Testing networked system using abnormal node failure

Techniques for testing a networked system using simulated abnormal node failure are disclosed. In some embodiments, a computer system performs operations comprising: repeatedly transmitting simulated requests to a networked system on which a software application is implemented using a plurality of nodes, the networked system being configured to respond to the simulated requests using the plurality of nodes; randomly selecting one or more nodes from the plurality of nodes; terminating the randomly selected one or more nodes; restarting the terminated randomly selected one or more nodes; repeating the randomly selecting one or more nodes, the terminating the randomly selected one or more nodes, and the restarting the terminated randomly selected one or more nodes until each one of the plurality of nodes has been terminated and restarted at least once during the first period of time; and determining response times of the networked system in responding to the simulated requests.

BACKGROUND

Networked systems, such as cloud computing systems, often suffer from system failure. Current solutions, such as load testing, only address predictable failures, thereby failing to test a networked system's ability to handle random abnormal failures. As a result, random abnormal failures continue to interrupt the functioning of networked systems. In addition to the issues discussed above, other technical problems may arise as well.

DETAILED DESCRIPTION

Example methods and systems for testing a networked system using simulated abnormal node failure are disclosed. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of example embodiments. It will be evident, however, to one skilled in the art that the present embodiments can be practiced without these specific details.

The implementation of the features disclosed herein involves a non-generic, unconventional, and non-routine operation or combination of operations. By applying one or more of the solutions disclosed herein, some technical effects of the system and method of the present disclosure are to provide a computer system that is specially-configured to test a networked system using simulated abnormal node failure. In some example embodiments, the computer system repeatedly transmits simulated requests to a networked system on which a software application is implemented using a plurality of nodes, with the networked system being configured to respond to the simulated requests using the plurality of nodes. The computer system may randomly select one or more nodes from the plurality of nodes. In some example embodiments, during a period of time when the simulated requests are repeatedly being transmitted to the networked system, the computer system terminates the randomly selected one or more nodes, and then restarts the terminated randomly selected one or more nodes. The computer system may repeat the randomly selecting one or more nodes, the terminating the randomly selected one or more nodes, and the restarting the terminated randomly selected one or more nodes until each one of the plurality of nodes has been terminated and restarted at least once during the period of time.

In some example embodiments, for each one of the simulated requests transmitted during the period of time, the computer system may determine a corresponding response time of the networked system in responding to the simulated request, which may then be stored in a database and subsequently used in performing a function to address vulnerabilities of the networked system to random abnormal failures. As a result of the features disclosed herein, the resiliency and recoverability of the networked system is significantly improved, since the response of networked system to random abnormal failures is tested, resulting in resilience and recoverability data that can be analyzed to detect and resolve technical deficiencies. Other technical effects will be apparent from this disclosure as well.

The methods or embodiments disclosed herein may be implemented as a computer system having one or more modules (e.g., hardware modules or software modules). Such modules may be executed by one or more hardware processors of the computer system. In some example embodiments, a non-transitory machine-readable storage device can store a set of instructions that, when executed by at least one processor, causes the at least one processor to perform the operations and method steps discussed within the present disclosure.

The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and benefits of the subject matter described herein will be apparent from the description and drawings, and from the claims.

FIG.1is an example network diagram illustrating a system100. A platform (e.g., machines and software), in the example form of an enterprise application platform112, provides server-side functionality, via a network114(e.g., the Internet) to one or more clients.FIG.1illustrates, for example, a client machine116with programmatic client118(e.g., a browser), a small device client machine122with a small device web client120(e.g., a browser without a script engine), and a client/server machine117with a programmatic client119.

Turning specifically to the enterprise application platform112, web servers124and Application Program Interface (API) servers125can be coupled to, and provide web and programmatic interfaces to, application servers126. The application servers126can be, in turn, coupled to one or more database servers128that facilitate access to one or more databases130. The web servers124, API servers125, application servers126, and database servers128can host cross-functional services132. The cross-functional services132can include relational database modules to provide support services for access to the database(s)130, which includes a user interface library136. The application servers126can further host domain applications134. The web servers124and the API servers125may be combined.

