Patent Publication Number: US-2023164042-A1

Title: Component-based risk evaluation techniques using processing flow signatures

Description:
RELATED APPLICATIONS 
     The present application claims priority under 35 U.S.C. § 119 to U.S. Provisional Pat. Application Number 63/264,339 filed on Nov. 19, 2021, the disclosure of which is hereby incorporated by reference as if entirely set forth herein. 
    
    
     BACKGROUND 
     Technical Field 
     This disclosure relates generally to computer system reliability, and more particularly to component-based risk evaluation techniques that utilize processing flow signature values. 
     Description of the Related Art 
     A server system may provide various services (e.g., web services) in which the computing resources of the server system perform computing operations on behalf of a requesting entity, such as an end user. A given service may be made up of many individual computing operations that may be performed for an end user. In performing a given one of these computing operations, the server system may use a processing flow that utilizes a combination of many different components. These components may be shared by multiple different processing flows to support the various computing operations. Accordingly, as the server system services a request, the associated processing flow may utilize a combination of many different components to generate the desired result for the user. In some instances, however, this component-based approach may present various technical challenges, particularly as the number of components utilized by the server system increases. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram illustrating an example server system that utilizes a set of components, according to some embodiments. 
         FIG.  2    is a block diagram illustrating an example server system generating a flow signature value while implementing a processing flow, according to some embodiments. 
         FIG.  3    is a block diagram illustrating an example flow analysis module, according to some embodiments. 
         FIG.  4    is a block diagram illustrating an example flow prospecting module during a prospecting phase, according to some embodiments. 
         FIG.  5    is a flow diagram illustrating an example method for performing component-based risk evaluation using processing flow signatures, according to some embodiments. 
         FIG.  6    is a block diagram illustrating an example computer system, according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     A server system may provide various services (e.g., web services) in which the computing resources of the server system (including hardware or software elements of the server system) perform computing operations on behalf of a requesting entity, such as an end user. Non-limiting examples of web services a server system may provide include email services, streaming media services, map-based services, online payment services, retail services, etc. 
     A given service may be made up of many individual computing operations (also referred to herein as “experiences”) that may be performed for an end user. Consider, as a non-limiting example, an embodiment in which a server system (e.g., implemented using multiple different, geographically diverse datacenters) provides an online payment service to many users (e.g., millions of users). In this example embodiment, the online payment service may allow end users to perform various computing operations, such as creating a user account, adding funding sources, sending and receiving funds to other user accounts, etc. 
     In performing a given one of these computing operations, the server system may use a processing flow that utilizes a combination of multiple (and, potentially, many) “components.” As used herein, the term “component” refers to a hardware or software element used to perform a portion of the computation used to complete the requested computing operation. Various non-limiting examples of components are described in detail below. In the context of a configuration management system utilized by the server system, a “component,” as used herein, may also be referred to as a “configuration item” (or “CI”), where the configuration management system may use various processes to monitor the status of the individual CI’s in use in the system. In this disclosure, the term “processing flow” refers to sequence of components used by the server system to perform a requested computing operation (e.g., transferring funds between user accounts, as one non-limiting example). 
     A server system may include a large number (e.g., thousands, tens of thousands, etc.) of distinct components. These components may be created by, and shared between, different teams or divisions within the entity operating the server system such that different components are “owned” or managed by different internal entities. In such an embodiment, when designing the processing flows that will be used to perform the computing operations offered, different combinations of components may be selected, used, and shared to support the various computing operations. Accordingly, as the server system services a request, the associated processing flow may utilize a combination of many different components to generate the desired result for the user. Note, however, that there may be changes to any number (e.g., thousands) of these components on a daily basis, for example as part of ongoing software development efforts. Further, in some instances, the number of components used in a given processing flow can be large (e.g., 100 , components, 1,000 components, 10,000 components, etc.). 
     While this component-based approach does facilitate scalability by allowing components to be created, managed, and shared between internal entities and experiences, it also presents various technical problems. For example, the processing flow for a given computing operation may change over time. Consider, as a non-limiting example, a particular component that is used in the processing flow associated with ten different computing operations included in the service provided by the server system. In this example, if there is a change to this particular component, it will affect the ten different processing flows associated with these ten different computing operations. Accordingly, if this change to the particular component negatively impacts the performance of this particular component, the performance of each of these ten different processing flows may also be negatively impacted (or prevented altogether). 
     Using prior techniques, there is little to no visibility about the underlying components included in a given processing flow, the changes made to those components, the performance of those components, the decommissioning of components, etc. Further, using prior techniques, these components may be treated as a group or “batch” (e.g., tier-1 databases) rather than as individual components. Accordingly, it may be difficult or impossible to determine, for a given computing operation, the identity and status of the underlying components included in the processing flow used to perform that given computing operation. Further, using prior approaches, there is no suitable technique that may be used to quantify the risk (e.g., to the server system, the service(s) it provides, the business it supports, etc.) associated with the constant changes to the components in the system. 
     In various embodiments, the disclosed techniques address these technical problems by using component-based risk evaluation techniques that use processing flow signature values to monitor and analyze the processing flows — and therefore the components — utilized by the server system. For example, in various embodiments, the disclosed techniques include assigning some or all of the components within a server system with a corresponding component identifier value. In some embodiments, for example, a component identifier value is an immutable name that uniquely identifies a particular component. As a requested computing operation is performed by the server system, these component identifier values may be used to create a flow identifier value indicative of the sequence (that is, the identity and order) of components included in the particular processing flow used, by the server system, to perform the requested computing operation. Stated differently, as different components in the processing flow are used, the identifier values for these different components are logged and combined (e.g., concatenated) to form a flow identifier value that indicates the sequence of components used by the server system to perform a given processing flow. Various embodiments further include creating a processing flow signature value by performing a hash operation on (that is, computing a hash value from) this flow identifier value. In one non-limiting embodiment, the disclosed techniques store a processing flow signature value, optionally along with one or more other items of information, associated with some or all of the processing flows performed by the server system. Non-limiting examples of additional information that may be logged includes: a flow identifier value corresponding to the flow signature value, a timestamp indicating a time at which the processing flow was performed, a counter indicating the number of times that the processing flow with a particular flow signature value was performed within a given time period, etc. 
