Patent ID: 12223456

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure. It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.

In the present document, the word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment or implementation of the present subject matter described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

The terms “comprise”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that one or more devices or sub-systems or elements or structures or components preceded by “comprises . . . a” does not, without more constraints, preclude the existence of other devices, sub-systems, additional sub-modules. Appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

A computer system (standalone, client or server computer system) configured by an application may constitute a “module” (or “subsystem”) that is configured and operated to perform certain operations. In one embodiment, the “module” or “subsystem” may be implemented mechanically or electronically, so a module includes dedicated circuitry or logic that is permanently configured (within a special-purpose processor) to perform certain operations. In another embodiment, a “module” or s “subsystem” may also comprise programmable logic or circuitry (as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations.

Accordingly, the term “module” or “subsystem” should be understood to encompass a tangible entity, be that an entity that is physically constructed permanently configured (hardwired) or temporarily configured (programmed) to operate in a certain manner and/or to perform certain operations described herein.

Referring now to the drawings, and more particularly toFIG.1throughFIG.23, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments, and these embodiments are described in the context of the following exemplary system and/or method.

FIG.1illustrates an exemplary block diagram representation of a network architecture100implementing a computer-implemented system102for an artificial intelligence (AI) driven autonomic application management framework in one or more environments116, in accordance with an embodiment of the present disclosure. According toFIG.1, the network architecture100includes the computer-implemented system102, one or more databases104, one or more electronic devices106, and the one or more environments116. The one or more electronic devices106may be associated with one or more users, and communicatively coupled to the computer-implemented system102(otherwise called as a system) via a communication network108. In an exemplary embodiment of the present disclosure, the electronic devices106may include a laptop computer, desktop computer, tablet computer, smartphone, wearable device, a digital camera, and the like. Further, the communication network108may be a wired network or a wireless network.

The computer-implemented system102may be at least one of, but not limited to, a central server, a cloud server, a remote server, an electronic device, a portable device, and the like. Further, the computer-implemented system102may be communicatively coupled to the database104, via the communication network108. The one or more databases104may include, but is not limited to, service level agreements (SLAs), enterprise applications, service level objective (SLO) parameters, policy record, measurable performance metrics, services, performance requirements, policy action(s) for each service, error files, standard SLI record, data compliance, security requirements, encryption requirements, governance requirements, requirements, objectives, constraints, a location, availability requirements, pricing requirements, security requirements associated with the one or more ERP applications, response time, availability, throughput, performance indicators, service name, a current performance level of a performance metric under consideration, an empty actions service name, performance required metric, action to improve or maintain the record, any other data, and combinations thereof. The one or more databases104may be any kind of databases/repositories such as, but are not limited to, relational database, dedicated database, dynamic database, monetized database, scalable database, cloud database, distributed database, any other database, and combination thereof.

Further, the one or more environments116may include, but is not limited to, multiple cloud environments, hybrid cloud environments, colocation environments, on-premises infrastructure environments, edge computing environments, and the like. Further, the one or more electronic devices106may be associated with, but not limited to, a user, an individual, an administrator, a vendor, a technician, a worker, a specialist, a healthcare worker, an instructor, a supervisor, a team, an entity, an organization, a company, a facility, a bot, any other user, and combination thereof. The entities, the organization, and the facility may include, but are not limited to, a hospital, a healthcare facility, an exercise facility, a laboratory facility, an e-commerce company, a merchant organization, an airline company, a hotel booking company, a company, an outlet, a manufacturing unit, an enterprise, an organization, an educational institution, a secured facility, a warehouse facility, a supply chain facility, any other facility and the like. The one or more electronic devices106may be used to provide input and/or receive output to/from the computer-implemented system102, and/or to the one or more databases104, respectively. The one or more electronic devices106may present to the one or more user interfaces for the one or more user to interact with the computer-implemented system102and/or to the one or more databases104for artificial intelligence (AI) driven autonomic application management framework need. The one or more electronic devices106may be at least one of, an electrical, an electronic, an electromechanical, and a computing device. The one or more electronic devices106may include, but is not limited to, a mobile device, a smartphone, a personal digital assistant (PDA), a tablet computer, a phablet computer, a wearable computing device, a virtual reality/augmented reality (VR/AR) device, a laptop, a desktop, a server, and the like.

The term “management” in the present disclosure implies deployment, monitoring, optimization, managing applications across the one or more environments116. For example, managing involves coordinating and automating various tasks and processes related to the lifecycle of applications, including deployment, monitoring, optimization, and management, to ensure efficient operation in multi-cloud environments.

Further, the computer-implemented system102may be implemented by way of a single device or a combination of multiple devices that may be operatively connected or networked together. The computer-implemented system102may be implemented in hardware or a suitable combination of hardware and software. The computer-implemented system102includes one or more hardware processor(s)110, and a memory112. The memory112may include a plurality of subsystems114. The computer-implemented system102may be a hardware device including the one or more hardware processors110executing machine-readable program instructions for the artificial intelligence (AI) driven autonomic application management framework. Execution of the machine-readable program instructions by the one or more hardware processors110may enable the proposed computer-implemented system102to manage the artificial intelligence (AI) driven autonomic application framework. The “hardware” may comprise a combination of discrete components, an integrated circuit, an application-specific integrated circuit, a field-programmable gate array, a digital signal processor, or other suitable hardware. The “software” may comprise one or more objects, agents, threads, lines of code, subroutines, separate software applications, two or more lines of code, or other suitable software structures operating in one or more software applications or on one or more processors.