The cross-functional services132provide services to users and processes that utilize the enterprise application platform112. For instance, the cross-functional services132can provide portal services (e.g., web services), database services, and connectivity to the domain applications134for users that operate the client machine116, the client/server machine117, and the small device client machine122. In addition, the cross-functional services132can provide an environment for delivering enhancements to existing applications and for integrating third-party and legacy applications with existing cross-functional services132and domain applications134. In some example embodiments, the system100comprises a client-server system that employs a client-server architecture, as shown inFIG.1. However, the embodiments of the present disclosure are, of course, not limited to a client-server architecture, and could equally well find application in a distributed, or peer-to-peer, architecture system.

FIG.2is a block diagram illustrating example enterprise applications and services in an enterprise application platform112. The enterprise application platform112can include cross-functional services132and domain applications134. The cross-functional services132can include portal modules140, database modules142(e.g., relational database modules), connector and messaging modules144, API modules146, and development modules148.

The portal modules140can enable a single point of access to other cross-functional services132and domain applications134for the client machine116, the small device client machine122, and the client/server machine117. The portal modules140can be utilized to process, author and maintain web pages that present content (e.g., user interface elements and navigational controls) to the user. In addition, the portal modules140can enable user roles, a construct that associates a role with a specialized environment that is utilized by a user to execute tasks, utilize services, and exchange information with other users within a defined scope. For example, the role can determine the content that is available to the user and the activities that the user can perform. The portal modules140include a generation module, a communication module, a receiving module and a regenerating module. In addition, the portal modules140can comply with web services standards and/or utilize a variety of Internet technologies including JAVA®, J2EE, SAP's Advanced Business Application Programming Language (ABAP®) and Web Dynpro, XML, JCA, JAAS, X.509, LDAP, WSDL, WSRR, SOAP, UDDI and MICROSOFT® .NET®.

The database modules142can provide support services for access to the database(s)130, which includes a user interface library136. The database modules142can provide support for object relational mapping, database independence, and distributed computing. The database modules142can be utilized to add, delete, update, and manage database elements. In addition, the database modules142can comply with database standards and/or utilize a variety of database technologies including SQL, SQLDBC, Oracle, MySQL, Unicode, JDBC, or the like.

The connector and messaging modules144can enable communication across different types of messaging systems that are utilized by the cross-functional services132and the domain applications134by providing a common messaging application processing interface. The connector and messaging modules144can enable asynchronous communication on the enterprise application platform112.

The API modules146can enable the development of service-based applications by exposing an interface to existing and new applications as services. Repositories can be included in the platform as a central place to find available services when building applications.

The development modules148can provide a development environment for the addition, integration, updating, and extension of software components on the enterprise application platform112without impacting existing cross-functional services132and domain applications134.

Turning to the domain applications134, a customer relationship management application150can enable access to and can facilitate collecting and storing of relevant personalized information from multiple data sources and business processes. Enterprise personnel that are tasked with developing a buyer into a long-term customer can utilize the customer relationship management applications150to provide assistance to the buyer throughout a customer engagement cycle.

Enterprise personnel can utilize financial applications152and business processes to track and control financial transactions within the enterprise application platform112. The financial applications152can facilitate the execution of operational, analytical, and collaborative tasks that are associated with financial management. Specifically, the financial applications152can enable the performance of tasks related to financial accountability, planning, forecasting, and managing the cost of finance.

Human resource applications154can be utilized by enterprise personnel and business processes to manage, deploy, and track enterprise personnel. Specifically, the human resource applications154can enable the analysis of human resource issues and facilitate human resource decisions based on real-time information.

Product life cycle management applications156can enable the management of a product throughout the life cycle of the product. For example, the product life cycle management applications156can enable collaborative engineering, custom product development, project management, asset management, and quality management among business partners.

Supply chain management applications158can enable monitoring of performances that are observed in supply chains. The supply chain management applications158can facilitate adherence to production plans and on-time delivery of products and services.

Third-party applications160, as well as legacy applications162, can be integrated with domain applications134and utilize cross-functional services132on the enterprise application platform112.

