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WO2021243342A1 - Recommandation d'action pour une défaillance d'application - Google Patents

Recommandation d'action pour une défaillance d'application Download PDF

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Publication number
WO2021243342A1
WO2021243342A1 PCT/US2021/040149 US2021040149W WO2021243342A1 WO 2021243342 A1 WO2021243342 A1 WO 2021243342A1 US 2021040149 W US2021040149 W US 2021040149W WO 2021243342 A1 WO2021243342 A1 WO 2021243342A1
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WO
WIPO (PCT)
Prior art keywords
failure
application
client device
event data
processor
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2021/040149
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English (en)
Inventor
Shiva BANSAL
Sandip BRAHMACHARY
Rupesh Vikram DEORE
Sarang Sudhakar SONAWANE
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Hewlett Packard Development Co LP
Original Assignee
Hewlett Packard Development Co LP
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Filing date
Publication date
Application filed by Hewlett Packard Development Co LP filed Critical Hewlett Packard Development Co LP
Publication of WO2021243342A1 publication Critical patent/WO2021243342A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0751—Error or fault detection not based on redundancy
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0793—Remedial or corrective actions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3466—Performance evaluation by tracing or monitoring
    • G06F11/3476—Data logging
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
    • G06F2201/86—Event-based monitoring
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
    • G06F2201/865—Monitoring of software
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models

Definitions

  • Computing systems utilize various types of applications to perform operations. During the operations, an application may fail without explanation.
  • FIG. 1 illustrates a block diagram of a computing system for recommending an action for an application failure, according to an example
  • FIG. 2 illustrates a flow diagram of a process to recommend an action for an application failure, according to an example
  • FIG. 3 illustrates a block diagram of a non-transitory storage medium storing machine-readable instructions to recommend an action for an application failure, according to an example
  • FIG. 4 illustrates an operational architecture of a system for recommending an action for an application failure, according to another example
  • FIG. 5 illustrates a sequence diagram for a process to generate a recommended action for an application failure, according to another example.
  • FIG. 6 is a block diagram illustrating a system to recommend action for an application failure, according to another example.
  • Computing systems include applications which have been installed for a variety of operations. While running an application, performance issues and sudden application crashes may occur which may lead to data loss and loss of productivity for users interacting with the application. If the cause for the performance issue is not resolved, the application may continue to crash. While logs of code may be maintained for the application operations, determining a solution for the cause of the performance issue may take time and a manual parsing of code. Therefore, it is described, a system, method, and computer readable medium to analyze event logs and extract patterns of code corresponding to application failures. The code may be analyzed using a Natural Language Process (NLP) algorithm. Once the pattern of code associated with the error has been identified, the system provides a recommended solution to the error. The solution may be a recommendation to upgrade the application, update the operating system, install a patch released by the application, etc.
  • NLP Natural Language Process
  • a system comprises a processor operatively coupled with a computer readable storage medium and instructions stored on the computer readable storage medium that, when executed by the processor, direct the processor to maintain an event log including first event data indicating a first failure of an application by a first client device.
  • the event log includes a recommended action associated with the first failure by the first client device.
  • the instructions direct the processor to receive second event data indicating a second failure of the application by a second client device. Using the event log, the first event data indicating the first failure is compared with the second event data indicating the second failure.
  • the instructions direct the processor to determine the recommended action based on the comparison and provide the recommended action to the second client device.
  • a non-transitory computer readable medium comprises instructions executable by a processor to receive a plurality of failure code blocks for an application by a plurality of client devices. For each failure code block for the application by the plurality of failure code blocks, an application solution is identified to mitigate an application failure. The instructions further direct the processor to monitor for an incoming failure code block for the application from a client device of the plurality of client devices. The instructions then direct the processor to process the incoming failure code block from the client device with the plurality of failure code blocks for application by the plurality of client devices to determine the application solution to mitigate the application failure. The application solution is then communicated to the client device.