The one or more hardware processors110may include, for example, microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and/or any devices that manipulate data or signals based on operational instructions. Among other capabilities, the one or more hardware processors110may fetch and execute computer-readable instructions in the memory112operationally coupled with the computer-implemented system102for performing tasks such as data processing, input/output processing, and/or any other functions. Any reference to a task in the present disclosure may refer to an operation being or that may be performed on data.

Though few components and subsystems are disclosed inFIG.1, there may be additional components and subsystems which is not shown, such as, but not limited to, ports, routers, repeaters, firewall devices, network devices, databases, network attached storage devices, servers, assets, machinery, instruments, facility equipment, emergency management devices, image capturing devices, sensors, any other devices, and combination thereof. The person skilled in the art should not be limiting the components/subsystems shown inFIG.1. AlthoughFIG.1illustrates the computer-implemented system102, and the one or more electronic devices106connected to the one or more databases104, one skilled in the art can envision that the computer-implemented system102, and the one or more electronic devices106can be connected to several electronic devices located at various locations and several databases via the communication network108.

Those of ordinary skilled in the art will appreciate that the hardware depicted inFIG.1may vary for particular implementations. For example, other peripheral devices such as an optical disk drive and the like, local area network (LAN), wide area network (WAN), wireless (e.g., wireless-fidelity (Wi-Fi)) adapter, graphics adapter, disk controller, input/output (I/O) adapter also may be used in addition or place of the hardware depicted. The depicted example is provided for explanation only and is not meant to imply architectural limitations concerning the present disclosure.

Those skilled in the art will recognize that, for simplicity and clarity, the full structure and operation of all data processing systems suitable for use with the present disclosure are not being depicted or described herein. Instead, only so much of the computer-implemented system102as is unique to the present disclosure or necessary for an understanding of the present disclosure is depicted and described. The remainder of the construction and operation of the system102may conform to any of the various current implementations and practices that were known in the art.

In an exemplary embodiment, the computer-implemented system102may be configured to obtain one or more items of data from at least one of: one or more electronic devices106associated with the one or more users and the one or more databases104. In an embodiment, the one or more data may include information associated with one or more service level agreements (SLAs) corresponding to the one or more applications (e.g., one or more enterprise applications) associated with the one or more environments116. In an embodiment, the one or more service level agreements (SLAs) associated with the one or more applications may include one or more natural language texts.

The computer-implemented system102may be further configured to determine one or more semantics and structure of the one or more natural language texts associated with the one or more service level agreements (SLAs) based on analysis of the one or more natural language texts associated with the one or more service level agreements (SLAs), using a first artificial intelligence (AI) model.

The computer-implemented system102may be further configured to extract one or more service level objectives (SLOs) and associated metrics corresponding to one or more services specified in the one or more service level agreements (SLAs) based on the determined one or more semantics and structures of the one or more natural language texts associated with the one or more service level agreements (SLAs), using the first artificial intelligence (AI) model. In an embodiment, the one or more service level objectives (SLOs) include, but are not limited to, response time, availability, throughput, performance indicators, and the like.

The computer-implemented system102may be further configured to obtain one or more first real-time data including one or more actual performance levels, and one or more service level indictors (SLIs), of the one or more services associated with the one or more applications, from one or more monitoring platforms. The computer-implemented system102may be further configured to determine whether the one or more actual performance levels of the one or more services associated with the one or more applications, are compliant with one or more expected performance levels by comparing the one or more actual performance levels with one or more pre-defined key performance indicators (KPIs) and one or more pre-defined goals of the one or more services defined in at least one of: the service level agreements (SLAs) and the service level objectives (SLOs), using the first artificial intelligence (AI) model.

In an embodiment, the first artificial intelligence (AI) model is trained with one or more pre-defined rules and criteria to assess the one or more pre-defined key performance indicators (KPIs) and one or more pre-defined goals defined in at least one of: the service level agreements (SLAs) and the service level objectives (SLOs). The computer-implemented system102may be further configured to automatically update the one or more service level objectives (SLOs) and associated metrics corresponding to the one or more services specified in the one or more service level agreements (SLAs), based on deviations of the one or more actual performance levels of the one or more services from the one or more expected performance levels, using the first artificial intelligence (AI) model.

The computer-implemented system102may be further configured to generate one or more insights associated with one or more actions, to be applied to one or more corresponding services, to be performed to automatically manage the one or more applications based on the automatic updates on the one or more service level objectives (SLOs) and associated metrics corresponding to the one or more services specified in the one or more service level agreements (SLAs), using the first artificial intelligence (AI) model. The computer-implemented system102may be further configured to provide information associated with the one or more actions, to be applied to the one or more corresponding services to automatically manage the one or more applications, to the one or more environments116.