FIG.3is a block diagram illustrating an example testing system300, in accordance with some example embodiments. In some embodiments, the testing system300comprises any combination of one or more of a request module310, a termination module320, an analysis module330, and one or more database(s)340. The request module310, a termination module320, an analysis module330, and the database(s)340can reside on a computer system, or other machine, having a memory and at least one processor (not shown). In some embodiments, the request module310, a termination module320, an analysis module330, and the database(s)340are incorporated into the enterprise application platform112inFIGS.1and2. However, it is contemplated that other configurations of the request module310, a termination module320, an analysis module330, and the database(s)340are also within the scope of the present disclosure.

In some example embodiments, one or more of the request module310, the termination module320, and the analysis module330are configured to provide a variety of user interface functionality, such as generating user interfaces, interactively presenting user interfaces to the user, receiving information from the user (e.g., interactions with user interfaces), and so on. Presenting information to the user can include causing presentation of information to the user (e.g., communicating information to a device with instructions to present the information to the user). Information may be presented using a variety of means including visually displaying information and using other device outputs (e.g., audio, tactile, and so forth). Similarly, information may be received via a variety of means including alphanumeric input or other device input. In some example embodiments, one or more of the request module310, the termination module320, and the analysis module330are configured to receive user input. For example, one or more of the request module310, the termination module320, and the analysis module330, can present one or more graphical user interface (GUI) elements (e.g., drop-down menu, selectable buttons, text field) with which a user can submit input. In some example embodiments, one or more of the request module310, the termination module320, and the analysis module330are configured to perform various communication functions to facilitate the functionality described herein, such as by communicating with a computing device (e.g., the small device client machine122, the client machine116, or the client/server machine117) via the network114using a wired or wireless connection.

In some example embodiments, the testing system300is communicatively coupled to a networked system350. For example, the testing system300may communicate with the networked system350via a network (e.g., via the network114inFIG.1). The networked system350may comprise any computer system that is configured to respond to requests transmitted to it via a network connection. In some example embodiments, a software application is implemented on the networked system350using a plurality of nodes360(e.g., NODE360-1, . . . , NODE360-N inFIG.3, where N is an integer greater than 1). For example, the plurality of nodes360may be used by the networked system350to implement the enterprise application platform112ofFIGS.1and2. In some example embodiments, the networked system350comprises a load balancer370that distributes a set of tasks over the plurality of nodes360with the aim of making their overall processing more efficient. Each node360may comprise any physical or virtual computing component that is capable of processing and responding to a request.

The networked system350may comprise a microservices architecture on which one or more software applications are implemented using the plurality of nodes360. A microservices architecture is a variant of the service-oriented architecture (SOA) structural style that arranges a software application as a collection of loosely coupled services. In a microservices architecture, services are fine-grained and the protocols are lightweight.

In some example embodiments, the request module310is configured to repeatedly transmit simulated requests to a networked system350during a first period of time. For example, the request module310may continuously transmit requests for a specific amount of time, with an interval amount of time between each transmission. In some example embodiments, the request module310comprises a controller node (not shown) and multiple worker nodes (not shown). The controller node may provide instructions to the worker nodes on transmitting the simulated requests to the networked system350, and the worker nodes may transmit the simulated requests to the networked system350based on the instructions provided by the controller node.

In some example embodiments, a user may configure a test plan to be used by the request module310in transmitting the simulated requests to the networked system350. For example, the testing system300may provide graphical user interface elements with which the user can interact with via a computing device on which the graphical user interface elements are displayed, thereby enabling the user to configure the test plan via the graphical user interface elements, such as by entering or selecting test configuration options. Examples of test configuration options for the test plan that may be configured by the user include, but are not limited to, a length of the period of time for the repeated transmission of simulated requests (e.g., repeatedly transmit simulated requests for 2 hours), a total number of simulated requests to transmit during the period of time, a ramp-up/ramp-down schedule for increasing and/or decreasing the number of simulated requests to transmit during the period of time, an interval amount of time between each transmission, one or more types of request to be used in the simulated requests, a number of worker nodes to be used in transmitting the simulated requests, and a number of threads to be used in transmitting the simulated requests. Other types of test configuration options for the test plan are also within the scope of the present disclosure.

In some example embodiments, each request transmitted by the request module310comprises a transaction, and the networked system350implements transaction processing. Transaction processing is information processing that is divided into individual, indivisible operations called transactions, where each transaction must succeed or fail as a complete unit, as opposed to being only partially complete. In addition to or as an alternative to transactions and transaction processing, the request module310may transmit other types of requests and the networked system350may implement other types of processing. In some example embodiments, the requests may comprise requests for the performance of functions of one or more enterprise applications or services, such as any function discussed above with respect to the enterprise application platform112(e.g., the cross-functional services132, the domain applications134inFIGS.1and2).

