DESC:FIELD
The present disclosure relates to computational systems and in particular relates to systems and methods for monitoring and diagnostics of an IT-based enterprise.
DEFINITIONS OF TERMS USED IN THE SPECIFICATION
As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used indicate otherwise.
The expression ‘Deterministic Finite State Automation/Machine’ (DFSA) used hereinafter in the specification refers to, but not limited to, a finite-state machine (FSM), a mathematical model of computation.
The expression ‘transition’ used hereinafter in the specification refers to, but not limited to, a change from one state to another state by the FSM, which is triggered by an input event, when the condition for each transition is fulfilled.
The expression ‘state’ used hereinafter in the specification refers, but not limited to, an operating state of a computing system, which maybe ON, OFF, Power saving, stand by or sleep mode.
The expression ‘Machine Learning Technique’ used hereinafter in the specification refers to, but not limited to, a set of techniques that allow implementing adaptive techniques to make predictions and to automatically organize input data according to the common features. The machine learning techniques include, but are not limited to, a support vector machine technique, linear regression, logistic regression, and Artificial Neural Networks (ANNs).
These definitions are in addition to those expressed in the art.
BACKGROUND
The background information herein below relates to the present disclosure but is not necessarily prior art.
Conventional digital enterprises use IT (information technology) engineers to determine the state of digital business operations currently going on in terms of the health of systems operating in an enterprises. For an uninterrupted functioning of the operations, these systems need to run properly and thus there is a dependency on the IT department all the time.
Typically, the IT department monitors the working state of the computing systems and maintains a record of related to each activity of the computing systems. The IT engineers determine the state of operations of the various hardware and software systems being used and reports about faults in the same, if detected. The faulty system is then looked into for corrective measures and the issue is resolved.
There are systems for monitoring faults in IT-based systems and also predicting likely future outcomes and component failure of such systems. For example, US20170139760A1 talks about analyzing machine logs and modifying the machine log data for the purpose.
Nonetheless, there is a need for efficiently automating monitoring and diagnostics of digital business operations in an enterprise.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
An object of the present disclosure is to provide a monitoring and diagnostics system for digital operations in an enterprise.
Another object of the present disclosure is to provide an intelligence based monitoring and diagnostics system for digital operations in an enterprise.
Still another object of the present disclosure is to provide a monitoring and diagnostics system for digital operations that is cost efficient.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure envisages a system for monitoring and diagnosing an IT-based enterprise. The system comprises a monitoring unit, a modeling unit, and a processing unit. The monitoring unit is configured to monitor at least one physical state and at least one operational state of at least one digital operation running on an IT system to generate state data for the digital operation and determine an instantaneous state of each of the at least one physical state and the at least one operational state.
The modeling unit is configured to receive the state data from the monitoring unit and generate a Deterministic Finite State Machine (DFSM) corresponding to the digital operation.
The processing unit is configured to compare the DFSM with a pre-defined expected state model stored in a database and generate a compared dataset for the digital operation, wherein the processing unit initializes a self-diagnostics module in case at least one value of the compared dataset falls outside a pre-determined range for fixing an anomaly in the digital operation. The system generates a state transition trend for each of the DFSM monitored over a period of time based on at least one machine learning technique.
In an embodiment, the at least one machine learning technique utilizes information related to probabilities of state transitions and maximum and minimum transition states for the at least one digital operation.
In another embodiment, the self-diagnostic module is configured to perform at least one operation selected from a group comprising of a reset, a cache clean up, reboot, and a factory reset.
In yet another embodiment, the self-diagnostic module is configured to notify an IT operator for replacing a faulty component of the IT system.
In an embodiment, the processing unit includes a predicting module which is configured to collect a current state data corresponding to each digital operation of the IT system continuously to build a performance model for the IT system.
In a further another embodiment, the performance model predicts an occurrence of failure based on at least one of performance data, service records, operating time, and events of occurred faults for each of the plurality of digital operations running on the IT system.
In a still further embodiment, the self-diagnostic module downloads a patch related to the faulty digital operation and updates the faulty digital operation by applying the downloaded patch.