FIG.2illustrates an exemplary block diagram representation of a computer implemented system, such as those shown inFIG.1, capable of the artificial intelligence (AI) driven autonomic applications management framework in the one or more environments116, in accordance with an embodiment of the present disclosure. The computer-implemented system102may also function as a system/server (hereinafter referred to as the system102). The computer-implemented system102comprises the one or more hardware processors110, the memory112, and a storage unit204. The one or more hardware processors110, the memory112, and the storage unit204are communicatively coupled through a system bus202or any similar mechanism. The memory112comprises the plurality of subsystems114in the form of programmable instructions executable by the one or more hardware processors110.

Further, the plurality of subsystems114includes a data obtaining subsystem206, a natural language processing (NLP) subsystem208, a service level objectives extraction subsystem210, a compliance determining subsystem212, a service level objectives updating subsystem214, a decision-making subsystem216, a fine-tuning subsystem218, a data extraction subsystem220, a reinforcement learning subsystem222, a knowledge graph generation subsystem224, an observability construction subsystem226, and an actions provisioning subsystem228.

The one or more hardware processors110, as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor unit, microcontroller, complex instruction set computing exceptionally long processor unit, reduced instruction set computing microprocessor unit, very long instruction word microprocessor unit, explicitly parallel instruction computing microprocessor unit, graphics processing unit, digital signal processing unit, or any other type of processing circuit. The one or more hardware processors110may also include embedded controllers, such as generic or programmable logic devices or arrays, application-specific integrated circuits, single-chip computers, and the like.

The memory112may be a non-transitory volatile memory and a non-volatile memory. The memory112may be coupled to communicate with the one or more hardware processors110, such as being a computer-readable storage medium. The one or more hardware processors110may execute machine-readable instructions and/or source code stored in the memory112. A variety of machine-readable instructions may be stored in and accessed from the memory112. The memory112may include any suitable elements for storing data and machine-readable instructions, such as read-only memory, random access memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, a hard drive, a removable media drive for handling compact disks, digital video disks, diskettes, magnetic tape cartridges, memory cards, and the like. In the present embodiment, the memory112includes the plurality of modules114stored in the form of machine-readable instructions on any of the above-mentioned storage media and may be in communication with and executed by the one or more hardware processors110.

The storage unit204may be a cloud storage or a repository such as those shown inFIG.1. The storage unit204may store, but is not limited to, the service level agreements (SLAs), the applications (e.g., the enterprise applications), the service level objectives (SLOs), policy records, measurable performance metrics, the services, performance requirements, policy action(s) for each service, error files, standard SLI record, data compliance, security requirements, encryption requirements, governance requirements, requirements, objectives, constraints, a location, availability requirements, pricing requirements, security requirements associated with the one or more ERP applications, response time, availability, throughput, performance indicators, service name, a current performance level of a performance metric under consideration, an empty actions service name, performance required metric, action to improve or maintain the record, any other data, and combinations thereof. The storage unit204may be any kind of databases/repositories such as, but are not limited to, relational database, dedicated database, dynamic database, monetized database, scalable database, cloud database, distributed database, any other database, and combination thereof.

The plurality of subsystems114includes the data obtaining subsystem206that is communicatively connected to the one or more hardware processors110. The data obtaining subsystem206is configured to obtain the one or more items of data from at least one of: the one or more electronic devices106associated with the one or more users and the one or more databases104. In an embodiment, the one or more data may include the information associated with the one or more service level agreements (SLAs) corresponding to the one or more applications (e.g., one or more enterprise applications) associated with the one or more environments116. In an embodiment, the one or more service level agreements (SLAs) associated with the one or more applications may include the one or more natural language texts.

The one or more applications may include, but are not limited to, the enterprise resource planning (ERP) applications, and customer relationship management (CRM) applications. In an example, the one or more SLAs may be an explicit contract between an infrastructure provider and an enterprise that runs services. The one or more SLAs includes, but are not limited to requirements, objectives, constraints, a location, availability requirements, pricing requirements, security requirements associated with the one or more applications, and the like.

The plurality of subsystems114further includes the natural language processing (NLP) subsystem208that is communicatively connected to the one or more hardware processors110. The natural language processing (NLP) subsystem208is configured to determine the one or more semantics and structures of the one or more natural language texts associated with the one or more service level agreements (SLAs) based on analysis of the one or more natural language texts associated with the one or more service level agreements (SLAs), using the first artificial intelligence (AI) model.

For determining the one or more semantics and structures of the one or more natural language texts associated with the one or more service level agreements (SLAs), the natural language processing subsystem208is configured to train the first artificial intelligence (AI) model on one or more training datasets encompassing one or more texts from one or more text sources (e.g., internet). The first artificial intelligence (AI) model may be a fine-tuned large language model (LLM).

The large language model (LLM) is a sophisticated artificial intelligence system configured to determine and generate the one or more natural language texts on a massive scale. The large language model (LLM) is configured to utilize deep learning techniques to process and generate coherent language, enabling the one or more applications including at least one of: natural language understanding, text completion, and human conversation. The Large language models (LLMs) are typically trained on vast and diverse training datasets that encompass a wide range of the one or more texts from the internet. The one or more training datasets may include at least one of: one or more articles, one or more books, one or more websites, and other text sources to expose the large language model to a broad spectrum of language patterns and contexts.