In some example embodiments, the termination module320is configured to randomly select one or more nodes360from the plurality of nodes360. The termination module320may use a random number generator to randomly select the node(s)360. In some example embodiments, during a first period of time, such as a period of time defined by the test plan, the termination module320terminates the randomly selected node(s)360, and then restarts the terminated randomly selected node(s)360. The termination module720may terminate the randomly selected node(s)360by communicating one or more instructions to the networked system350to terminate (e.g., shut down) the randomly selected node(s)360. For example, the termination module720may transmit the instruction(s) to terminate the randomly selected node(s)360to a cluster controller on the networked system350. The cluster controller may comprise any component configured to control the plurality of nodes360. The cluster controller may terminate the randomly selected node(s)360in response to receiving the instruction(s) from the termination module320. Other ways of terminating the randomly selected node(s)360are also within the scope of the present disclosure.

Similarly, the termination module720may restart the randomly selected node(s)360by communicating one or more instructions to the networked system350to restart the randomly selected node(s)360that has been shut down. For example, the termination module720may transmit the instruction(s) to restart the randomly selected node(s)360to a cluster controller on the networked system350. The cluster controller may restart the randomly selected node(s)360in response to receiving the instruction(s) from the termination module320. Alternatively, the networked system350may automatically restart terminated nodes360as part of a predefined logic configured for handling failure events. Other ways of restarting the randomly selected node(s)360are also within the scope of the present disclosure.

The termination module320may repeat the terminating and restarting of randomly selected nodes360until each one of the plurality of nodes360has been terminated and restarted at least once during the period of time. In some example embodiments, the termination module320performs the random selection of the nodes360in real-time for each iteration of the terminating and restarting cycle. Alternatively, the termination module320may determine the random selection of the nodes360for each iteration of the terminating and restarting cycle ahead of time, such as by performing the random selection of the nodes360for a massive amount of iterations of the terminating and restarting cycle prior to the first termination, and then storing the sequence of random selections for subsequent use during the performance of the terminating and restarting cycle. For example, the termination module320may first perform one-thousand iterations of node selection (e.g., nodes1and3randomly selected in the first iteration, nodes2and14randomly selected in the second iteration, nodes2and41randomly selected in the third iteration, . . . , nodes8and37randomly selected in the one-thousandth iteration), store a record of the one-thousand sets of node selections, and then use the stored node selections in order during the iterations of the terminating and restarting cycles.

Each repeated iteration of the terminating and restarting operations, as well as each iteration of the random selection operation (if the random selection operation is performed in real-time as part of the cycle rather than being performed ahead of time prior to the start of the cycle), may be performed immediately after the previous iteration of these operations in response to the completion of the previous iteration of these operations. Alternatively, the termination module320may wait an interval amount of time, such as a few seconds, between performing each repeated iteration of these operations. The interval amount of time between each repeated iteration may be uniform for each repeated iteration. Alternatively, the interval amount of time between each repeated iteration may be randomly determined using a random number generator in order to provide additional unpredictability to the simulated node terminations, thereby improving the simulation of abnormal failure.

The number of times the terminating and restarting operations are repeated may depend on the total number of nodes360in the plurality of nodes360, as well as on the way in which the nodes360are randomly selected by the termination module320. In example embodiments in which the termination module320is configured to randomly select one node360for each iteration, use a list of the plurality of nodes360from which the nodes360are randomly selected, and remove nodes360from the list when the nodes360are randomly selected, the number of times that the terminating and restarting operations are repeated is equal to the total number of nodes360in the plurality of nodes360. For example, if the total number of nodes360in the plurality of nodes360is ten, and the termination module320randomly selects and removes one node360from the list of the plurality of nodes360, then the terminating and restarting operations may be performed ten times. Alternatively, in example embodiments in which the termination module320keeps the nodes360that have been randomly selected in the list and available for subsequent random selection, the termination module320may randomly select the same node360from the plurality of nodes360more than once (e.g., nodes1and3randomly selected in the first iteration, nodes2and3randomly selected in the second iteration, nodes2and41randomly selected in the third iteration, etc.), thereby making the number of times that the terminating and restarting operations are repeated unpredictable. As a result of the termination module320being configured to potentially select the same node360from the plurality of nodes360more than once, the termination module320provides additional unpredictability to the simulated node terminations, thereby improving the simulation of abnormal failure.