In a still another embodiment, the self-diagnostic module employs organization policies and priorities pre-stored in the database for diagnosing the digital operations running on the IT system.
In yet another embodiment, the at least one machine learning technique is selected from a group consisting of a K-NN technique, a random forest technique, an extra trees technique, a decision trees technique, a multilayer perceptron technique, a linear regression technique, a ridge technique, a LASSO technique etc.
In an embodiment, a method for monitoring and diagnostics of an IT-based enterprise comprises the steps of:
• monitoring at least one state of each digital operation running on at least one IT system to generate a state data;
• generating a DFSM related to each of the monitored digital operations running in the IT system based on the state data;
• comparing the DFSM with a pre-defined expected state model to generate a compared dataset;
• initializing self-diagnostics for fixing an anomaly in case at least one value of the compared dataset falls outside a pre-determined range for fixing an anomaly in the digital operation; and
• generating a state transition trend for each of the DFSM monitored over a period of time based on at least one machine learning technique.
BRIEF DESCRIPTION OF ACCOMPANYING DRAWING
The present disclosure will now be described with the help of the accompanying drawing, in which:
FIGURE 1 illustrates a block diagram of a system for monitoring and diagnostics for digital operations in an enterprise, in accordance with an embodiment of the present disclosure; and
FIGURE 2 illustrates an exemplary method for performing system monitoring and diagnostics for digital operations in an enterprise, in accordance with an embodiment of the present disclosure.
LIST OF REFERENCE NUMERALS USED IN THE DESCRIPTION AND DRAWING
Reference Numeral Reference
100 System
102 Monitoring Unit
104 Database
106 Modeling Unit
108 Processing Unit
112 Self-Diagnostic Module
114 Predicting Module
DETAILED DESCRIPTION
Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.
Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details are set forth relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.
The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a,” "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms "comprises," "comprising," “including,” and “having,” are open ended transitional phrases and therefore specify the presence of stated features, integers, steps, operations, elements, modules, units and/or components, but do not forbid the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The particular order of steps disclosed in the method and process of the present disclosure is not to be construed as necessarily requiring their performance as described or illustrated. It is also to be understood that additional or alternative steps may be employed.
When an element is referred to as being "connected to" or "coupled to" another element, it may be directly on, connected or coupled to the other element. As used herein, the term "and/or" includes any and all combinations of one or more
All computer-based applications and underlying hardware can be modeled as a DFSA, an abstract machine that can exist in exactly one state at a time, and the transition from one state to another is deterministic. However, each transition can have probabilities based on time to transition with maximum and minimum times.
A computer implemented system (hereinafter referred as “system”) 100 for monitoring and diagnostics of digital operations and related processes in an enterprise using DFSA technique is now being described with reference to Figure 1 of the accompanying drawings.
The system 100 includes a monitoring unit 102, a database 104, a modeling unit 106, and a processing unit 108. The processing unit 108 includes a self-diagnostic module 112 and a predicting module 114.
The monitoring unit 102 is configured to monitor the current states of the digital operations and related processes running in the IT system periodically, and to generate state data of the digital operations. The monitoring unit 102 also monitors the transition information related to the running digital operation. In an embodiment, the monitoring unit 102 includes a plurality of sensors, placed in the proximity of the IT system, to sense the physical parameters of the IT system to generate the state data. The physical parameters may relate to for example a temperature of the underlying hardware, memory space, instantaneous processing speed and the like. In another embodiment, the monitoring unit 102 includes bots in the underlying application to monitor logic transitions and application parameters. The application parameters may relate to for example a load of the information to be processed, version of the software, types of hierarchy to be followed, i.e., serial, parallel, tree, etc.
The modeling unit 106 models out a deterministic finite state machine for each digital operation that is currently running on the IT system employed by the enterprise. For example, in a banking enterprise, a DFSA say for a loan application approval application can be computed by logging in both hardware and software details as captured by, for example, sensors in the hardware to monitor physical parameters and bots in the application to monitor logic transitions and application parameters.