  • FIG. 1 illustrates a block diagram of computing system 100 for recommending an action for an application failure, according to an example.
  • Computing system 100 depicts communication interface 102, processor 104, memory 106, and storage medium 108.
  • storage medium 108 may include instructions 110-118 that are executable by processor 104.
  • storage medium 108 can be said to store program instructions that, when executed by processor 104, implement the components of computing system 100.
  • the executable instructions stored in storage medium 108 include, as an example, instructions 110 to maintain an event log of first event data for first application failure and instructions 112 to receive second event data for a second application failure.
  • the executable instructions stored in storage medium 108 also include, as an example, instructions 114 to compare the first and second event data, instructions 116 to determine the recommended action, and instructions 118 to provide the recommended action.
  • Instructions 110 to maintain an event log of first event data for first application failure represent program instructions that when executed by processor 104 cause computing system 100 to maintain an event log including first event data indicating a first failure of an application by a first client device.
  • the event log includes a recommended action associated with the first failure by the first client device.
  • the event log may include a record of captured behavioral changes of a machine, such as occurrences in which an application has failed.
  • the event log may store code patterns for an event (i.e. , an application failure), a proposed solution for the application failure, an application version, a client device model and manufacturer, an operating system version, etc.
  • the event log is maintained in a cloud-based data repository to be ingested by a machine learning system.
  • the event logs may be stored on a cloud-based storage system, a local storage system, or a combination of remote and local storage systems.
  • the event log may include application failure information from a variety of client devices running an application.
  • the event log may maintain code patterns associated with an application crash from a variety of different client devices.
  • the event log information may be received from a client device external to computing system 100 using communication interface 102.
  • Instructions 112 to receive second event data for a second application failure represent program instructions that when executed by processor 104 cause computing system 100 to receive second event data indicating a second failure of the application by a second client device.
  • the second event data may indicate that a new failure of an application has occurred, which has not yet been logged in the event log.
  • the application failure may be new in that the event has not been logged. However, it may not be a new type of failure for an application and other application failures that are similar to the new application failure may have already been logged in the event log. Furthermore, the new failure may not yet have been solved. Therefore, there may not be a solution included with the new application failure.
  • Instructions 114 to compare the first and second event data represent program instructions that when executed by processor 104 cause computing system 100 to compare, using the event log, the first event data indicating the first failure with the second event data indicating the second failure.
  • the code pattern for the failures may be similar enough to compare the code in the event logs and determine when the application failures are similar.
  • Computing system 100 may determine whether the code pattern of the second application failure matches one or more of the code patterns for other application failures maintained in the event log to a threshold level.
  • the code in the event logs may be parsed using a Natural Language Processing (NLP) model.
  • NLP Natural Language Processing
  • processor 104 may compare the first event data with the second event data by using NLP model to compare the first block of code with the second block of code.
  • the instructions may further direct processor 104 to build a machine learning model (or communicate with an external system hosting the machine learning model) with information associated with a plurality of application failures by a plurality of client devices.
  • Instructions 116 to determine the recommended action represent program instructions that when executed by processor 104 cause computing system 100 to determine what previous solutions have been provided to remedy the previous application failure which is similar to the current application failure.
  • the recommended action may include a recommendation to upgrade the application to a new version of the application.
  • the recommended action may include a recommendation to update an operating system installed on the second client device.
  • the recommended action may include a recommendation to install a patch of code into the application used by the second client device.
  • the instructions may further direct the processor to update the event log with the second event data indicating the second failure by the second client device.
  • the event log may be updated with the code pattern for the application failure, as well as the code pattern indicating the recommended solution.
  • Instructions 118 to provide the recommended action represent program instructions that when executed by processor 104 cause computing system 100 to provide the recommended action to the second client device.
  • the recommended action may be communicated to the second client device by providing a pop-up notification indicating the solution to the second application failure.
