data (the diagnostic data generated on the client computing system  106  and that generated on the service computing system  102 ) by accessing a single data store. Similarly, because the diagnostic data is only generated on service computing system  102  and provided to support data storage system  122  when a problem is reported or automatically detected, this saves expensive processing overhead that would otherwise be used to generate the diagnostic data. It also conserves bandwidth which would be consumed in transmitting unneeded diagnostic data. 
     It will be noted that the above discussion has described a variety of different systems, components and/or logic. It will be appreciated that such systems, components and/or logic can be comprised of hardware items (such as processors and associated memory, or other processing components, some of which are described below) that perform the functions associated with those systems, components and/or logic. In addition, the systems, components and/or logic can be comprised of software that is loaded into a memory and is subsequently executed by a processor or server, or other computing component, as described below. The systems, components and/or logic can also be comprised of different combinations of hardware, software, firmware, etc., some examples of which are described below. These are only some examples of different structures that can be used to form the systems, components and/or logic described above. Other structures can be used as well. 
     The present discussion has mentioned processors and servers. In one embodiment, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. They are functional parts of the systems or devices to which they belong and are activated by, and facilitate the functionality of the other components or items in those systems. 
     Also, a number of user interface displays have been discussed. They can take a wide variety of different forms and can have a wide variety of different user actuatable input mechanisms disposed thereon. For instance, the user actuatable input mechanisms can be text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. They can also be actuated in a wide variety of different ways. For instance, they can be actuated using a point and click device (such as a track ball or mouse). They can be actuated using hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc. They can also be actuated using a virtual keyboard or other virtual actuators. In addition, where the screen on which they are displayed is a touch sensitive screen, they can be actuated using touch gestures. Also, where the device that displays them has speech recognition components, they can be actuated using speech commands. 
     A number of data stores have also been discussed. It will be noted they can each be broken into multiple data stores. All can be local to the systems accessing them, all can be remote, or some can be local while others are remote. All of these configurations are contemplated herein. 
     Also, the figures show a number of blocks with functionality ascribed to each block. It will be noted that fewer blocks can be used so the functionality is performed by fewer components. Also, more blocks can be used with the functionality distributed among more components. 
       FIG. 4  is a block diagram of architecture  100 , shown in  FIG. 1 , except that its elements are disposed in a cloud computing architecture  500 . Cloud computing provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that delivers the services. In various embodiments, cloud computing delivers the services over a wide area network, such as the internet, using appropriate protocols. For instance, cloud computing providers deliver applications over a wide area network and they can be accessed through a web browser or any other computing component. Software or components of architecture  100  as well as the corresponding data, can be stored on servers at a remote location. The computing resources in a cloud computing environment can be consolidated at a remote data center location or they can be dispersed. Cloud computing infrastructures can deliver services through shared data centers, even though they appear as a single point of access for the user. Thus, the components and functions described herein can be provided from a service provider at a remote location using a cloud computing architecture. Alternatively, they can be provided from a conventional server, or they can be installed on client devices directly, or in other ways. 
     The description is intended to include both public cloud computing and private cloud computing. Cloud computing (both public and private) provides substantially seamless pooling of resources, as well as a reduced need to manage and configure underlying hardware infrastructure. 
     A public cloud is managed by a vendor and typically supports multiple consumers using the same infrastructure. Also, a public cloud, as opposed to a private cloud, can free up the end users from managing the hardware. A private cloud may be managed by the organization itself and the infrastructure is typically not shared with other organizations. The organization still maintains the hardware to some extent, such as installations and repairs, etc. 
     In the example shown in  FIG. 4 , some items are similar to those shown in  FIG. 1  and they are similarly numbered.  FIG. 4  specifically shows that service computing systems  102  and  104  can be located in cloud  502  (which can be public, private, or a combination where portions are public while others are private). Therefore, users  114  and  120  use user devices  504  and  506  to access those systems through cloud  502 .  FIG. 4  also shows that support data storage system  122  can also be located in cloud  502 . Support user  128  uses support computing system  124  to access support data storage system  122 . 
       FIG. 4  also depicts another example of a cloud architecture.  FIG. 4  shows that it is also contemplated that some elements of architecture  100  can be disposed in cloud  502  while others are not. By way of example, support data storage system  122  can be disposed outside of cloud  502 , and accessed through cloud  502 . Regardless of where they are located, they can be accessed directly by devices  504  and  506  and system  124 , through a network (either a wide area network or a local area network), they can be hosted at a remote site by a service, or they can be provided as a service through a cloud or accessed by a connection service that resides in the cloud. All of these architectures are contemplated herein. 