     Maintaining these items of information may provide various technical benefits. For example, when a processing flow is completed and a new processing flow signature value generated, that processing flow signature value may be compared to processing flow signature values generated for previous instances of that processing flow. In the event that the new signature value fails to match the previous signature values, the disclosed techniques may determine that there has been a change to one or more of the components included in that processing flow. Once this change has been detected, various embodiments include using the flow identifier values for this processing flow to identify which of the underlying component(s) has changed. Non-limiting examples of changes to the components may include: additions of new components to the processing flow, modification of existing components in the processing flow, and removal or decommissioning of components included in the processing flow. Once the relevant components have been identified (e.g., in a real-time or near real-time manner), further investigation into the nature and extent of the changes to these components can be performed. Accordingly, various disclosed embodiments provide improved visibility into the identity and status of the combination of components included in the various (and, potentially, numerous) processing flows used by the server system. Additional technical benefits provided by various disclosed embodiments are described in more detail below. 
     Referring now to  FIG.  1   , block diagram  100  depicts a server system  110  that includes a set of components  112 A- 112 N (or, collectively, components  112 ), a flow prospecting module  120 , a flow analysis module  130 , and a data store  140 . In various embodiments, server system  110  provides one or more computing resources as part of a service (e.g., a web service) that may be used directly by end users or that may be integrated with (or otherwise used by) services provided by third-parties. As one non-limiting example, server system  110 , in some embodiments, provides an online payment service that may be used by end users to perform online financial transactions (e.g., sending or receiving funds) or utilized by merchants to receive funds from users during financial transactions. Note, however, that this embodiment is described merely as one non-limiting example. In other embodiments, server system  110  may provide any of various suitable services, such as an email service, a streaming media service, etc. Additionally note that, in some embodiments, a “server system” (such as server system  110 ) may be implemented using a single machine. In other embodiments, however, a “server system” may be implemented using multiple machines executing (e.g., at one or more datacenters) for the benefit of a single entity. For example, in some embodiments, server system  110  may be implemented using multiple machines located at one or more geographically remote datacenters. 
       FIG.  1    further includes client device  102  operated by user  106 . Client device  102  may be any of various suitable computing devices, such as a smartphone, laptop computer, desktop computer, tablet computer, etc. that user  106  may use to access the service(s) provided via server system  110 . For example, in various embodiments, client device  102  executes a software application  104 , such as a web browser or a service-specific software application, usable to access one or more computing resources provided by the server system  110 . In the depicted embodiment, user  106  uses client device  102  to send a request  160  to perform a computing operation via a service provided by server system  110 . As a non-limiting example, consider an embodiment in which the requested computing operation specified by request  160  is to check an account balance of a user account of the user  106 . 
     In various embodiments, the server system  110  utilizes different combinations of components  112  to perform the various computing operations available via the service the server system  110  provides. For example, to service request  160 , the server system  110  implements a processing flow that utilizes a sequence of components  112  to perform a series of tasks (or “sub-operations”) necessary to complete the requested computing operation. Non-limiting examples of these tasks could include any of various different computational sub-operations, such as user-verification, risk evaluation, data retrieval, routing, load balancing, etc., that need to be completed in order to accomplish the broader computing operation requested by the user. As a simplified example for the purposes of illustration, assume that the processing flow utilized by the server system  110  uses the following sequence of components  112  to perform the requested computing operation for request  160 : component  112 A,  112 B,  112 D,  112 E, and  112 N. (Note that, in many embodiments, a processing flow may include a sequence of any number (e.g., hundreds, thousands, etc.) of components  112 .) Once the processing flow has completed execution (e.g., by successfully performing the requested operation, through an unexpected termination of the processing flow, etc.), the server system  110  may provide a response  162  to the client device  102 . 
     Non-limiting examples of “components” that may be included in a processing flow include an asset, a container that is running a service, a virtual machine, a third-party library, a physical asset, business logic that is embedded into an application, a Kubernetes cluster, etc. Consider, as a non-limiting example, an instance in which a service (provided by the server system  110 ) runs an API that may be used by requesting entities (e.g., end users, third-party systems, etc.) to send a request to that service. In this example, the service running the API would be considered a “component.” Further, assume that this API has a particular configuration such that it accepts, in a properly formatted API request, a particular set of parameters. This configuration for the API, in various embodiments, will have an associated component identifier value. If that configuration is later changed (e.g., to modify the particular set of attributes included in an API request), that change would result in the assignment of a new identifier value for that component. When the flow signature value (e.g., hash value) for a processing flow that includes this service is later computed, this change to the configuration will result in a change to the signature value. As described herein, that change in signature value may be investigated further to identify the source of this change (the update to the API specification, in the current example). 
     Various embodiments include utilizing identifier values associated with components in the server system to track which components within the infrastructure of the server system  110  are in use for the various processing flows. As one non-limiting example, a processing flow may include the sequence of components  112  (or simply the set of components  112  that have been assigned a component identifier value) used by the server system  110  from the ingress point of a request (e.g., a request  160  from a user  106 ), to a database, through one or more software applications or microservices, to a point of egress at which a response is provided to the user. In various embodiments, the disclosed techniques include generating flow signature values for the processing flows utilized by the server system  110  to service client requests. In some embodiments, for example, the server system  110  generates a flow signature value as a hash value based on a concatenation (or other combination) of the identifier values for the sequence of components in the utilized processing flow. By using the component identifier values for each of the components in a processing flow and using these component identifier values to create the flow signature value, the disclosed techniques are capable of modeling the processing flow for a given computing operation. These flow identifier values and flow signature values may be generated and stored over time for many (or all) of the different processing flows used by the server system  110 , which, in various embodiments, helps to identify any changes in the components  112  included in a processing flow. 
     In various embodiments, the disclosed techniques may be said to operate in two complementary (and, optionally, simultaneous) phases: a prospecting phase, and a reconciliation phase. In various embodiments, the reconciliation phase and prospecting phase may be said to operate as state machines within an overall autonomous system. During the prospecting phase, the flow prospecting module  120  generates flow signature values for the permissible processing flows that are permitted via the server system  110 . That is, flow prospecting module  120  generates flow signature values for the sequences of components  112  that are permitted based on the logic and constraints of the individual components  112 . In various embodiments, the prospecting operations may be repeated (e.g., on a periodic basis) to generate new and updated flow signature values as components are added to or removed from the server system  110 . Flow prospecting module  120  and various embodiments of the prospecting phase are described in detail below with reference to  FIG.  4   . 