  • the recommended action may be communicated to the second client device by sending a message outside of the application which has failed, such as an email message to a user account associated with the second client device.
  • the recommended solution may be sent to the operating system of the client device to be implemented without a user input.
  • Storage medium 108 represents any number of memory components capable of storing instructions that can be executed by processor 104. As a result, storage medium 108 may be implemented in a single device or distributed across devices. Likewise, processor 104 represents any number of processors capable of executing instructions stored by storage medium 108.
  • FIG. 2 illustrates a flow diagram of method 200 to recommend an action for an application failure, according to an example.
  • Some or all of the steps of method 200 may be implemented in program instructions in the context of a component or components of an application used to carry out the action recommendation feature.
  • the flow diagram of FIG. 2 shows a specific order of execution, the order of execution may differ from that which is depicted. For example, the order of execution of two of more blocks shown in succession by be executed concurrently or with partial concurrence. All such variations are within the scope of the present disclosure.
  • method 200 maintains an event log in a cloud-based data repository to be ingested by a machine learning system, at 201.
  • the event log includes first event data indicating a first failure by a first client device and a recommended action associated with the first failure by the first client device.
  • Method 200 builds a machine learning model with information associated with the first failure by the first client device, at 202.
  • the machine learning model may further include an NPL model which may parse code blocks included in the first event data indicating the first failure the by first client device.
  • the machine learning model may be implemented on a cloud-based server which may interact with the cloud-based repository to retrieve event log data.
  • the machine learning model may also be built to follow a rule-based approach.
  • the machine learning model may follow the 60- 20-20 rule in which 60% of data will be used for building the model, 20% will be used for validating the model and rectifying the parameters to tune the model to get the improved accuracy, precession, recall other statistical metrics, and the remaining 20% will be used to test the model.
  • the failure code for the machine learning model may be selected based on blocks of code from client devices that may be associated with an application failure.
  • the event data may be identified and stored in the data repository to be ingested by the machine learning model using a Python library or Apache flume.
  • the application solution to mitigate the application failure may be selected based on blocks of code from client devices that may be associated with an application failure.
  • an NLP model may be used to parse the blocks of code to determine a pattern. Once the pattern for the application failure is identified, the event data may be ingested by the machine learning model to identify the application solution to mitigate the failure.
  • Method 200 receives the second event data indicating a second failure by a second client device, at 203.
  • the second event data indicating the second failure by the second client device may be received from a communication interface by the second client device to a cloud platform.
  • the second event data may be extract from second client device by a program running on the second client device. The program may then upload the second event data to the cloud platform for further processing.
  • Method 200 compares, using the machine learning model, the first event data indicating the first failure with the second event data indicating the second failure to determine the recommended action, at 204.
  • the machine learning model may use NLP to parse the program code and determine when a block of code is available to be compared with a block of code from the first failure by the first client device.
  • the recommended action may include an upgrade of the application to a new version of the application, an update of an operating system of the client device, and an installation of a patch of code into the application by the client device.
  • FIG. 3 illustrates a block diagram of non-transitory storage medium 300 storing machine-readable instructions that upon execution cause a system to recommend an action for an application failure, according to an example.
  • Storage medium is non-transitory in the sense that is does not encompass a transitory signal but instead is made up of a memory component configured to store the relevant instructions.
  • the machine-readable instructions include instructions 302 to receive a plurality of failure code blocks for an application by a plurality of client devices.
  • the machine-readable instructions also include instructions 304 to identify, for each failure code block for the application by the plurality of client devices, an application solution to mitigate an application failure.
  • the machine-readable instructions also include instructions 306 to monitor for an incoming failure code block for the application from a client device of the plurality of client devices.
  • the machine-readable instructions include instructions 308 to process the incoming failure code block from the client device with the plurality of failure code blocks for the application by the plurality of client devices to determine the application solution to mitigate the application failure.