     It will also be noted that architecture  100 , or portions of it, can be disposed on a wide variety of different devices. Some of those devices include servers, desktop computers, laptop computers, tablet computers, or other mobile devices, such as palm top computers, cell phones, smart phones, multimedia players, personal digital assistants, etc. 
       FIG. 5  is one example of a computing environment in which architecture  100 , or parts of it, (for example) can be deployed. With reference to  FIG. 5 , an example system for implementing some embodiments includes a computing device in the form of a computer  810  that is configured to operate as described above. Components of computer  810  may include, but are not limited to, a processing unit  820  (which can comprise processors or servers from previous FIGS.), a system memory  830 , and a system bus  821  that couples various system components including the system memory to the processing unit  820 . The system bus  821  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus. Memory and programs described with respect to  FIG. 1  can be deployed in corresponding portions of  FIG. 5 . 
     Computer  810  typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer  810  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. It includes hardware storage media including both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer  810 . Communication media typically embodies computer readable instructions, data structures, program modules or other data in a transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media. 
     The system memory  830  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  831  and random access memory (RAM)  832 . A basic input/output system  833  (BIOS), containing the basic routines that help to transfer information between elements within computer  810 , such as during start-up, is typically stored in ROM  831 . RAM  832  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  820 . By way of example, and not limitation,  FIG. 5  illustrates operating system  834 , application programs  835 , other program modules  836 , and program data  837 . 
     The computer  810  may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,  FIG. 5  illustrates a hard disk drive  841  that reads from or writes to non-removable, nonvolatile magnetic media, and an optical disk drive  855  that reads from or writes to a removable, nonvolatile optical disk  856  such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  841  is typically connected to the system bus  821  through a non-removable memory interface such as interface  840 , and optical disk drive  855  are typically connected to the system bus  821  by a removable memory interface, such as interface  850 . 
     Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc. 
     The drives and their associated computer storage media discussed above and illustrated in  FIG. 5 , provide storage of computer readable instructions, data structures, program modules and other data for the computer  810 . In  FIG. 5 , for example, hard disk drive  841  is illustrated as storing operating system  844 , application programs  845 , other program modules  846 , and program data  847 . Note that these components can either be the same as or different from operating system  834 , application programs  835 , other program modules  836 , and program data  837 . Operating system  844 , application programs  845 , other program modules  846 , and program data  847  are given different numbers here to illustrate that, at a minimum, they are different copies. 
     A user may enter commands and information into the computer  810  through input devices such as a keyboard  862 , a microphone  863 , and a pointing device  861 , such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  820  through a user input interface  860  that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A visual display  891  or other type of display device is also connected to the system bus  821  via an interface, such as a video interface  890 . In addition to the monitor, computers may also include other peripheral output devices such as speakers  897  and printer  896 , which may be connected through an output peripheral interface  895 . 
     The computer  810  is operated in a networked environment using logical connections to one or more remote computers, such as a remote computer  880 . The remote computer  880  may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  810 . The logical connections depicted in  FIG. 5  include a local area network (LAN)  871  and a wide area network (WAN)  873 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
     When used in a LAN networking environment, the computer  810  is connected to the LAN  871  through a network interface or adapter  870 . When used in a WAN networking environment, the computer  810  typically includes a modem  872  or other means for establishing communications over the WAN  873 , such as the Internet. The modem  872 , which may be internal or external, may be connected to the system bus  821  via the user input interface  860 , or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer  810 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 5  illustrates remote application programs  885  as residing on remote computer  880 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
     It should also be noted that the different examples described herein can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is contemplated herein. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. 
     Example 1 is a computing system, comprising: 
     at least one processor; 
     service execution logic, executed by the at least one processor, that receives a client request generated by a client computing system and generates a response to the client request; 
     correlation identifier (ID) generator logic, executed by the at least one processor, that generates a correlation ID corresponding to the client request, the service execution logic sending the response and the correlation ID to the client computing system; 
     cache interaction logic, executed by the at least one processor, that generates a memory record that includes the client request and the corresponding correlation ID for storage in a memory; and 
     a server diagnostic data generation system that receives the correlation ID and a problem indication indicating that a problem is detected, with respect to the response, at the client computing system, the server diagnostic data generation system generating server diagnostic data based on the request stored in the memory with the corresponding correlation ID and sending the server diagnostic data to a support data storage system, along with the corresponding correlation ID. 
     Example 2 is the computing system of any or all previous examples wherein the service execution logic comprises: 
     a front end system that exposes an interface that receives the client request. 
     Example 3 is the computing system of any or all previous examples wherein the service execution logic comprises: 
     a back end system that receives an indication of the client request from the front end system and performs a service operation, based on the client request, to generate the response. 