In some example embodiments, the termination module320is configured to randomly select fewer than the total number of nodes360in the plurality of nodes360. For example, in a scenario where the total number of nodes360in the plurality of nodes is ten, the termination module320limit the number of nodes360that are randomly selected for each iteration to nine, thereby ensuring that at least one node360in the plurality of nodes360is available for use without interruption.

In some example embodiments, the analysis module330is configured to determine a corresponding response time of the networked system350in responding to each one of the simulated requests transmitted during the period of time. For example, in determining a corresponding response time for a simulated request, the analysis module330may determine a time at which the simulated request was transmitted from the testing system300to the networked system350, which may be set and stored (e.g., in the database(s)340) by the request module310when the simulated request is transmitted and retrieved by the analysis module330, and also determine a time at which one of the nodes360of the networked system350responds to the simulated request, which may be determined from metadata of a message sent by the networked system350as a response to the simulated request. The analysis module330may then calculate the response time for the simulated request based on the difference between the time at which the simulated request was transmitted and the time at which the node360responded to the simulated request. Other ways of determining the response time for each simulated request are also within the scope of the present disclosure.

The analysis module330may store the corresponding response times for the simulated requests in the database(s)340. For example, each response time may be stored in the database(s)340in association with an identification of the corresponding simulated request.FIG.4illustrates a table400of example simulated requests and their corresponding response times. In some example embodiments, the corresponding response times are stored in a time series database. However, other types of databases may be used as well.

The testing system300may perform the above-discussed testing operations over multiple different periods of time, using a different test plan for each different period of time. For example, the testing system300may increase the number of nodes that are randomly selected, terminated, and restarted after each period of time, starting with randomly selecting only one node for every iteration used during a first period of time, then randomly selecting two nodes for every iteration used during a second period of time after the first period of time, and then randomly selecting three nodes for every iteration used during a third period of time after the second period of time, and so on and so forth until a specified maximum number of nodes are randomly selected for every iteration of a period of time. The testing system300may implement other differences between different testing periods of time as well, including but not limited to, different durations of time periods, different numbers of simulated requests transmitted, different types of simulated requests transmitted, and different intervals or frequencies used for transmission of simulated requests.

In some example embodiments, the analysis module330is configured to perform a function using at least a portion of the corresponding response times for the simulated requests. The function comprises causing at least a portion of the corresponding response times for the simulated requests to be displayed on a computing device, such as on the client machine116, the small device client machine122, or the client/server machine117inFIG.1. The response times may be displayed in response to user input, such as in response to a user of the computing device selecting one or more user interface elements configured to trigger the display of the response times.

FIG.5illustrates an example graphical user interface (GUI)500in which response times of simulated requests are displayed, in accordance with some example embodiments. In the GUI500, the response times are displayed as part of a time series graph510, such as a line graph of repeated measurements taken over regular time intervals. The GUI500also displays statistics520of the response times for each simulated request, such as a minimum response time, a maximum response time, an average response time, and a current response time. Other data or visualizations based on the response times may also be displayed.

FIG.6illustrates another example GUI600in which response times of simulated requests are displayed. Similar to the GUI500, in the GUI600, the response times are displayed as part of a time series graph. Additionally, in the GUI600, selectable user interface elements610and620are displayed to enable the user to configure the details of the display. For example, the user may use the selectable user interface element610to select a specific simulated request for which to display the corresponding response times, and the user may use the selectable user interface element620to select at what granularity in terms of time for the X-axis of the time series graph to display the corresponding response times.