     The prospecting phase may be thought of as a “non-production” phase because, in various embodiments, the prospecting operations are performed independent of the requests being received and serviced by the server system  110 . The reconciliation phase, by contrast, may be thought of as a “production phase” because, in various embodiments, the reconciliation operations discussed herein are performed based on the instantiated production environment used by server system  110  to service requests from clients. For example, as a request is serviced, a flow signature value may be created based on the component identifiers for the sequence of components used to service that request. This process of generating flow signature values for the processing flows used by the server system  110  may be performed for all (or any desired subset) of the requests received by the server system  110 . Note that, in embodiments in which server system  110  hosts a large-scale service, server system  110  may receive many (e.g., millions) requests each day. In various embodiments, flow signature values generated either by the flow prospecting module  120  during a prospecting operation, or by the server system  110  while performing computing operations to service requests from requesting users, may be stored as part of flow signature data  144  in data store  140  included in (or accessible to) server system  110 . 
     In various embodiments, the disclosed techniques include analyzing these flow signature values generated during the reconciliation phase, which may provide various technical benefits -particularly in the context of server systems providing large-scale services used by many users. For example, in various embodiments the disclosed techniques improve the ability to monitor the use of, and detect risks associated with, the various (and often numerous) processing flows and components  112  utilized by the server system  110 . 
     Various embodiments of flow analysis module  130  and the disclosed flow analysis techniques are described in detail below with reference to  FIG.  3   . In various embodiments, these flow analysis techniques may provide various technical benefits to the server system  110 . For example, in various embodiments the flow analysis techniques include using the flow signature values to detect changes to the components  112  included in the processing flows used by server system  110 . As described below, this may enable the efficient tracking and monitoring of components  112  utilized in business-critical processing flows such that, when a change is made to one or more components  112 , this change may be detected in a fast and efficient manner. Further, in various embodiments, the flow analysis operations include tracking the number of times that different processing flows are used during a given time interval, which may be particularly useful for tracking changes in “velocity” with respect to the server system  110  as a whole and with respect to specific processing flows. 
     Additionally, in some embodiments, the flow analysis techniques include detecting “unexpected” processing flows performed by the server system  110 . For example, as explained below, the flow analysis module  130  may compare the flow signature values generated during the reconciliation phase to the flow signature values generated during the prospecting phase and, if there is not a match, the processing flow associated with that flow signature value may be deemed “unexpected” and flagged for further investigation. By contrast, using prior techniques it may be difficult or impossible to detect when an “unexpected” processing flow has been used by the system. Further, in various embodiments, the flow analysis techniques include identifying a particular component (or components) as a point-of-failure in a processing flow. For example, as described below, in addition to a flow signature value, in various embodiments each processing flow will also have an associated “return code” (e.g., an HTTP status code) indicative of the outcome of the processing flow that may be used to determine whether a requested operation was successfully performed. If not, various embodiments include using the flow signature value (or the underlying flow identifier value) to identify a component that acted as a point-of-failure for the processing flow. This approach to identifying a point-of-failure component may provide significant technical benefits, for example by allowing a malfunctioning component in a processing flow — out of, for example, thousands of possible components — to be identified in a fast and computationally efficient manner. 
     Referring now to  FIG.  2   , block diagram  200  depicts an embodiment in which server system  110  generates a flow signature value 220 while implementing a processing flow to service a request  160  for a user  106 . 
     As noted above, in various embodiments the server system  110  utilizes different combinations of components  112  to perform computing operations provided via the service that it hosts. The various components  112  used by a particular processing flow may be utilized in a particular sequence so as to perform a series of tasks needed to complete the requested computing operation. As shown in  FIG.  2   , in some embodiments these components  112  may be referred to as “hops” to signify that a component  112  in a processing flow is one step of many taken to complete a requested operation. 
     In various embodiments, each component  112  in the processing flow assists in creating a flow identifier value for that processing flow. For example, in some embodiments the flow identifier value for a particular processing flow is a combination (e.g., a concatenation) of the components  112  used in that processing flow in the order in which the respective components  112  were utilized. For example, in various embodiments, a flow identifier value (e.g., an alphanumeric value) is created by appending the component identifier value (e.g., a unique, immutable name) of each successive component  112  in the processing flow, and a flow signature value 220 is created by taking a hash of the flow identifier value. Accordingly, in various embodiments this flow signature value 220 is specific to the sequence of components  112  used in the corresponding processing flow, and the same flow signature value 220 will be generated each time that same processing flow (that is, the exact same sequence of components  112 ) is used by the server system  110  to service a request. 
     With reference to the non-limiting example shown in  FIG.  2   , for instance, the depicted processing flow first utilizes component  112 A. In some embodiments, once component  112 A completes its task, it may add its unique component identifier value to a flow identifier value for the particular processing flow. In this non-limiting example, the component identifier value for component  112 A would be the first and only identifier value included in the flow identifier value at this point. In various embodiments, the flow identifier value may be passed (e.g., in an HTTP header field, such as a cookie) from component  112  to component  112  in the flow such that each subsequent component  112  can append their respective identifier value to the end (or beginning, in some implementations) of the running flow identifier value. 
     In the depicted embodiment, the last hop in the processing flow is component  112 N. In various embodiments, the final component  112  in a processing flow may both add its identifier value to the flow identifier value and, once completed, generate a flow signature value 220 based on the flow identifier value. In various embodiments, the flow signature value 220 may be a hash value generated by taking a hash of the flow identifier value. As one non-limiting example, in some embodiment the md5 message-digest algorithm may be used to generate a flow signature value 220 based on the flow identifier value. Note, however, this is merely one non-limiting example and, in other embodiments, any suitable hashing algorithm may be used. Further, in other embodiments, the flow signature value 220 may be generated using other suitable encoding techniques (e.g., encoding techniques that do not utilize a hash function). 