In an embodiment, the one or more training datasets may be obtained from one or more languages, and the one or more training datasets may include one or more topics, allowing the first artificial intelligence model (e.g., the large language model) to learn one or more nuances and intricacies of human language across different domains. In an embodiment, the one or more specific training datasets used, may be changed depending on the one or more users (e.g., an organization or researcher) training the large language model. The large language model may determine the semantics and structure of the one or more nature language texts. The large language model may posse the ability to determine and generate human-like text, enabling tasks including at least one of: natural language understanding, text generation, language translation, and summarization. The large language model may excel in applications including at least one of: conversational agents, code generation, sentiment analysis, and question answering, showcasing their versatility across various language-related tasks.

Upon obtaining the one or more data from at least one of: one or more electronic devices106associated with the one or more users and the one or more databases104, the natural language processing subsystem208is further configured to process the one or more data to determine the one or more semantics and structures of the one or more natural language texts associated with the one or more service level agreements (SLAs) based on the training of the first artificial intelligence (AI) model on the one or more training datasets. In an embodiment, processing the one or more data includes breaking the one or more natural language texts associated with the one or more service level agreements (SLAs) to be learned by the trained first artificial intelligence (AI) model to determine the one or more semantics and structures of the one or more natural language texts.

In an embodiment, the large language model may have a comprehensive understanding of the one or more semantics and structures of the one or more natural language texts associated with the one or more service level agreements (SLAs). The large language model may effectively navigate complex language structures and infer semantic meanings, facilitating tasks including at least one of: natural language understanding and context-aware text generation, by enhancing/fine-tuning the large language model with an ability to reason. Further, based on the enhanced reasoning capability, the large language model may make informed responses to questions, make connections between disparate pieces of information, and exhibit a degree of contextual awareness in generating human-like contextual text to reason and act.

The plurality of subsystems114further includes the service level objectives extractions subsystem210that is communicatively connected to the one or more hardware processors110. The service level objectives extractions subsystem210is configured to extract the one or more service level objectives (SLOs) and the associated metrics corresponding to the one or more services specified in the one or more service level agreements (SLAs) based on the determined one or more semantics and structures of the one or more natural language texts associated with the one or more service level agreements (SLAs), using the first artificial intelligence (AI) model (e.g., the large language model (LLM)).

In an embodiment, the trained first artificial intelligence (AI) model is fine-tuned with the determined one or more semantics and structure of the one or more natural language texts specific to the one or more service level objectives (SLOs) and associated metrics, using the fine-tuning subsystem218. In an embodiment, the fine-tuned first artificial intelligence (AI) model may include a Linguistic Latent Attribute model (LLAMA 2). In other words, the large language model is fine-tuned to grasp the one or more semantics and structure of the one or more natural language texts specific to service level objectives (SLOs) and associated metrics. In an embodiment, the one or more data (i.e., static data) from one or more contractual documents may provide a foundation for the fine-tuning process of the large language model, enabling the large language model to analyze and determine the intricacies of the service level agreements (SLAs). The service level objectives extraction subsystem210is further configured to extract the one or more service level objectives (SLOs) and associated metrics based on the fine-tuning process of the trained first artificial intelligence (AI) model with the determined one or more semantics and structures of within the one or more natural language texts.

The data obtaining subsystem206is further configured to obtain the one or more first real-time data including the one or more actual performance levels, and the one or more service level indictors (SLIs), of the one or more services associated with the one or more applications, from the one or more monitoring platforms. In an embodiment, the one or more monitoring platforms may include at least one of: New Relic, Prometheus, Azure, and other Observability platforms, capturing live and low-level performance indicators of IT infrastructure (i.e., the one or more services of the one or more applications). In an embodiment, the one or more real-time data (i.e., dynamic data) enables generation of an instance LLM model and one or more knowledge graphs, providing a real-time perspective of system's health.

The plurality of subsystems114further includes the compliance determining subsystem212that is communicatively connected to the one or more hardware processors110. The compliance determining subsystem212is configured to determine whether the one or more actual performance levels of the one or more services associated with the one or more applications, are compliant with the one or more expected performance levels by comparing the one or more actual performance levels with the one or more pre-defined key performance indicators (KPIs) and the one or more pre-defined goals of the one or more services defined in at least one of: the service level agreements (SLAs) and the service level objectives (SLOs), using the first artificial intelligence (AI) model.

In an embodiment, the first artificial intelligence (AI) model (e.g., the large language model) is trained with the one or more pre-defined rules and criteria to assess the one or more pre-defined key performance indicators (KPIs) and the one or more pre-defined goals defined in at least one of: the service level agreements (SLAs) and the service level objectives (SLOs).

The plurality of subsystems114further includes the service level objectives updating subsystem214that is communicatively connected to the one or more hardware processors110. The service level objectives updating subsystem214is configured to automatically update the one or more service level objectives (SLOs) and associated metrics corresponding to the one or more services specified in the one or more service level agreements (SLAs), based on the deviations of the one or more actual performance levels of the one or more services from the one or more expected performance levels, using the first artificial intelligence (AI) model.