  • the machine-readable instructions also include instructions 310 to communicate the application solution to the client device.
  • program instructions 302-310 can be part of an installation package that when installed can be executed by a processor to implement the components of a computing device.
  • non-transitory storage medium 300 may be a portable medium such as a CD, DVD, or a flash drive.
  • Non-transitory storage medium 300 may also be maintained by a server from which the installation package can be downloaded and installed.
  • the program instructions may be part of an application or applications already installed.
  • non-transitory storage medium 300 can include integrated memory, such as a hard drive, solid state drive, and the like.
  • FIG. 4 illustrates an operational architecture of a system for recommending an action for an application failure, according to another example.
  • FIG. 4 illustrates operational scenario 400 that relates to what occurs when event data is stored in a data repository and the recommended action is generated using machine learning algorithms or techniques in a recommendation engine.
  • Operational scenario 400 includes application service 401 , first computing device 402, second computing device 403, data repository 404, and recommendation engine 405.
  • Data repository 404 includes a table indicating logged failures 410, types of failures 411 , solution status 412, and actions 413.
  • Application service 401 is representative of any device capable of running an application natively or in the context of a web browser, streaming an application, or executing an application in any other manner.
  • Examples of application service 401 include, but are not limited to, personal computers, mobile phones, tablet computers, desktop computers, laptop computers, wearable computing devices, or any other form factor, including any combination of computers or variations thereof.
  • Application service 401 may include various hardware and software elements in a supporting architecture suitable for performing process 500.
  • FIG. 6 One such representative architecture is illustrated in FIG. 6 with respect to computing system 601.
  • Application service 401 also includes a software application or application component capable of generating a recommended action in accordance with the processes described herein.
  • the software application may be implemented as a natively installed and executed application, a web application hosted in the context of a browser, a streamed or streaming application, a mobile application, or any variation or combination thereof.
  • first computing device 402 may transfer first event data to application service 401.
  • client devices include any or some combination of the following: a desktop computer, a notebook computer, a tablet computer, a smartphone, a game appliance, a wearable device (e.g., a smart watch, a head-mount device, etc.), or any other type of electronic device.
  • Application service 401 may then transfer the first event data for first computing device 402 to data repository 404.
  • Data repository 404 may be any data structure (e.g., a database, such as a relational database, non-relational database, graph database, etc.), a file, a table, or any other structure which may store a collection of data.
  • recommendation engine 405 is able to generate recommended actions for failures.
  • various failure blocks 410 may be associated with different failure types 411 , such as an operating system failure, local application failure, or online application failure.
  • a status 412 is shown indicating whether there is a known solution to the failure.
  • a recommended action 413 is provided, such as updating an operating system or installing a patch of code.
  • recommendation engine 405 processes the received second event data from application service 401 and the first event data from data repository 404.
  • Recommendation engine 405 may be a rule-based engine which may process a selection of keywords and combinations of keywords to determine a pattern of code which indicates a failure type and an associated recommended action.
  • Recommendation engine 405 may further include a data filtration system which filters the selected keywords and code blocks to determine data which will be used in generating the recommended action.
  • recommendation engine 405 may use a statistical supervised model to filter the data and generate the recommended action. The recommended action is then communicated to second computing device 403 via application service 401 .
  • FIG. 5 illustrates a sequence diagram for process 500 to generate a recommended action for an application failure, according to another example.
  • the sequence diagram illustrates an operation of system 400 to generate a recommended action for mitigating an application failure by a client device using event data stored in a data repository and processed using machine learning techniques in a recommendation engine.
  • application service 401 receives the first event data indicating a first application failure from first computing device 402 and transfers the first event data to data repository 404, at 501.
  • data repository 404 collects and maintains first event data for the first application failure, at 502.
  • application service 401 receives the second event data indicating the second application failure from second computing device 402 and transfers the second event data to recommendation engine 405, at 503.