     In the depicted embodiment, the flow signature value 220 is stored, along with a return code  230  (e.g., an HTTP status code), in flow results  210 . As an example, for a particular utilization of a processing flow, the flow results  210  may include a corresponding data record that specifies the flow signature value 220 and the associated return code  230  (optionally along with one or more other items of information, e.g., a timestamp, as desired). For example, various embodiments include storing, in flow results  210 , one or more of the flow identifier value, the processing flow signature value 220, the return code  230 , and a timestamp associated with some or all of the processing flows performed by the server system  110  for a given period of time (e.g., week, month, year, etc., or indefinitely). 
     In various embodiments, a flow signature value 220 and return code  230  may be stored in flow results  210  for each (or any desired subset) of the processing flows utilized by the server system  110  for subsequent analysis. For example, as shown in  FIG.  2   , in various embodiments the flow results  210  may be accessed and analyzed by the flow analysis module  130 . Various non-limiting embodiments of flow analysis module  130  are described in detail below with reference to  FIG.  3   . For the purposes of the present discussion, however, note that flow analysis module  130 , in various embodiments, is operable to analyze the flow signature value 220 and return code  230  and, based on that analysis, generate a flow state determination  240 . In various embodiments, this flow state determination  240  may include one or more items of information corresponding to the processing flow, which may be used for various flow analysis techniques described herein, for example to monitor and detect changes in flow velocity, detect unexpected processing flows, and identify point-of-failure components. 
     Referring now to  FIG.  3   , block diagram  300  depicts an embodiment of flow analysis module  130  evaluating flow results  210  associated with the processing flows used by the server system  110  to perform requested computing operations. In the depicted embodiment, flow analysis module  130  is depicted as a state machine that includes state logic  310  and a set of states  320 . (Note that, although only 3 states are explicitly depicted in  FIG.  3    for clarity, this is simply one non-limiting embodiment. In other embodiments, flow analysis module  130  may reach any suitable number of states based on the state logic  310 .) 
     In various embodiments, state logic  310  utilizes flow signature data  144  (e.g., including the flow signature values generated during the prospecting phase) to analyze the flow signature value 220 and return code  230  and generate an appropriate flow state determination  240 . As a non-limiting example, for each result (e.g., stored as a character string formatted as a “flow signature value 220: return code  230 ” value) in flow results  210 , the state logic  310  may split the result such that flow signature value 220 is added to a set “E” (corresponding to “experiences”), the return code  230  is added to a set “RC,” and a counter associated with that particular flow signature value 220 and return code  230  combination is incremented. Further, in some such embodiments the state logic  310  may then evaluate each of the “experiences” in the set E. For example, as described in more detail below, if the return code  230  indicates that a requested operation was not successfully completed, the flow analysis module  130  module may increment a counter associated with that return code  230  and trigger an event-response workflow. If, however, the return code  230  indicates that the requested operation was successful, the flow analysis module  130  may determine whether the flow signature value 220 matches a flow signature value 220 generated during prospecting. If so, an appropriate counter may be incremented to track that occurrence. If not, the flow analysis module  130  may initiate an impact analysis workflow for further investigation. Various non-limiting embodiments of these flow analysis operations are now described in more detail below. 
     For example, as shown in  FIG.  3   , in various embodiments the flow analysis module  130  determine an appropriate flow state determination  240  by determining whether the return code  230  for the processing flow indicates whether the request was successful or not. In some embodiments, for example, the return codes may be HTTP status codes that indicate whether a requested operation specified by a request (e.g., request  160 ) has been successfully completed. In such embodiments, for example, the 2xx class of status codes (e.g., the  200  OK status code) indicates that the request was successful, while the 5xx class of status codes (e.g., the  500  Internal Server Error status code) indicates that the server was unsuccessful in complete the requested operation. 
     If the return code  230  indicates that a processing flow was unsuccessful (indicated by State 3 in  FIG.  3   ), the flow analysis module  130  may generate a flow state determination  240 C indicating that the requested operation was unsuccessful. In the depicted embodiment, for example, this flow state determination  240  may be provided to an event response workflow  330  for further investigation. In various embodiments the flow analysis techniques include identifying a particular component  112  (or components  112 ) as a point-of-failure in a processing flow. For example, if the return code  230  indicates that a requested operation was not successful, the flow analysis module  130  (or another module in server system  110 ) may use the flow signature value 220 to identify a component  112  that caused the processing failure. As a non-limiting example, while servicing a request, one of the components  112  in the processing flow may be malfunctioning, causing the processing flow to fail to complete the requested computing operation. In some such embodiments, the disclosed techniques may stop appending component identifier values to the flow identifier value at a point of failure such that, when a processing flow fails, the disclosed techniques may identify the final component  112  specified in the flow identifier value as a point of failure for the processing flow. As another non-limiting example, if a particular processing flow for a particular computing operation is intended to include a sequence of 40 different components  112  and the flow identifier value indicates that the flow stopped at the 15 th  component — component 112 M —this component 112 M may be identified as a potential source of the failure. In various embodiments, this may enable problematic components  112  to be identified and remedied more quickly than using prior techniques, thereby improving the operation of the server system as a whole. 
     If, however, the return code  230  indicates that the requested operation was successfully performed, the flow analysis module  130  may determine an appropriate flow state determination  240  by comparing a flow signature value 220 to the flow signature data  144  to determine whether the flow signature value 220 matches a flow signature value 220 generated during the prospecting operations. If there is such a match (indicated by State 1 in  FIG.  3   ), that flow signature value 220 may be said to be “expected” because it matches a flow signature value for a processing flow that was deemed to be permissible by the flow prospecting module  120  during prospecting. In  FIG.  3   , for example, in response to determining both that the return code  230  indicates the requested operation was successful and that the flow signature value 220 matches a flow signature value previously generated during the prospecting operation, the flow analysis module  130  may generate a flow state determination  240 A indicative of this result. As noted above, in various embodiments the flow analysis operations include tracking the number of times that different processing flows are used during a given time interval. For example, in  FIG.  3   , based on a determination that the processing flow was successful (based on return code  230 ) and that the flow signature value 220 was “expected,” the flow analysis module  130  increments a counter associated with the flow signature value 220 that tracks the number of times that flow signature value 220 was generated during a particular time period. 