The plurality of subsystems114further includes the data extraction subsystem220that is communicatively connected to the one or more hardware processors110. The data extraction subsystem220is configured to extract one or more second real-time data from the one or more first real-time data for fine-tuning the large language model (LLM) using a second artificial intelligence (AI) model. In an embodiment, the second artificial intelligence (AI) model may be a small language model (SLM). For extracting the one or more second real-time data from the one or more first real-time data, the data extraction subsystem220is configured to obtain the one or more first real-time data from the one or more monitoring platforms using a data ingestion layer. The data ingestion layer is configured to determine whether the one or more first real-time data are obtained efficiently and to preprocess the one or more first real-time data to determine whether the one or more first real-time data include consistency and compatibility across the one or more monitoring platforms.

The data extraction subsystem220is further configured to categorize the one or more first real-time data based on at least one of: one or more types of the one or more first real-time data and the one or more monitoring platforms, using a categorization and routing layer. The categorization and routing layer is configured to optimize a routing process of the one or more first real-time data to determine whether the one or more first real-time data are directed to a corresponding small language model (SLM) for analysis of the one or more first real-time data.

The data extraction subsystem220is further configured to process the one or more types of the one or more first real-time data and add one or more securities and governance criteria to the one or more first real-time data. For processing the one or more types of the one or more first real-time data includes (a) assessing relevancy of the one or more first real-time data using one or more pre-defined criteria, (b) mitigating noise by filtering one or more repetitive data points associated with the one or more first real-time data, and (c) training each small language model (SLM) to recognize one or more patterns and anomalies within one or more domains associated with the one or more first real-time data, to identify one or more security-related events.

The data extraction subsystem220is further configured to mitigate one or more data volumes by eliminating the one or more repetitive data points associated with the one or more first real-time data to determine an importance of each data point associated with the one or more first real-time data, using a data relevance assessment and reduction layer. The data extraction subsystem220is further configured to extract the one or more second real-time data from the one or more first real-time data based on an analysis of the relevancy of the one or more first real-time data.

The plurality of subsystems114further includes the reinforcement learning subsystem222that is communicatively connected to the one or more hardware processors110. The reinforcement learning subsystem222is configured to optimize the small language model (SLM) based on one or more feedback and results associated with the extraction of the one or more second real-time data using a reinforcement learning layer through a language model (LM) agent. In an embodiment, the language model (LM) agent is configured to serve as a dynamic interface between the second artificial intelligence (AI) model and the one or more monitoring platforms. The language model (LM) agent may possess reinforcement capabilities, facilitating continuous learning and adjustment based on real-time feedback from the dynamic data, which ensures that the computer-implemented system102evolves and adapts to emerging patterns, contributing to a more resilient and responsive monitoring framework. In another embodiment, the small language model (SLM) is optimized by learning the one or more second real-time data from one or more historical data to analyse security-related one or more second real-time data.

The plurality of subsystems114further includes the fine-tuning subsystem218that is communicatively connected to the one or more hardware processors110. The fine-tuning subsystem218is configured to fine-tune the first artificial intelligence (AI) model with the one or more second real-time data in accordance with at least one of: the one or more service level agreements (SLAs), the one or more service level objectives (SLOs) and associated metrics corresponding to the one or more services. In an embodiment, the fine-tuned first artificial intelligence (AI) model may be the instance language model.

The plurality of subsystems114further includes the knowledge graph generation subsystem224that is communicatively connected to the one or more hardware processors110. The knowledge graph generation subsystem224is configured to generate one or more knowledge graphs defining one or more structure and relationships common to one or more language models within the one or more domains. In an embodiment, the one or more knowledge graphs are one or more ontology views with one or more optimized level concepts and one or more meta-relations including at least one of: one or more entities, one or more attributes of the one or more entities, and one or more relationships between the one or more entities.

For fine-tuning the first artificial intelligence (AI) model with the one or more second real-time data, the fine-tuning subsystem218is configured to obtain the one or more second real-time data from the one or more monitoring platforms. In an embodiment, the one or more second real-time data are stored in one or more interim databases. The fine-tuning subsystem218is further configured to translate the one or more second real-time data into one or more formats for fine-tuning the first artificial intelligence (AI) model.

The fine-tuning subsystem218is further configured to map the one or more second real-time data to at least one of: the one or more service level agreements (SLAs), the one or more service level objectives (SLOs) and associated metrics corresponding to the one or more services, available in the first artificial intelligence (AI) model fine-tuned with the determined one or more semantics and structure of the one or more natural language texts. The fine-tuning subsystem218is further configured to convert the mapped one or more second real-time data into one or more question answering based datasets for fine-tuning the first artificial intelligence (AI) model. The fine-tuning subsystem218is further configured to update the one or more knowledge graphs based on the one or more second real-time data to update one or more instance view graphs.