In an implementation, the database 104 is communicatively coupled to the system 100 over a network and configured to store information related to the plurality of digital operations monitored by the monitoring unit 102, as well as a plurality of Finite State Machines modeled using the modeling unit 106.
In an embodiment, the database 104 stores a set of pre-determined set of rules for actions to be initiated based on the state of a process and/or a value of a pre-defined operational parameter pertaining to the process. For example, the rules may include faults pertaining to each finite state machine such as a storage state ‘full’, a ‘dead’ machine, response time beyond a certain threshold and the like. In an embodiment, the database 104 is also configured to store a set of machine learning rules, data analysis rules, and a plurality of data processing techniques. In one more embodiment, diagnostics or healing related rules can also be stored. For example, if a hardware of an IT system is found to be ‘dead’, processing is distributed to another hardware.
In a further embodiment, all the above information may be locally stored on a system memory (not shown in the figure) which may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or a non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes, and/or a cloud based storage (cloud storage).
In operation, the modeling unit 106 receives the state data from the monitoring unit 102. Under the set system operating commands, the modeling unit 106 generates a deterministic finite state machine related to each of the monitored digital operations running in the IT system. For example, for a banking enterprise, the modeling unit 106 generates a number of deterministic finite state machines related to each activity performed by the banking enterprise such as transaction model, current balance model, and user profile model based on a current/real time scenario. In an embodiment, the modeling unit 106 is capable to handle an unexpected state by using its artificial intelligence.
Further, the processing unit 108 is configured to cooperate with the modeling unit 106 and process input information from the modeling unit 106 based on a set of pre-determined rules for obtaining a set of system operating commands. The processing unit 108 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processing unit 108 is configured to fetch and execute the set of pre-determined rules stored in the database 104 to control modules/units of the system 100.
In operation, the processing unit 108 analyzes each of the DFSM generated by the modeling unit 106 for the relevancy of the DFSM with respect to one or more pre-defined standard state models stored in the database 104. To perform the desired task, the processing unit 108 applies extraction and comparing techniques. The processing unit 108 extracts a matching standard state model having best suitability from the database 104 for each generated DFSM and compares the two to generate a compared dataset indicative of a functioning of the DFSM.
In an embodiment, the processing unit 108 generates an alert signal if the generated dataset falls outside a pre-determined range designated for the standard state model(s) selected. In an embodiment, the processing unit 108 identifies any significant deviations in the generated data from a normal behavior to classify a fault into one of a hardware and/or application anomalies. For example, in case the generated value in terms of system memory being available at a given time is 25%, the same may be considered as “full”; otherwise anything greater than that shall be considered memory “available”.
In an embodiment, in case the processing unit 108 generates an alert signal for a DFSM, the processing unit 108 initializes the self-diagnostic module 112 for fixing the digital operation in the underlying IT system. In an embodiment, the processing unit 108 detects unlikely state transitions based on the comparing of the received state transition information and the stored probabilities of corresponding IT system.
In an embodiment, the self-diagnostic module 112 is configured to start system diagnostics by performing at least one operation such as reset, cache clean up, reboot, factory reset, kill, terminate, and a combination thereof. For example, if the processing unit 108 identifies the state of the IT system as “Memory Full”, then the self-diagnostic module 112 automatically performs cache clean up and restarts that particular machine/process. Further, if the self-diagnostic module 112 identifies that a particular component of the machine is faulty, and then the self-diagnostic module 112 notifies an IT administrator for replacing the faulty component of the IT system. In an embodiment, the self-diagnostic module 112 is configured to send an email, an SMS, an automated call to the IT administrator regarding the current state of the IT system. In other embodiment, if the processing unit 108 identifies a faulty hardware , then the processing unit 108 is configured to delegate the process performed by that hardware on the other hardware’s such that the business enterprise run without hindrance. For example, if the memory space is running low on an IT system for processing a huge data then the processing unit 108 advices the IT administrator to perform the required task on other IT systems having a high speed RAM.