     In various embodiments, the flow analysis module  130  may be operable to detect “unexpected” processing flows performed by the server system  110 . For example, if there is not a match between the flow signature value 220 and any of the flow signature values generated during prospecting (indicated by State 2 in  FIG.  3   ), that flow signature value 220 may be said to be “unexpected” because it does not match a flow signature value for a processing flow that was deemed to be permissible by the flow prospecting module  120  during prospecting. In  FIG.  3   , for example, in response to determining that the return code  230  indicates the requested operation was successful and that the flow signature value 220 does not match a flow signature value 220 previously generated during the prospecting operation, the flow analysis module  130  may generate a flow state determination  240 B indicative of this result. In the depicted embodiment, for example, this flow state determination  240  may be provided to an experience impact analysis  325  for further investigation. For example, this unexpected processing flow could correspond to a malicious operation that was successfully performed via the server system  110 , or the unexpected processing flow could simply correspond to a computing operation that was not yet discovered during the prospecting phase. By autonomously identifying and flagging these processing flows for further investigation, the disclosed techniques enable for the fast and computationally efficient resolution of potential problems as those problems arise. Further note that, in various embodiments, the flow analysis operations also include tracking the number of times that “unexpected” processing flows are detected. For example, in  FIG.  3   , the flow analysis module  130  increments a counter associated with the “unexpected” flow signature value 220 to track the number of times that this flow signature value 220 was generated during a particular time period. 
     Information about an occurrence of the flow signature value 220, including the flow signature value 220, the return code  230 , a timestamp, the counter, etc., may be stored as part of flow signature data  144 . Such information may be particularly useful, for example, in detecting changes in velocity associated with various processing flows (both “expected” and “unexpected”) utilized in the server system  110  over time. For example, in some embodiments, the flow analysis module  130  may track the number of times a particular processing flow is used, by the server system  110 , during successive time intervals and identify changes in that processing flow’s use over time. This information may be valuable on its own, providing the organization operating the server system  110  with insight regarding how frequently the different processing flows are used and how that use changes over time. 
     Further, in some embodiments, the flow signature values 220 or velocity information may be used to detect changes in processing flows or the respective components  112  included therein. For example, in some embodiments, the components  112  included in a given processing flow may change and, when this change occurs, there will be a resulting change in the flow signature value 220 for that processing flow. By monitoring the flow signature values 220, the disclosed techniques facilitate the detection of changes in processing flows (e.g., components added to the flow, components removed from the flow, changes in the sequence of components in the flow, changes to the configuration of components in the flow, or any combination thereof). Accordingly, once a change in the flow signature value 220 has been detected, one may determine that there has been a change in the underlying processing flow, which may be investigated further. In various embodiments, detecting and identifying changes in a processing flow based on a change in its signature value is both less error-prone and less computationally demanding than other available techniques (e.g., comparing the list of component identifiers for the components included in the processing flow). 
     In some embodiments, the server system  110  may maintain (or have access to) a list (implemented using any suitable data structure or collection) that maps a component identifier value to a corresponding component  112 . For example, this operation may be performed by component system of record  402  described below with reference to  FIG.  4   . In various embodiments, once it has been determined that there has been a change to the processing flow (based on a change in the associated flow signature value 220), various embodiments include determining which of the component(s)  112  in that processing flow have changed. For example, in some embodiments, this determining includes comparing the flow identifier value (e.g., the set of concatenated component identifier values) for the current instance of the processing flow to the flow identifier value for a previous instance (e.g., the most-recent previous instance) of the processing flow to determine how these values differ. Once the changed component(s)  112  have been identified, the extent of those changes may be investigated. For example, a change management system may be consulted to determine what changes were made to a given component  112  in the flow. 
     Various embodiments further allow the entity operating the server system  110  to define thresholds used to determine whether a change is significant enough to warrant further investigation or action (that is, if the risk posed by this change is deemed “acceptable”). For example, in some embodiments, certain important or business-critical components  112  or processing flows may be flagged (e.g., within the data structure providing the mapping of component identifier value to corresponding component  112 ) such that, when a change is detected in these processing flows or components  112 , an indication of this change may be generated. 
     Note that, although only three states  320  are explicitly shown in  FIG.  3   , this non-limiting embodiment is depicted merely as an example and, in other embodiments, state logic  310  may reach one or more additional states based on the analysis of the flow signature values 220 and return codes  230 . For example, in some embodiments one or more additional states  320  may leverage volumetric differences between the active cycle (e.g., a current time period) and results from one or more previous cycles (e.g., flow signature values 220 and return codes  230  generated during previous time periods or during prospecting). As a non-limiting example, in some embodiments the flow analysis operations may detect changes in velocity associated with one or more processing flows, where “velocity” refers to the number of times a processing flow is utilized by the server system  110  during a given cycle. Note that, in various embodiments, these other states  320  (e.g., states beyond the three depicted in  FIG.  3   ) may add determination for reoccurrence, flapping states, and anomalous availability or security results of the flow signature values 220. 
     Further note that, although the flow analysis module  130  is shown as part of the reconciliation phase in the production environment, this depicted embodiment is provided as merely one non-limiting example. In other embodiments, some or all of the flow analysis operations described herein may be performed in a non-production environment (e.g., as part of the prospecting phase). 
     Referring now to  FIG.  4   , block diagram  400  depicts a flow prospecting module  120  during a prospecting phase, according to some embodiments. In various embodiments, the flow prospecting module  120  generates flow signature values 220 for the permissible processing flows that are permitted via the server system  110 . 
     In the embodiment depicted in  FIG.  4   , block diagram  400  includes a component system of record  402 , which, in various embodiments, is a data management system that acts as an authoritative source for data relating to the components  112  utilized by the server system  110 . In various embodiments, through continued development efforts within an organization (e.g., by various software development or business teams within a company), both logical and IT components  112  may be added to the server system  110 . In  FIG.  4   , for example, business users  404  within the organization may develop logical components  112  and IT users  406  within the company may develop IT components  112 . As these components  112  are created and on-boarded, unique, immutable component identifier values  410  for the components  112  may be added to the component system of record  402 . That is, in various embodiments, as a component  112  is added to the server system  110 , a unique, immutable component identifier  410 B may be added to the component system of record  402  for that component  112 . 