The plurality of subsystems114further includes the decision-making subsystem216that is communicatively connected to the one or more hardware processors110. The decision-making subsystem216is configured to generate the one or more insights associated with the one or more actions, to be applied to the one or more corresponding services, to be performed to automatically manage the one or more applications based on the automatic updates on the one or more service level objectives (SLOs) and associated metrics corresponding to the one or more services specified in the one or more service level agreements (SLAs), using the first artificial intelligence (AI) model.

For generating the one or more actions, the decision making subsystem216with the first artificial intelligence (AI) model is configured to retrieve an initial state of the one or more service level objectives (SLOs) and associated metrics based on an Augmented Deep Active learning for text and Planning Trajectories (ADAPT) agent configured with the first artificial intelligence (AI) model being fine-tuned. The decision making subsystem216is further configured to generate one or more plan trajectories to accomplish in meeting the one or more service level objectives (SLOs) based on the initial state of the one or more service level objectives (SLOs) and associated metrics, by querying the first artificial intelligence (AI) model from the Augmented Deep Active learning for text and Planning Trajectories (ADAPT) agent.

The decision making subsystem216is further configured to generate the one or more actions, to be applied to the one or more environments116, based on at least one of: current state of the one or more service level objectives (SLOs) and probabilities of meeting the one or more service level objectives (SLOs), by at least one of: learning, planning procedures, and resembling actor-critic updates for Partial Observable Markov Decision Processes (POMDPs). The decision making subsystem216is further configured to receive one or more feedbacks to refine a decision-making process on generation of the one or more actions upon analysing the generated one or more actions using a critic model. The decision making subsystem216is further configured to recur the refinement of the decision-making process on generation of the one or more actions until one or more optimal actions are generated. The decision making subsystem216is further configured to optimize the generated one or more actions to adapt with evolving conditions within the one or more environments116by updating the learning, planning procedures. The decision making subsystem216is further configured to provide the optimized one or more actions to the one or more environments116to accomplish in meeting the one or more service level objectives (SLOs).

The plurality of subsystems114further includes the observability construction subsystem226that is communicatively connected to the one or more hardware processors110. The observability construction subsystem226is configured to construct partial observabilities by providing one or more observations when the language model (LM) agent has partial knowledge of a state of the one or more environments116, using the Partially Observable Markov Decision process (POMDP). In an embodiment, the one or more observations are configured to provide implicit information about a true state, allowing the language model (LM) agent to update probability distribution over one or more possible states.

The plurality of subsystems114further includes the actions provisioning subsystem228that is communicatively connected to the one or more hardware processors110. The actions provisioning subsystem228is configured to provide the information associated with the one or more actions, to be applied to the one or more corresponding services to automatically manage the one or more applications, to the one or more environments116.

FIG.3illustrates a schematic diagram300of a logical representation of the one or more service level agreements (SLAs)302, in accordance with an embodiment of the present disclosure. Business processes may depend heavily on a multitude of software applications, each handling various tasks. To determine overall health of a business, the computer-implemented system102needs to examine the key performance indicators of individual processes and consolidate the key performance indicators of all individual processes into a unified system that may provide the one or more insights enabling the one or more actions to be taken to either correct the process or to make the process more efficient. The reported data may not always be real-time and the reported data may be days or even months old, depending on the reporting frequency.

For example, an IT company specializes in deploying and managing software for its customers. In this scenario, the IT company commits to the Service Level Agreements (SLAs)302, ensuring the one or more services304including at least one of: 24/7 availability and response times under 300 milliseconds for business applications. For a moderately complex deployment, involving various applications like front-end, APIs, and databases across physical or virtual machines, meeting these SLAs302requires seamless collaboration among various components of the one or more applications.

FIG.4illustrates a schematic diagram400depicting the one or more service level agreements (SLAs)302and one or more service level objectives (SLOs), that are measured by a telemetry402, in accordance with an embodiment of the present disclosure. Each component of the one or more applications may provide the telemetry402on its health, performance, and errors. The telemetry402is delivered at a rapid frequency, in a sub-second interval, generating a substantial volume of data. Interpreting the data may pose a complex challenge, and an even more daunting task is an ability to respond in real-time to address issues before the data lead to a breach of service level objectives (SLOs).

The computer-implemented system102may automate the entire lifecycle of the service level agreements (SLAs)302, starting from a moment of signing the contracts, capturing the one or more real-time data from various documents, and utilizing the artificial intelligence model capabilities to reason and act to generate the one or more insights received from the one or more real-time data, serves as significant solution for one or more organizations. The decision-making process on generating the one or more insights associated with the one or more actions by the artificial intelligence model to be applied to the corresponding services, may significantly reduce allocation of time, financial resources, and personnels associated with operations.

FIG.5illustrates a schematic diagram500depicting an automation of the one or more service level agreements (SLAs) and one or more service level objectives (SLOs) using the artificial intelligence (AI) model (e.g., the large language model (LLM)), in accordance with an embodiment of the present disclosure. The large language model has capabilities of analyzing the one or more natural language texts, generating the determining context reasoning. The main component of the large language model is a transformer configured to encode and decode the one or more natural language texts based on one or more deep neural networks of the transformer. The large language model may store the one or more semantics and structure of the one or more natural language texts based on analysis of the one or more pre-defined rules (e.g., grammar) of the language, determination of the context of the text and reasoning based on the one or more pre-defined rules. The large language model is configured to automate the one or more service level objectives (SLOs) and generate the one or more actions to meet the SLOs if needed.