In some example embodiments, the function performed by the analysis module330comprises analyzing at least a portion of the response times, and then generating a recommendation for modifying the networked system350based on the analysis. For example, the analysis module330may input the response times and their corresponding attributes (e.g., type of simulated requests to which the response times correspond, nodes360to which the simulated requests were transmitted, timing of transmission of simulated requests) into a model to generate the recommendation for modifying the networked system350. The recommendation may include, but is not limited to, a modification to the architecture of the networked system350, a removal of specific nodes360from being used in servicing certain requests, an addition of a specific number of nodes360to be used in servicing certain requests, and a change of code being executed on the networked system350. Other types of recommendations may be generated and recommended by the analysis module330as well.

In some example embodiments, the model used by the analysis module330to generate a recommendation for modifying the networked system350may be configured to determine the type of recommendation based on the response times and their corresponding attributes. For example, the model may be configured to identify the requests for which the corresponding response times are above a predetermined threshold response time (e.g., a response time above which is identified as being a response failure) as corresponding to a response failure, and then, use those identified requests corresponding to a response failure to determine whether the networked system350has a capacity problem or a code problem based on whether the identified requests include multiple types of requests above a threshold number of types of requests. In one example, the model may determine that the networked system350has a capacity problem in response to a determination that the identified requests include multiple types of requests above a threshold number of types of requests (e.g., when the networked system350experienced a response failure for all types of requests), and the model may determine that the networked system350has a code problem in response to a determination that the identified requests do not include above a threshold number of types of requests (e.g., when the networked system350experience a response failure for only one particular type of request, but responded within a sufficient amount of time for other types of requests). The model may be configured to recommend the addition of nodes360in response to a determination that the networked system350has a capacity problem.

The model may also be configured to recommend a change of code executed on the networked system350in response to a determination that the networked system350has a code problem. In some example embodiments, the model comprises a machine-learned model that is trained to find patterns in the code associated with the failed responses to the identified requests. For example, the machine-learned model may be trained using training data that includes examples of code that are each labeled as examples of a code problem. The example of code may also be labeled with an indication of a specific type of problem, such as the length of the code being too long. The trained machine-learned model may then be applied to the code associated with the failed responses to the identified requests (e.g., the code of the nodes360that were tasked with processing the identified requests) to generate an output that identifies whether or not the code is the cause of response failure. The trained machine-learned model may generate an output that identifies the specific type of problem with the code, such as the length of the code being too long, as previously discussed.

In some example embodiments, the generated recommendation is displayed on the computing device of the user. Additionally or alternatively, the analysis module330may automatically modify the networked system350based on the analysis of the response times. For example, the analysis module330may determine whether a trigger condition has been satisfied by the response times, and then perform the recommended modification based on a determination that the trigger condition has been satisfied. One example of a trigger condition comprises a measure of the response times exceeding a threshold value (e.g., if the average response time exceeds a threshold response time value). Other types of trigger conditions are also within the scope of the present disclosure.

Although the testing system300is illustrated inFIG.3as being external to the networked system350, in some example embodiments, one or more components of the testing system300may be partially or fully implemented on the networked system350. For example, the testing system300may deploy the request module310and the termination module320as agents onto each one of the plurality of nodes360. Other configurations of the components of the testing system300are also within the scope of the present disclosure.

FIG.7is a flowchart700illustrating an example method of testing a networked system using simulated abnormal node failure. The method700can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processing device), or a combination thereof. In one example embodiment, one or more of the operations of the method700are performed by the testing system300ofFIG.3or any combination of one or more of its components (e.g., the request module310, the termination module320, the analysis module330).

At operation710, the testing system300, during a first period of time, repeatedly transmits simulated requests to the networked system350on which a software application is implemented using the plurality of nodes360. The networked system350may be configured to respond to the simulated requests using the plurality of nodes360. The software application may be implemented on a microservices architecture of the networked system350. However, other implementations of the software application are also within the scope of the present disclosure.

The testing system300may randomly select one or more nodes360from the plurality of nodes360, at operation720. In some example embodiments, the testing system300may randomly select the node(s)360during the first period of time. Alternatively. the testing system300may randomly select the node(s)360prior to the first period of time. In some example embodiments, for each random selection of one or more nodes360during the first period of time, a total number of the randomly selected one or more nodes360is less than a total number of the plurality of nodes360.

Next, at operation730, the testing system300may terminate the randomly selected node(s)360. The testing system730may terminate the randomly selected node(s)360by communicating one or more instructions to the networked system350to terminate (e.g., shut down) the randomly selected node(s)360. Other ways of terminating the randomly selected node(s)360are also within the scope of the present disclosure.