     In various embodiments, the flow prospecting module  120  is operable to compute flow signature values for the processing flows that are permitted, using different combinations of the components  112 , via the server system  110 . That is, in various embodiments the flow prospecting module  120  is a software agent that computes flow signature values 220 for any possible flow that is allowed by the configuration deployed by the users  204  or  206  of the organization operating the server system  110 . For example, in some embodiments the flow prospecting module  120  takes the configuration information provided by the human users  204  or  206  and applies automation to identify every permitted sequence of components  112 , generating a flow signature value 220 for each such sequence. In various embodiments, each flow signature value may be said to represent a corresponding itemized capability of the server system  110  and the sum of all of its constituent components  112  in sequential order. 
     In various embodiments, this process may be autonomously repeated such that, as the component system of record  402  is updated, new corresponding flow signature values 220 are created. That is, in various embodiments the flow prospecting module  120  is constantly running and generating a (potentially very large) list of flow signature values 220 (e.g., as hash values) indicative of the permissible processing flows that are possible via the components  112  in the server system  110 . In some embodiments, these flow signature values 220 may be stored in tables (or in any other suitable data-storage format) and may be used, for example, by the flow analysis module  130 , for example as described above with reference to  FIG.  3   . 
     As shown in  FIG.  4   , in various embodiments the prospecting phase receives feedback from the flow analysis module  130 . In various embodiments, the flow analysis module  130  provides the flow state determinations  240  for subsequent use in the prospecting phase or for use in subsequent flow analysis operations. For example, in various embodiments the flow signature data  144  may be used to store counter information associated with the different flow signature values 220 for expected processing flows. Further, as depicted in  FIG.  4   , information relating to “unexpected” processing flows may be utilized by the system. For example, in  FIG.  4   , flow state determination  240 B corresponding to an “unexpected” processing flow may be provided to a remediation or certification workflow  420  for further analysis. In some embodiments, the output of this remediation or certification workflow  420  may be utilized, for example, by business users  404  to revise aspects of one or more components  112  (e.g., logical components) used by the server system  110 . 
     Example Methods 
     Referring now to  FIG.  5   , a flow diagram illustrating an example method  500  for performing component-based risk evaluation using processing flow signatures is depicted, according to some embodiments. In various embodiments, method  500  may be performed by server system  110  of  FIG.  1    to generate a flow signature value 220 based on a sequence of components  112  used, by the server system  110 , to perform a requested computing operation. For example, server system  110  may include (or have access to) a non-transitory, computer-readable medium having program instructions stored thereon that are executable by the server system  110  (e.g., by one or more computer systems included in the server system  110 ) to cause the operations described with reference to  FIG.  5   . In  FIG.  5   , method  500  includes elements  502 - 512 . While these elements are shown in a particular order for ease of understanding, other orders may be used. In various embodiments, some of the method elements may be performed concurrently, in a different order than shown, or may be omitted. Additional method elements may also be performed as desired. 
     At  502 , in the illustrated embodiment, the server system  110  provides a service that is usable to perform various computing operations for requesting users  106 . As described above, in various embodiments the server system  110  includes a set of (potentially numerous) components  112  that have corresponding unique component identifier values. In various embodiments, different combinations of the components  112  are usable to perform the various computing operations included in the service hosted by server system  110 . 
     At  504 , in the illustrated embodiment, the server system  110  receives, from a client device  102 , a request to perform a particular computing operation. As a non-limiting example in embodiments in which the server system  110  hosts an online payment service, the requested computing operation may be to transfer funds from one user account to another. At  506 , in the illustrated embodiment, the server system  110  performs the particular computing operation via a particular processing flow. In various embodiments, the processing flow includes a particular sequence of components  112  performing a series of tasks that are associated with the particular computing operation. 
     At  508 , in the illustrated embodiment, the server system  110  generates a particular flow signature value 220 for the particular processing flow. In  FIG.  5   , element  508  includes sub-elements  510 - 512 . Note, however, that this embodiment is provided merely as one non-limiting example and, in other embodiments, generating the particular flow signature value for the particular processing flow may include additional or different sub-elements than those shown in  FIG.  5   . At  510 , in the illustrated embodiment, the server system  110  generates a flow identifier value for the particular processing flow by combining component identifier values for the particular sequence of components used to perform the series of tasks. At  512 , in the illustrated embodiment, the server system  110  performs a hash operation based on the flow identifier value to generate the particular flow signature value. As a non-limiting example, in some embodiments the flow signature value is generated as an md5 hash value, though any suitable hashing technique may be used, as desired. 
     As noted above, various disclosed embodiments include performing flow analysis operations based on the flow signature values generated by server system  110 . In various embodiments, method  500  may include one or more such flow analysis operations. For example, in some embodiments, method  500  includes determining that the particular flow signature value for the particular processing flow matches a previously generated flow signature value (e.g., generated during a prospecting phase) corresponding to a permissible processing flow that is permitted by the server system  110  and, in response, incrementing a counter associated with the particular flow signature value. In such embodiments, the counter may indicate the number of times, during a particular time period, that the particular processing flow was utilized by the server system  110 . 
     Further, in various embodiments, method  500  includes flow analysis operations related to tracking the “velocity” associated with one or more processing flows utilized by the server system  110 . For example, in some embodiments method  500  includes the server system  110  generating, during a first time period, a plurality of instances of the particular flow signature value while servicing repeated requests, from a plurality of users, to perform the particular computing operation, and, for each of the plurality of instances, incrementing a counter indicating a number of times, during the first time period, that the particular processing flow was utilized by the server system. In such embodiments, method  500  may further include comparing the first counter to a second counter associated with the particular flow signature value, where the second counter indicates a number of times, during a second time period, that the particular processing flow was utilized by the server system  110  and, based on the comparing, detecting a change in frequency of the particular processing flow between the first and second time periods. 
     Additionally, in various embodiments, method  500  includes determining that a particular flow signature value does not match any signature values included in a list of previously generated (e.g., during a prospecting phase) flow signature values corresponding to permissible processing flows that are permitted by the server system  110 . For example, as described above, in some embodiments the flow analysis module  130  may determine that a processing flow is “unexpected” in response to the corresponding flow signature value 220 not matching any of the flow signature values generated by the flow prospecting module  120  during its prospecting operations. 