In an embodiment, the processing unit 108 is configured to check a change in the state of the running process/ application for a pre-determined time and if an idle state is determined, the processing unit 108 is configured to shut down the particular process/application thereby saving electricity and providing a cost efficient system.
In an operative embodiment, the self-diagnostic module 112 continuously looks for any updates available to the process or application running on the IT system over the network. In case updates are available related to the application, the self-diagnostic module 112 automatically downloads the updates and installs the same.
In another operative embodiment, if the self-diagnostic module 112 finds that the process or application is faulty, then the self-diagnostic module 112 is configured to download a patch related to the faulty process/application to repair the faulty process/application by installing the downloaded patch.
The self-diagnostic module 112 further employs organization policies and priorities pre-stored in the database 104 for diagnosing the faulty process/application on the IT system. The priorities may be based on various factors like demand for a particular machine, position of a person assigned to a machine, number of machines of the same type, etc. For example, a server has a highest priority to get diagnosed first as the working of other machines rely on the server. Further, a CEO using an IT system gets preference over an IT trainee using another IT system in the organization.
Further, the processing unit 108 includes a predicting unit 114 configured to employ at least one machine learning technique to generate a state transition trend for each of the digital operation or IT system. In an embodiment, the predicting module 114 may be in communication with the processing unit 108 and/or the database 104 to receive state value(s), generated current state models, expected pre-determined state value(s), expected pre-determined standard state models and a set of machine learning rules.
In operation, under the set of machine learning rules, the predicting module 114 employs at least one machine learning technique, and predicts an occurrence of an event/failure. In an embodiment, the result of predicting module 114 is stored in the database 104. For example, the predicting module 114 receives the generated current state models and by crawling and extracting database 104 check the next expected state of the process/ application. The predicting module 114 further includes various factors to build the performance model such as performance data of the IT system, service records (number of services required by the IT system), operating time (number of hours for which the IT system works daily), and events of faults occurred already (frequency of faults detected) for each of the plurality of digital operations running in the IT system.
In an embodiment, the at least one machine learning technique is selected from a group consisting of a K-NN technique, a random forest technique, an extra trees technique, a decision trees technique, a multilayer perceptron technique, a linear regression technique, a ridge technique, a LASSO technique etc.
FIGURE 2 illustrates an exemplary method 200 for monitoring and diagnostics of an IT-based enterprise, in accordance with an embodiment of the present disclosure.
At step 202, current states of one or more digital operations running on an IT system are monitored. In an implementation, a monitoring unit 102 is configured to receive physical data pertaining to the underlying hardware and application logic/parameters. In an embodiment, the monitoring unit 102 also monitors the transition information related to the running digital operations. The monitoring unit 102 is configured to communicate with a plurality of physical sensors, application bots and the like.
At step 204, a state model (a deterministic finite state machine) is generated corresponding to the monitored data. In an implementation, a modeling unit 106 models a DFSM for each digital operation that is currently running on the IT system employed by the enterprise based on the monitored data from the monitoring unit 102.
At step 206, the generated state model is compared with a pre-defined expected state model. In an implementation, the processing unit 108 compares the received generated model with the pre-defined models to generate a compared dataset.
At step 208, the processing unit 108 initializes self-diagnostics for fixing an anomaly in case at least one value of the compared dataset falls outside a pre-determined range for fixing an anomaly in the digital operation. The processing unit 108 generates an alert signal in case the compared dataset falls outside a predetermined range. In an implantation, the processing unit 108, in addition to generating the alert signal, initializes the diagnostics module 112 to run automatic system diagnostics by performing at least one operation such as reset, cache clean up, reboot, factory reset, kill, terminate , and a combination thereof.
At step 210, a state transition trend is generated. In an implementation, a predicting module 114 generates a state transition trend for each of the business operations running on the IT system by employing at least one machine learning technique. The generated state transition trend is further analyzed for predicting an occurrence of failure of the business operation or hardware of the IT system.
The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer- readable medium. Other examples and implementations are within the scope and spirit of the disclosure and appended claims. For example, due to the nature of software, functions described above can be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations.