Subsequently, the testing system300may restart the terminated randomly selected node(s)360, at operation740. The termination module720may restart the randomly selected node(s)360by communicating one or more instructions to the networked system350to restart the randomly selected node(s)360that has been shut down. The networked system350may automatically restart terminated nodes360as part of a predefined logic configured for handling failure events. Other ways of restarting the randomly selected node(s)360may also be employed by the testing system300.

At operation750, the testing system300determines whether each one of the plurality of nodes360has been terminated and restarted at least once during the first period of time (e.g., during the repeated transmission of the simulated requests). If the testing system300determines that not all of the plurality of nodes360have been terminated and restarted at least once during the first period of time, then the method700returns to operation720, where the testing system300repeats another iteration of randomly selecting one or more node(s)360.

If the testing system300determines, at operation750, that each one of the plurality of nodes360has been terminated and restarted at least once during the first period of time, then the testing system300may proceed to determining, for each one of the simulated requests transmitted during the first period of time, a corresponding response time of the networked system350in responding to the simulated request, at operation760, and then storing the corresponding response times for the simulated requests in the database(s)340, at operation770. In some example embodiments, the method may return to operation710to repeatedly transmit simulated requests to the networked system350during a another period of time subsequent to the first period of time according to a different test plan than used in the previous iteration(s) of operation710during the previous period(s) of time or a different part of the same test plan, and then proceed operations720to770based on the repeated transmission of simulated requests during the new recent period of time.

At operation780, the testing system300may perform a function using at least a portion of the corresponding response times for the simulated requests. In some example embodiments, the function comprises causing the portion of the corresponding response times for the simulated requests to be displayed on a computing device. For example, the testing system300may display the response times using the GUI500ofFIG.5or the GUI600ofFIG.6. Other functions are also within the scope of the present disclosure.

FIG.8is a flowchart illustrating an example method800of performing a function using response times for simulated requests. The method800can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processing device), or a combination thereof. In one example embodiment, one or more of the operations of the method800are performed by the testing system300ofFIG.3or any combination of one or more of its components (e.g., the request module310, the termination module320, the analysis module330).

At operation810, the testing system300analyzes the portion of the corresponding response times. Next, the testing system300may generate a recommendation for modifying the networked system350based on the analysis of the response times, at operation820. For example, the testing system300may use any of the features discussed above with respect to the analysis module330to analyze the response times and generate the recommendation for modifying the networked system350. Then, at operation830, the testing system300may cause the generated recommendation to be displayed on a computing device.

FIG.9is a flowchart illustrating another example method900of performing a function using response times for simulated request. The method900can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processing device), or a combination thereof. In one example embodiment, one or more of the operations of the method900are performed by the testing system300ofFIG.3or any combination of one or more of its components (e.g., the request module310, the termination module320, the analysis module330).

At operation910, the testing system300analyzes the response times. Then, the testing system300may modify the networked system350based on the analysis of the response times, at operation920. For example, the testing system300may use any of the features discussed above with respect to the analysis module330to analyze the response times and modify the networked system350.

Example 1 includes a computer-implemented method performed by a computer system having a memory and at least one hardware processor, the computer-implemented method comprising: during a first period of time, repeatedly transmitting simulated requests to a networked system on which a software application is implemented using a plurality of nodes, the networked system being configured to respond to the simulated requests using the plurality of nodes; during the first period of time: randomly selecting one or more nodes from the plurality of nodes; terminating the randomly selected one or more nodes; restarting the terminated randomly selected one or more nodes; and repeating the randomly selecting one or more nodes, the terminating the randomly selected one or more nodes, and the restarting the terminated randomly selected one or more nodes until each one of the plurality of nodes has been terminated and restarted at least once during the first period of time; for each one of the simulated requests transmitted during the first period of time, determining a corresponding response time of the networked system in responding to the simulated request; storing the corresponding response times for the simulated requests in a database; and performing a function using at least a portion of the corresponding response times for the simulated requests.

Example 2 includes the computer-implemented method of example 1, wherein the software application is implemented on a microservices architecture of the networked system.