     In some embodiments, method  500  includes detecting when a requested operation was not successfully performed. For example, in some embodiments, method  500  includes detecting, based on a return code  230  associated with the particular flow signature value 220, an unsuccessful outcome of the particular computing operation and, in response to the detecting, identifying a particular component, of the particular sequence of components, as a point of failure for the particular processing flow. In some embodiments, for instance, identifying the particular component includes determining a final component identifier value included in the flow identifier value for the particular processing flow and, using this final component identifier value, identifying a final component in the particular sequence of components used in the particular processing flow. 
     Further, as noted above, in various embodiments method  500  includes performing various prospecting operations (e.g., by a flow prospecting module  120 ). In some such embodiments, method  500  includes accessing a set of component identifier values associated with a set of components utilized by the server system  110  to provide the service and generating a list of flow signature values corresponding to permissible processing flows that are permitted using the different combinations of the plurality of components in the server system. In some such embodiments, method  500  further includes autonomously generating the updated list of flow signature values on a periodic basis, including by accessing an updated set of identifier values associated with an updated set of components utilized by the server system to provide the service and generating a corresponding updated list of flow signature values corresponding to permissible processing flows that are permitted using different combinations of the updated set of components. 
     Example Computer System 
     Referring now to  FIG.  6   , a block diagram of an example computer system  600  is depicted, which may implement one or more computer systems, such as server system  110  (or one or more computer systems included in server system  110 ) of  FIG.  1   , according to various embodiments. Computer system  600  includes a processor subsystem  620  that is coupled to a system memory  640  and I/O interfaces(s)  660  via an interconnect  680  (e.g., a system bus). I/O interface(s)  660  is coupled to one or more I/O devices  670 . Computer system  600  may be any of various types of devices, including, but not limited to, a server computer system, personal computer system, desktop computer, laptop or notebook computer, mainframe computer system, server computer system operating in a datacenter facility, tablet computer, handheld computer, workstation, network computer, etc. Although a single computer system  600  is shown in  FIG.  6    for convenience, computer system  600  may also be implemented as two or more computer systems operating together. 
     Processor subsystem  620  may include one or more processors or processing units. In various embodiments of computer system  600 , multiple instances of processor subsystem  620  may be coupled to interconnect  680 . In various embodiments, processor subsystem  620  (or each processor unit within  620 ) may contain a cache or other form of on-board memory. 
     System memory  640  is usable to store program instructions executable by processor subsystem  620  to cause system  600  perform various operations described herein. System memory  640  may be implemented using different physical, non-transitory memory media, such as hard disk storage, floppy disk storage, removable disk storage, flash memory, random access memory (RAM-SRAM, EDO RAM, SDRAM, DDR SDRAM, RAMBUS RAM, etc.), read only memory (PROM, EEPROM, etc.), and so on. Memory in computer system  600  is not limited to primary storage such as system memory  640 . Rather, computer system  600  may also include other forms of storage such as cache memory in processor subsystem  620  and secondary storage on I/O devices  670  (e.g., a hard drive, storage array, etc.). In some embodiments, these other forms of storage may also store program instructions executable by processor subsystem  620 . 
     I/O interfaces  660  may be any of various types of interfaces configured to couple to and communicate with other devices, according to various embodiments. In one embodiment, I/O interface  660  is a bridge chip (e.g., Southbridge) from a front-side to one or more back-side buses. I/O interfaces  660  may be coupled to one or more I/O devices  670  via one or more corresponding buses or other interfaces. Examples of I/O devices  670  include storage devices (hard drive, optical drive, removable flash drive, storage array, SAN, or their associated controller), network interface devices (e.g., to a local or wide-area network), or other devices (e.g., graphics, user interface devices, etc.). In one embodiment, I/O devices  670  includes a network interface device (e.g., configured to communicate over Wi-Fi, Bluetooth, Ethernet, etc.), and computer system  600  is coupled to a network via the network interface device. 
     The present disclosure includes references to an “embodiment” or groups of “embodiments” (e.g., “some embodiments” or “various embodiments”). Embodiments are different implementations or instances of the disclosed concepts. References to “an embodiment,” “one embodiment,” “a particular embodiment,” and the like do not necessarily refer to the same embodiment. A large number of possible embodiments are contemplated, including those specifically disclosed, as well as modifications or alternatives that fall within the spirit or scope of the disclosure. 
     This disclosure may discuss potential advantages that may arise from the disclosed embodiments. Not all implementations of these embodiments will necessarily manifest any or all of the potential advantages. Whether an advantage is realized for a particular implementation depends on many factors, some of which are outside the scope of this disclosure. In fact, there are a number of reasons why an implementation that falls within the scope of the claims might not exhibit some or all of any disclosed advantages. For example, a particular implementation might include other circuitry outside the scope of the disclosure that, in conjunction with one of the disclosed embodiments, negates or diminishes one or more the disclosed advantages. Furthermore, suboptimal design execution of a particular implementation (e.g., implementation techniques or tools) could also negate or diminish disclosed advantages. Even assuming a skilled implementation, realization of advantages may still depend upon other factors such as the environmental circumstances in which the implementation is deployed. For example, inputs supplied to a particular implementation may prevent one or more problems addressed in this disclosure from arising on a particular occasion, with the result that the benefit of its solution may not be realized. Given the existence of possible factors external to this disclosure, it is expressly intended that any potential advantages described herein are not to be construed as claim limitations that must be met to demonstrate infringement. Rather, identification of such potential advantages is intended to illustrate the type(s) of improvement available to designers having the benefit of this disclosure. That such advantages are described permissively (e.g., stating that a particular advantage “may arise”) is not intended to convey doubt about whether such advantages can in fact be realized, but rather to recognize the technical reality that realization of such advantages often depends on additional factors. 
     Unless stated otherwise, embodiments are non-limiting. That is, the disclosed embodiments are not intended to limit the scope of claims that are drafted based on this disclosure, even where only a single example is described with respect to a particular feature. The disclosed embodiments are intended to be illustrative rather than restrictive, absent any statements in the disclosure to the contrary. The application is thus intended to permit claims covering disclosed embodiments, as well as such alternatives, modifications, and equivalents that would be apparent to a person skilled in the art having the benefit of this disclosure. 