     Example 4 is the computing system of any or all previous examples wherein the server diagnostic data generation system is configured to, receive the problem indication and the correlation ID, to use the cache interaction logic to access the memory. 
     Example 5 is the computing system of any or all previous examples wherein the server diagnostic data generation system is configured to use the interaction logic to access the memory and obtain an indication of the client request from the cache memory based on the correlation ID and generate the server diagnostic data based on the indication of the client request. 
     Example 6 is the computing system of any or all previous examples wherein the server diagnostic data generation system is configured to generate the server diagnostic data by re-submitting the client request obtained from the memory, to the service execution logic. 
     Example 7 is the computing system of any or all previous examples wherein the memory comprises a cache memory that is configurable to vary retention of the client request. 
     Example 8 is a computer implemented method, comprising: 
     receiving, with at least one processor, a client request generated by a client computing system; 
     generating, with service execution logic implemented by the at least one processor, a response to the client request; 
     generating, with correlation identifier (ID) generator logic, a correlation ID corresponding to the client request; 
     sending the response and the correlation ID to the client computing system; 
     generating, with the at least one processor, a memory record that includes the client request and the corresponding correlation ID for storage in a memory; 
     receiving, at a server diagnostic data generation system, the correlation ID and a problem indication indicating that a problem is detected, with respect to the response, at the client computing system; 
     generating, with the server diagnostic data generation system, server diagnostic data based on the request stored in the memory with the corresponding correlation ID; and 
     sending the server diagnostic data to a support data storage system, along with the corresponding correlation ID. 
     Example 9 is the computer implemented method of any or all previous examples wherein receiving a client request comprises: 
     exposing an interface, with a front end system, that receives the client request from the client computing system. 
     Example 10 is the computer implemented method of any or all previous examples wherein generating the response comprises: 
     receiving, at a back end system, an indication of the client request from the front end system; and 
     performing a service operation with the back end system, based on the client request, to generate the response. 
     Example 11 is the computer implemented method of any or all previous examples wherein generating server diagnostic data comprises: 
     accessing the memory record based on the problem indication and the correlation ID to obtain the client request, from the memory, corresponding to the correlation identifier. 
     Example 12 is the computer implemented method of any or all previous examples wherein generating the server diagnostic data comprises: 
     receiving an indication of the client request from the memory; and 
     generating the server diagnostic data based on the indication of the client request. 
     Example 13 is the computer implemented method of any or all previous examples generating the server diagnostic data comprises: 
     re-submitting the client request obtained from the memory, to the service execution logic. 
     Example 14 is the computer implemented method of any or all previous examples wherein generating a memory record comprises: 
     generating the memory record for storage in a cache memory that is configurable to vary retention of the memory record. 
     Example 15 is a computer implemented method, comprising: 
     receiving server diagnostic data generated by a first service computing system based on a client request received from a client computing system, the client request having a corresponding correlation identifier; 
     receiving client diagnostic data and the correlation identifier from a client computing system, the client diagnostic data being generated by the client computing system based on the client request and a response from the first service computing system; 
     storing the server diagnostic data from the first service computing system and the client diagnostic data in a data store, indexed by the correlation identifier; and 
     exposing an interface that receives an access request from a support computing system and providing access to the stored server diagnostic data and the client diagnostic data to the support computing system based on the correlation identifier and the access request. 
     Example 16 is the computer implemented method of any or all previous examples and further comprising: 
     receiving server diagnostic data generated by a second service computing system, and the correlation identifier, based on the client request received, at the first service computing system, from the client computing system; and 
     storing the server diagnostic data from the second service computing system in the data store, indexed by the correlation identifier. 
     Example 17 is the computer implemented method of any or all previous examples wherein exposing an interface comprises: 
     exposing the interface that receives the access request from the support computing system and providing access to the stored server diagnostic data from the first service computing system, the server diagnostic data from the second computing system and the client diagnostic data to the support computing system based on the correlation identifier and the access request. 
     Example 18 is a computer implemented method, comprising: 
     sending a service request to a service computing system; 
     receiving a response to the service request, from the service computing system, along with a correlation identifier corresponding to the service request; 
     detecting a support trigger; 
     sending a support notification to the server computing system along with the correlation identifier based on the detected support trigger; 
     generating client diagnostic data based on the service request and the response; 
     and 
     sending the client diagnostic data and the correlation identifier to a support data storage system. 
     Example 19 is the computer implemented method of any or all previous examples wherein detecting a support trigger comprises: 
     detecting user actuation of a support request actuator on a user interface. 
     Example 20 is the computer implemented method of any or all previous examples wherein detecting a support trigger comprises: 
     detecting an automatically generated support trigger.