  • the first event data from the first application failure is retrieved from data repository 404 and transferred to recommendation engine 405 to be processed using machine learning techniques, 504.
  • the blocks of code indicating the application failure may be retrieved from data repository 404.
  • the first event data may also include the recommended action.
  • a failure A may be retrieved which has an operating system type (e.g., type 411), a status as having a known application solution (e.g., status 412), and a recommended action (e.g., action 413), such as an operating system upgrade, an application update, or a suggested patch of code which may be implemented for the application.
  • Recommendation engine 405 then processes the first event data and the second event data to determine a recommended action for the application failure, at 505. Once the recommended action has been determined, the recommended action is transferred to application service 401 , and application service 401 in turn transfers the recommended action to second computing device 403, at 506. In a final operation, data repository 404 is updated with the second event data indicating the second application failure, at 507.
  • FIG. 6 illustrates computing system 601 , which is representative of any system or visual representation of systems in which the various applications, services, scenarios, and processes disclosed herein may be implemented.
  • Examples of computing system 601 include, but are not limited to, server computers, rack servers, web servers, cloud computing platforms, and data center equipment, as well as any other type of physical or virtual server machine, container, and any variation or combination thereof.
  • Other examples may include smart phones, laptop computers, tablet computers, desktop computers, hybrid computers, gaming machines, virtual reality devices, smart televisions, smart watches and other wearable devices, as well as any variation or combination thereof.
  • Computing system 601 may be implemented as a single apparatus, system, or device or may be implemented in a distributed manner as multiple apparatuses, systems, or devices.
  • Computing system 601 includes, but is not limited to, processing system 602, storage system 603, software 605, communication interface system 607, and user interface system 609.
  • Processing system 602 is operatively coupled with storage system 603, communication interface system 607, and user interface system 609.
  • Processing system 602 loads and executes software 605 from storage system 603.
  • Software 605 includes application 606, which is representative of the processes discussed with respect to the preceding FIG.s 1-5, including method 200.
  • software 605 When executed by processing system 602 to enhance an application, software 605 directs processing system 602 to operate as described herein for at least the various processes, operational scenarios, and sequences discussed in the foregoing examples.
  • Computing system 601 may optionally include additional devices, features, or functionality not discussed for purposes of brevity.
  • processing system 602 may comprise a micro processor and other circuitry that retrieves and executes software 605 from storage system 603.
  • Processing system 602 may be implemented within a single processing device but may also be distributed across multiple processing devices or sub systems that cooperate in executing program instructions. Examples of processing system 602 include general purpose central processing units, graphical processing unites, application specific processors, and logic devices, as well as any other type of processing device, combination, or variation.
  • Storage system 603 may comprise any computer readable storage media readable by processing system 602 and capable of storing software 605.
  • Storage system 603 may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Examples of storage media include random access memory, read only memory, magnetic disks, optical disks, flash memory, virtual memory and non-virtual memory, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other suitable storage media, except for propagated signals.
  • Storage system 603 may be implemented as a single storage device but may also be implemented across multiple storage devices or sub-systems co-located or distributed relative to each other.
  • Storage system 603 may comprise additional elements, such as a controller, capable of communicating with processing system 602 or possibly other systems.
  • Software 605 may be implemented in program instructions and among other functions may, when executed by processing system 602, direct processing system 602 to operate as described with respect to the various operational scenarios, sequences, and processes illustrated herein.
  • Software 605 may include program instructions for implementing method 200.
  • the program instructions may include various components or modules that cooperate or otherwise interact to carry out the various processes and operational scenarios described herein.
  • the various components or modules may be embodied in compiled or interpreted instructions, or in some other variation or combination of instructions.
  • the various components or modules may be executed in a synchronous or asynchronous manner, serially or in parallel, in a single threaded environment or multi-threaded, or in accordance with any other suitable execution paradigm, variation, or combination thereof.