     For example, features in this application may be combined in any suitable manner. Accordingly, new claims may be formulated during prosecution of this application (or an application claiming priority thereto) to any such combination of features. In particular, with reference to the appended claims, features from dependent claims may be combined with those of other dependent claims where appropriate, including claims that depend from other independent claims. Similarly, features from respective independent claims may be combined where appropriate. 
     Accordingly, while the appended dependent claims may be drafted such that each depends on a single other claim, additional dependencies are also contemplated. Any combinations of features in the dependent that are consistent with this disclosure are contemplated and may be claimed in this or another application. In short, combinations are not limited to those specifically enumerated in the appended claims. 
     Where appropriate, it is also contemplated that claims drafted in one format or statutory type (e.g., apparatus) are intended to support corresponding claims of another format or statutory type (e.g., method). 
     Because this disclosure is a legal document, various terms and phrases may be subject to administrative and judicial interpretation. Public notice is hereby given that the following paragraphs, as well as definitions provided throughout the disclosure, are to be used in determining how to interpret claims that are drafted based on this disclosure. 
     References to a singular form of an item (i.e., a noun or noun phrase preceded by “a,” “an,” or “the”) are, unless context clearly dictates otherwise, intended to mean “one or more.” Reference to “an item” in a claim thus does not, without accompanying context, preclude additional instances of the item. A “plurality” of items refers to a set of two or more of the items. 
     The word “may” is used herein in a permissive sense (i.e., having the potential to, being able to) and not in a mandatory sense (i.e., must). 
     The terms “comprising” and “including,” and forms thereof, are open-ended and mean “including, but not limited to.” 
     When the term “or” is used in this disclosure with respect to a list of options, it will generally be understood to be used in the inclusive sense unless the context provides otherwise. Thus, a recitation of “x or y” is equivalent to “x or y, or both,” and thus covers 1) x but not y, 2) y but not x, and 3) both x and y. On the other hand, a phrase such as “either x or y, but not both” makes clear that “or” is being used in the exclusive sense. 
     A recitation of “w, x, y, or z, or any combination thereof” or “at least one of ... w, x, y, and z” is intended to cover all possibilities involving a single element up to the total number of elements in the set. For example, given the set [w, x, y, z], these phrasings cover any single element of the set (e.g., w but not x, y, or z), any two elements (e.g., w and x, but not y or z), any three elements (e.g., w, x, and y, but not z), and all four elements. The phrase “at least one of ... w, x, y, and z” thus refers to at least one element of the set [w, x, y, z], thereby covering all possible combinations in this list of elements. This phrase is not to be interpreted to require that there is at least one instance of w, at least one instance of x, at least one instance of y, and at least one instance of z. 
     Various “labels” may precede nouns or noun phrases in this disclosure. Unless context provides otherwise, different labels used for a feature (e.g., “first circuit,” “second circuit,” “particular circuit,” “given circuit,” etc.) refer to different instances of the feature. Additionally, the labels “first,” “second,” and “third” when applied to a feature do not imply any type of ordering (e.g., spatial, temporal, logical, etc.), unless stated otherwise. 
     The phrase “based on” or is used to describe one or more factors that affect a determination. This term does not foreclose the possibility that additional factors may affect the determination. That is, a determination may be solely based on specified factors or based on the specified factors as well as other, unspecified factors. Consider the phrase “determine A based on B.” This phrase specifies that B is a factor that is used to determine A or that affects the determination of A. This phrase does not foreclose that the determination of A may also be based on some other factor, such as C. This phrase is also intended to cover an embodiment in which A is determined based solely on B. As used herein, the phrase “based on” is synonymous with the phrase “based at least in part on.” 
     The phrases “in response to” and “responsive to” describe one or more factors that trigger an effect. This phrase does not foreclose the possibility that additional factors may affect or otherwise trigger the effect, either jointly with the specified factors or independent from the specified factors. That is, an effect may be solely in response to those factors, or may be in response to the specified factors as well as other, unspecified factors. Consider the phrase “perform A in response to B.” This phrase specifies that B is a factor that triggers the performance of A, or that triggers a particular result for A. This phrase does not foreclose that performing A may also be in response to some other factor, such as C. This phrase also does not foreclose that performing A may be jointly in response to B and C. This phrase is also intended to cover an embodiment in which A is performed solely in response to B. As used herein, the phrase “responsive to” is synonymous with the phrase “responsive at least in part to.” Similarly, the phrase “in response to” is synonymous with the phrase “at least in part in response to.” 
     Within this disclosure, different entities (which may variously be referred to as “units,” “circuits,” other components, etc.) may be described or claimed as “configured” to perform one or more tasks or operations. This formulation-[entity] configured to [perform one or more tasks]-is used herein to refer to structure (i.e., something physical). More specifically, this formulation is used to indicate that this structure is arranged to perform the one or more tasks during operation. A structure can be said to be “configured to” perform some task even if the structure is not currently being operated. Thus, an entity described or recited as being “configured to” perform some task refers to something physical, such as a device, circuit, a system having a processor unit and a memory storing program instructions executable to implement the task, etc. This phrase is not used herein to refer to something intangible. 
     In some cases, various units/circuits/components may be described herein as performing a set of task or operations. It is understood that those entities are “configured to” perform those tasks/operations, even if not specifically noted. 
     The term “configured to” is not intended to mean “configurable to.” An unprogrammed FPGA, for example, would not be considered to be “configured to” perform a particular function. This unprogrammed FPGA may be “configurable to” perform that function, however. After appropriate programming, the FPGA may then be said to be “configured to” perform the particular function. 
     For purposes of United States patent applications based on this disclosure, reciting in a claim that a structure is “configured to” perform one or more tasks is expressly intended not to invoke 35 U.S.C. § 112(f) for that claim element. Should Applicant wish to invoke Section 112(f) during prosecution of a United States patent application based on this disclosure, it will recite claim elements using the “means for” [performing a function] construct. 
     “In this disclosure, various “modules” operable to perform designated functions are shown in the figures and described in detail. As used herein, a “module” refers to software or hardware that is operable to perform a specified set of operations. A module may refer to a set of software instructions that are executable by a computer system to perform the set of operations. A module may also refer to hardware that is configured to perform the set of operations. A hardware module may constitute general-purpose hardware as well as a non-transitory computer-readable medium that stores program instructions, or specialized hardware such as a customized ASIC.