FIG.6illustrates an exemplary schematic diagram600representing the one or more service level agreements (SLAs) and one or more service level objectives (SLOs) and associated metrics for a web application, in accordance with an embodiment of the present disclosure. The exemplary schematic diagram shows that the SLAs, SLOs and associated metrics may have the one or more semantics and structure of the natural language texts. The rule in this exemplary case is determined from the “service level performance” for example, “if the response rate <30 ms, the SLO meets the expectation”.

In overall, the large language model is configured to analyze the one or more semantics and structure of the natural language texts and determine one or more semantics and structure of the natural language texts associated with the one or more service level agreements (SLAs), the one or more service level objectives (SLOs) and associated metrics. The large language model is further configured to decode the one or more real-time dynamic data for better decision-making on generating actions for the one or more corresponding services for an autonomic system to meet the SLAs and SLOs expectations, using the one or more pre-defined rules the large language model is trained with.

The large language models (e.g., generative pre-trained transformer (GPT)), has capabilities in understanding the semantics and structure of the natural language texts. This includes the ability to comprehend the context, meaning, and relationships within textual information, which is crucial for interpreting SLAs, SLOs, and metrics specified in these documents. The capabilities of the large language models to process and understand the one or more real-time data may be valuable for monitoring dynamic aspects of SLAs and SLOs, which allows for the analysis of live data streams, identifying trends, anomalies, or deviations from expected performance levels specified by the SLOs.

Further, the large language model is trained with the one or more pre-defined rules and criteria to evaluate the key performance indicators (KPIs) and goals specified in SLAs and SLOs. By applying these pre-defined rules to the one or more real-time data, the computer-implemented system102with the large language model may determine whether the one or more actual performance levels are compliant with the one or more expected performance levels. The determination of the semantics and structure of the natural language texts, the real-time data and the utilization of the pre-defined rules, by the large language model, may contribute to the development of an efficient computer-implemented system102(i.e., an automated SLA and SLO system) to generate corrective actions if needed. The computer-implemented system102may continuously evaluate performance metrics, compare the performance metrics against the pre-defined goals, and generate the one or more insights or alerts based on the comparison and analysis.

The one or more insights generated from the computer-implemented system102may contribute to more informed automated decision-making. The computer-implemented system102may utilize the one or more insights to proactively address issues, optimize performance, and generate strategic decisions related to service levels. In summary, leveraging the capabilities large language model for understanding the one or more semantics and structure of the natural language texts, processing the real-time data, and applying the one or more pre-defined rules, may serve as a cornerstone for building an efficient and intelligent autonomic system for monitoring meeting SLAs and SLOs expectations, leading to improved decision-making in managing services and meeting performance goals.

In specific, the large language model may play a pivotal role in fine-tuning, combining internal enterprise specific proprietary knowledge and external insights to establish an initial metal model and a knowledge graph. The initial meta model may be used for comprehending static aspects of SLA definitions and associated metrics. The computer-implemented system102retrieves the one or more real-time dynamics data from the one or more monitoring platforms including at least one of: New Relic, Prometheus, and similar monitoring platforms. The one or more monitoring platforms may capture the live and low-level performance metrics from the IT infrastructure. The one or more real-time dynamic data is utilized to generate the instance large language model and the knowledge graph are dynamically generated for providing a real-time view of system's performance. The computer-implemented system102utilizes reinforcement learning through the language model (LM) agent, ensuring continuous adaptation. A planner, introduced into the computer-implemented system102, contributes to adaptive learning, handling mathematical computations and guiding the computer-implemented system102towards defined goals.

The language model (LM) agent may be configured as a dynamic bridge, facilitating integration with the one or more monitoring platforms but crucially introduces reinforcement learning. The planner with its proficiency in mathematical precision and goal-oriented planning, enhances the overall intelligence of the computer-implemented system102. This collaborative effort ensures a comprehensive view of SLA and SLO adherence. Time-series analysis is facilitated through the knowledge graph's built-in data partitioning, providing a nuanced understanding of performance trends. The integrated meta model ensures interpretability, empowering the computer-implemented system102with actionable intelligence for informed decision-making. The planner is configured to integrate into the software architecture (i.e., the artificial intelligence (AI) driven autonomic application management framework), handling mathematical calculations, defining expected trajectories, and providing adaptive planning strategies. The planner works in harmony with the large language models (LLMs), contributing to a more precise and adaptive corrective system. The planner ensures that the computer-implemented system102not only comprehends the language-based context but also excels in the intricate mathematical aspects inherent in SLA and SLO management.

In summary, the software architecture with the planner represents a pinnacle in automated SLA and SLO monitoring and taking autonomic actions. The collaborative synergy between LLMs, the planner, and real-time dynamic data ensures compliance and continual adaptation to the evolving dynamics of enterprise operations, setting a new standard in intelligent and adaptive monitoring solutions.