  • Software 605 may include additional processes, programs, or components, such as operating system software, virtual machine software, or other application software, in addition to or that include process 606.
  • Software 605 may also comprise firmware or some other form of machine- readable processing instructions executable by processing system 602.
  • software 605 may, when loaded into processing system 602 and executed, transform a suitable apparatus, system, or device (of which computing system 601 is representative) overall from a general-purpose computing system into a special-purpose computing system.
  • encoding software 605 on storage system 603 may transform the physical structure of storage system 603.
  • the specific transformation of the physical structure may depend on various factors in different examples of this description. Such factors may include, but are not limited to, the technology used to implement the storage media of storage system 603 and whether the computer-storage media are characterized as primary or secondary storage, as well as other factors.
  • software 605 may transform the physical state of the semiconductor memory when the program instructions are encoded therein, such as by transforming the state of transistors, capacitors, or other discrete circuit elements constituting the semiconductor memory. A similar transformation may occur with respect to magnetic or optical media. Other transformations of physical media are possible without departing from the scope of the present description, with the foregoing examples provided only to facilitate the present discussion.
  • Communication interface system 607 may include communication connections and devices that allow for communication with other computing systems (not shown) over communication networks (not shown). Examples of connections and devices that together allow for inter-system communication may include network interface cards, antennas, power amplifiers, RF circuitry, transceivers, and other communication circuitry. The connections and devices may communicate over communication media to exchange communications with other computing systems or networks of systems, such as metal, glass, air, or any other suitable communication media. The aforementioned media, connections, and devices are well known and need not be discussed at length here.
  • User interface system 609 may include a keyboard, a mouse, a voice input device, a touch input device for receiving a touch gesture from a user, a motion input device for detecting non-touch gestures and other motions by a user, and other comparable input devices and associated processing elements capable of receiving user input from a user.
  • Output devices such as a display, speakers, haptic devices, and other types of output devices may also be included in user interface system 609. In some cases, the input and output devices may be combined in a single device, such as a display capable of displaying images and receiving touch gestures.
  • the aforementioned user input and output devices are well known in the art and need not be discussed at length here.
  • User interface system 609 may also include associated user interface software executable by processing system 602 in support of the various user input and output devices discussed above.
  • Communication between computing system 601 and other computing systems may occur over a communication network or networks and in accordance with various communication protocols, combinations of protocols, or variations thereof. Examples include intranets, internets, the Internet, local area networks, wide area networks, wireless networks, wired networks, virtual networks, software defined networks, data center buses, computing backplanes, or any other type of network, combination of network, or variation thereof.
  • the aforementioned communication networks and protocols are well known and need not be discussed at length here.
  • FIG.s The functional block diagrams, operational scenarios and sequences, and flow diagrams provided in the FIG.s are representative of example systems, environments, and methodologies for performing novel aspects of the disclosure. While, for purposes of simplicity of explanation, methods included herein may be in the form of a functional diagram, operational scenario or sequence, or flow diagram, and may be described as a series of acts, it is to be understood and appreciated that the methods are not limited by the order of acts, as some acts may, in accordance therewith, occur in a different order and/or concurrently with other acts from that shown and described herein. It should be noted that a method could alternatively be represented as a series of interrelated states or events, such as in a state diagram. Moreover, not all acts illustrated in a methodology may be required for a novel example.
  • examples described may include various components and features. It is also appreciated that numerous specific details are set forth to provide a thorough understanding of the examples. However, it is appreciated that the examples may be practiced without limitations to these specific details. In other instances, well known methods and structures may not be described in detail to avoid unnecessarily obscuring the description of the examples. Also, the examples may be used in combination with each other.