Example 3 includes the computer-implemented method of example 1 or example 2, wherein for each random selection of one or more nodes during the first period of time, a total number of the one or more nodes is less than a total number of the plurality of nodes.

Example 4 includes the computer-implemented method of any one of examples 1 to 3, further comprising: during a second period of time after the first period of time, repeatedly transmitting additional simulated requests to the networked system, the networked system being configured to respond to the additional simulated requests using the plurality of nodes; during the second period of time: randomly selecting one or more nodes from the plurality of nodes; terminating the randomly selected one or more nodes; restarting the terminated randomly selected one or more nodes; and repeating the randomly selecting one or more nodes, the terminating the randomly selected one or more nodes, and the restarting the terminated randomly selected one or more nodes until each one of the plurality of nodes has been terminated and restarted at least once during the second period of time; for each one of the additional simulated requests transmitted during the second period of time, determining a corresponding response time of the networked system in responding to the additional simulated request; and storing the corresponding response times for the additional simulated requests in the database, wherein for each random selection of one or more nodes during the first period of time, a total number of the one or more nodes randomly selected is a first number, wherein for each random selection of one or more nodes during the second period of time, a total number of the one or more nodes randomly selected is a second number that is greater than the first number, and wherein the function is performed using the corresponding response times for the simulated requests transmitted during the first period of time and the corresponding response times for the additional simulated requests transmitted during the second period of time.

Example 5 includes the computer-implemented method of any one of examples 1 to 4, wherein the function comprises causing the at least a portion of the corresponding response times for the simulated requests to be displayed on a computing device.

Example 6 includes the computer-implemented method of any one of examples 1 to 5, wherein the function comprises: analyzing the at least a portion of the corresponding response times; generating a recommendation for modifying the networked system based on the analyzing the at least a portion of the corresponding response times; and causing the generated recommendation to be displayed on a computing device.

Example 7 includes the computer-implemented method of any one of examples 1 to 6, wherein the function comprises: analyzing the at least a portion of the corresponding response times; and modifying the networked system based on the analyzing the at least a portion of the corresponding response times.

Example 8 includes a system comprising: at least one processor; and a non-transitory computer-readable medium storing executable instructions that, when executed, cause the at least one processor to perform the method of any one of examples 1 to 7.

Example 9 includes a non-transitory machine-readable storage medium, tangibly embodying a set of instructions that, when executed by at least one processor, causes the at least one processor to perform the method of any one of examples 1 to 7.

Example 10 includes a machine-readable medium carrying a set of instructions that, when executed by at least one processor, causes the at least one processor to carry out the method of any one of examples 1 to 7.

The example computer system1000includes a processor1002(e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory1004, and a static memory1006, which communicate with each other via a bus1008. The computer system1000may further include a graphics or video display unit1010(e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system1000also includes an alphanumeric input device1012(e.g., a keyboard), a user interface (UI) navigation (or cursor control) device1014(e.g., a mouse), a storage unit (e.g., a disk drive unit)1016, an audio or signal generation device1018(e.g., a speaker), and a network interface device1020.

The storage unit1016includes a machine-readable medium1022on which is stored one or more sets of data structures and instructions1024(e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions1024may also reside, completely or at least partially, within the main memory1004and/or within the processor1002during execution thereof by the computer system1000, the main memory1004and the processor1002also constituting machine-readable media. The instructions1024may also reside, completely or at least partially, within the static memory1006.

Each of the features and teachings disclosed herein can be utilized separately or in conjunction with other features and teachings to provide a system and method for blind spot implementation in neural networks. Representative examples utilizing many of these additional features and teachings, both separately and in combination, are described in further detail with reference to the attached figures. This detailed description is merely intended to teach a person of skill in the art further details for practicing certain aspects of the present teachings and is not intended to limit the scope of the claims. Therefore, combinations of features disclosed above in the detailed description may not be necessary to practice the teachings in the broadest sense, and are instead taught merely to describe particularly representative examples of the present teachings.

The example methods or algorithms presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems, computer servers, or personal computers may be used with programs in accordance with the teachings herein, or it may prove convenient to construct a more specialized apparatus to perform the method steps disclosed herein. The structure for a variety of these systems will appear from the description herein. It will be appreciated that a variety of programming languages may be used to implement the teachings of the disclosure as described herein.