In addition, any disclosure of components contained within other components or separate from other components should be considered exemplary because multiple other architectures may potentially be implemented to achieve the same functionality, including incorporating all, most, and/or some elements as part of one or more unitary structures and/or separate structures.
The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer- readable medium. Other examples and implementations are within the scope and spirit of the disclosure and appended claims. For example, due to the nature of software, functions described above can be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations.
In addition, any disclosure of components contained within other components or separate from other components should be considered exemplary because multiple other architectures may potentially be implemented to achieve the same functionality, including incorporating all, most, and/or some elements as part of one or more unitary structures and/or separate structures.
TECHNICAL ADVANCEMENTS AND ECONOMICAL SIGNIFICANCE
The present disclosure described herein above has several technical advantages including, but not limited to, the realization of a system and a method, that:
• provides for simultaneous monitoring and self-diagnostics of systems for digital operations in an enterprise;
• provides for an intelligence based monitoring and system diagnostics for digital operations in an enterprise; and
• is cost efficient.
The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein
The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.
The foregoing description of the specific embodiments so fully reveals the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
,CLAIMS:WE CLAIM:
1. A system (100) for monitoring and diagnosing an IT-based enterprise, said system (100) comprising:
• a monitoring unit (102) configured to monitor at least one physical state and at least one operational state of at least one digital operation running on an IT system to generate state data for said digital operation and determine an instantaneous state of each of said at least one physical state and said at least one operational state;
• a modeling unit (106) configured to receive said state data from said monitoring unit (102) and generate a Deterministic Finite State Machine (DFSM) corresponding to said digital operation;
• a processing unit (108) configured to compare said DFSM with a pre-defined expected state model stored in a database (104) and generate a compared dataset for said digital operation, wherein said processing unit (108) initializes a self-diagnostics module (112) in case at least one value of said compared dataset falls outside a pre-determined range for fixing an anomaly in said digital operation,
wherein the system (100) generates a state transition trend for each of said DFSM monitored over a period of time based on at least one machine learning technique.
2. The system as claimed in claim 1, wherein said at least one machine learning technique utilizes information related to probabilities of state transitions and maximum and minimum transition states for said at least one digital operation.
3. The system as claimed in claim 1, wherein said self-diagnostic module (112) is configured to perform at least one operation selected from a group comprising of a reset, a cache clean up, reboot, and a factory reset.
4. The system as claimed in claim 1, wherein said self-diagnostic module (112) is configured to notify an IT operator for replacing a faulty component of said IT system.
5. The system as claimed in claim 1, wherein said processing unit (108) includes a predicting module (114) configured to collect a current state data corresponding to each digital operation of said IT system continuously to build a performance model for said IT system.
6. The system as claimed in claim 5, wherein said performance model predicts an occurrence of failure based on at least one of performance data, service records, operating time, and events of occurred faults for each of said plurality of digital operations running on said IT system.
7. The system as claimed in claim 1, wherein said self-diagnostic module (112) downloads a patch related to said faulty digital operation and updates said faulty digital operation by applying said downloaded patch.
8. The system as claimed in claim 1, wherein said self-diagnostic module (112) employs organization policies and priorities pre-stored in said database (104) for diagnosing said digital operations running on said IT system.
9. The system as claimed in claim 1, wherein said at least one machine learning technique is selected from a group consisting of a K-NN technique, a random forest technique, an extra trees technique, a decision trees technique, a multilayer perceptron technique, a linear regression technique, a ridge technique, a LASSO technique etc.
10. A method (200) for monitoring and diagnostics of an IT-based enterprise, said method (200) comprising following steps:
• monitoring (202) at least one state of each digital operation running on at least one IT system to generate a state data;
• generating (204) a DFSM related to each of said monitored digital operations running in said IT system based on said state data;
• comparing (206) said DFSM with a pre-defined expected state model to generate a compared dataset;
• initializing (208) self-diagnostics for fixing an anomaly in case at least one value of said compared dataset falls outside a pre-determined range for fixing an anomaly in said digital operation; and
• generating (210) a state transition trend for each of said DFSM monitored over a period of time based on at least one machine learning technique.