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Abstract

Dans un exemple de mise en œuvre selon certains aspects de la présente invention, un système comprend un processeur fonctionnellement couplé à un support de stockage lisible par ordinateur et des instructions stockées sur le support de stockage lisible par ordinateur lesquelles, lorsqu'elles sont exécutées par le processeur, amènent le processeur à maintenir un journal d'événements comprenant de premières données d'événement indiquant une première défaillance d'une application par un premier dispositif client. Le journal d'événements comprend une action recommandée associée à la première défaillance par le premier dispositif client. Les instructions amènent le processeur à recevoir de secondes données d'événement indiquant une seconde défaillance de l'application par un second dispositif client. Au moyen du journal d'événements, les premières données d'événement indiquant la première défaillance sont comparées aux secondes données d'événement indiquant la seconde défaillance. Les instructions amènent le processeur à déterminer l'action recommandée sur la base de la comparaison et à fournir l'action recommandée au second dispositif client.
PCT/US2021/040149 2020-05-26 2021-07-01 Recommandation d'action pour une défaillance d'application Ceased WO2021243342A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
IN202041022014 2020-05-26
IN202041022014 2020-05-26

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210406114A1 (en) * 2020-06-25 2021-12-30 Hewlett Packard Enterprise Development Lp Method and system for facilitating a self-healing network
US20230359543A1 (en) * 2022-05-06 2023-11-09 Cox Communications, Inc. Systems and methods for proactive service health detection
US11985069B2 (en) 2022-07-31 2024-05-14 Cisco Technology, Inc. Auto-detection of application failures for forecasting network path performance
EP4542396A1 (fr) * 2023-10-17 2025-04-23 Sap Se Recommandation intelligente de projets d'aperçus à travers des applications logicielles et des paysages

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130159500A1 (en) * 2011-12-16 2013-06-20 Microsoft Corporation Discovery and mining of performance information of a device for anticipatorily sending updates to the device
US20180027006A1 (en) * 2015-02-24 2018-01-25 Cloudlock, Inc. System and method for securing an enterprise computing environment
US20180131579A1 (en) * 2016-11-09 2018-05-10 SingeHop, LLC Systems and interface for remotely managing server operations
US20190102244A1 (en) * 2017-09-29 2019-04-04 Apple Inc. Method and system for predicting failure events
US20190286628A1 (en) * 2016-12-08 2019-09-19 Zhejiang Dahua Technology Co., Ltd. Methods and systems for processing log data

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130159500A1 (en) * 2011-12-16 2013-06-20 Microsoft Corporation Discovery and mining of performance information of a device for anticipatorily sending updates to the device
US20180027006A1 (en) * 2015-02-24 2018-01-25 Cloudlock, Inc. System and method for securing an enterprise computing environment
US20180131579A1 (en) * 2016-11-09 2018-05-10 SingeHop, LLC Systems and interface for remotely managing server operations
US20190286628A1 (en) * 2016-12-08 2019-09-19 Zhejiang Dahua Technology Co., Ltd. Methods and systems for processing log data
US20190102244A1 (en) * 2017-09-29 2019-04-04 Apple Inc. Method and system for predicting failure events

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210406114A1 (en) * 2020-06-25 2021-12-30 Hewlett Packard Enterprise Development Lp Method and system for facilitating a self-healing network
US11860724B2 (en) * 2020-06-25 2024-01-02 Hewlett Packard Enterprise Development Lp Method and system for facilitating a self-healing network
US20230359543A1 (en) * 2022-05-06 2023-11-09 Cox Communications, Inc. Systems and methods for proactive service health detection
US12135604B2 (en) * 2022-05-06 2024-11-05 Cox Communications, Inc. Systems and methods for proactive service health detection
US20250013524A1 (en) * 2022-05-06 2025-01-09 Cox Communications, Inc. Systems and methods for proactive service health detection
US11985069B2 (en) 2022-07-31 2024-05-14 Cisco Technology, Inc. Auto-detection of application failures for forecasting network path performance
EP4542396A1 (fr) * 2023-10-17 2025-04-23 Sap Se Recommandation intelligente de projets d'aperçus à travers des applications logicielles et des paysages

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