FIG.7illustrates a schematic diagram700representing a high-level architecture of the artificial intelligence (AI) driven autonomic applications management framework in the one or more environments116, in accordance with an embodiment of the present disclosure. The high-level architecture of the artificial intelligence (AI) driven autonomic applications management framework is initiated with a data ingestion from one or more sources including at least one of: the one or more electronic devices106associated with the one or more users, and the one or more databases. The one or more data702may include the information associated with the one or more service level agreements (SLAs) and the one or more service level objectives (SLOs) corresponding to the one or more applications in the one or more environments116. The one or more service level agreements (SLAs) and the one or more service level objectives (SLOs) are generally stored in contracts, documents and electronic mails. In an embodiment, the one or more data702may bring specificity to data context.

In an embodiment, the one or more service level agreements (SLAs) and the one or more service level objectives (SLOs) are typically documented in at least one of: contracts, emails, and other written agreements. The one or more data702may be crucial for some reasons. The one or more service level agreements (SLAs) and the one or more service level objectives (SLOs) provide specific details about the expectations and commitments between parties involved, including a service provider and its customers. The SLAs and SLOs outline precise metrics, benchmarks, and performance targets. The SLAs and SLOs may include valuable information associated with at least one of: a scope of services, response times, uptime guarantees, and penalties for non-compliance. The above said information ensures that both parties have a clear understanding of what is expected.

The SLAs and SLOs may provide context by defining the rules and conditions under which the one or more services may be delivered. The context may help in interpreting and enforcing the agreements effectively. The well-defined SLAs and SLOs may facilitate informed decision-making on generating the one or more actions to the one or more corresponding services. The SLAs and SLOs allow the organizations to assess whether the service providers are meeting their commitments and make necessary adjustments or decisions based on the one or more data. The SLAs and SLOs establish accountability by setting expectations for performance. If there are disputes or issues, the SLAs and SLOs may serve as a reference point to determine responsibility and liability.

The SLAs and SLOs are essential components of business agreements that ensure the quality and reliability of the one or more services and provide a foundation for effective communication, monitoring, and decision-making between the parties involved. The one or more data702are significant as the data provide more information about various SLAs, SLOs, and terms about the organization. The one or more data702brings the specific semantic and logical reasoning with pure context which helps in core decision-making. In an embodiment, the one or more data702for SLA, SLO contracts are generally stored in pdf, word, text, emails, and the like. In an embodiment, the one or more data702are utilized by at least one of: the software architecture, an infrastructure architecture, the information of a selected technology, and the one or more monitoring platforms, for fine-tuning a static model (i.e., the large language model).

The one or more organizations may use one or more service management tools or software to track and manage the SLAs and SLOs. The one or more service management tools may store the SLAs and SLOs in a structured database or the computer-implemented system102, making the one or more service management tools easier to monitor and report on performance of the one or more applications. The one or more data702is high structured by nature in terms of SLAs and SLOs, metrics and rules. The one or more data702need to be ingested into the computer-implemented system102.

In an embodiment, there may be references and discussions about the SLAs and SLOs in unstructured text documents, emails, and communications logs. In such a scenario, the natural language processing model is used to extract and analyse one or more relevant information from the unstructured text/data704. In an embodiment, the generative pre-trained transformer (GPT)706may provide a wealth of information associated with the SLAs, SLOs and associated metrics in a generic context. The one or more data from the GPT706provides for the generic semantics and logical context to build out the meta framework for SLAs, SLOs and associated metrics.

In an embodiment, the one or more data702from the one or more sources need to be cleansed of any irregularities. In an embodiment, the one or more data702are processed (i.e., pre-processing of the one or more data702) to determine the one or more semantics and structure of the one or more natural language texts associated with the one or more service level agreements (SLAs). The processing of the one or more data includes breaking the one or more natural language texts associated with the one or more service level agreements (SLAs) to be learned by the trained first artificial intelligence (AI) model to determine the one or more semantics and structure of the one or more natural language texts.

For example, the service level statement “SLO for performance refers to minimum response rate of less than 30 ms for web applications” is broken to determine how the large language model learns from the documents. In an embodiment, the web application may be selected to explain the semantic analysis, structure analysis, and implicit rules. In the semantic analysis, a meaning statement describes a service level objective (SLO) related to the performance of the web applications. Specifically, the meaning statement specifies a criterion for performance, stating that the minimum response rate should be less than 30 milliseconds (ms). This means that the web application should ideally respond to user requests within this timeframe to meet the defined performance standard. In the semantic analysis, a context is within the realm of web development and performance optimization. The SLOs are common metrics used to set goals and to measure the performance of the web applications. The context further implies that achieving a response rate of less than 30 ms is considered desirable for optimal user experience and efficient operation of the web application.

In the structure analysis, a syntax of a statement is clear and grammatically correct. The syntax follows a standard sentence structure with at least one of: subject (“SLO for performance”), verb (“refers to”), and object (“minimum response rate of less than 30 ms for web applications”). In the structure analysis, a logical structure may have a logical flow, initiated with a definition of the SLO for performance and then specifying the criterion for measuring performance in terms of response rate. In the structure analysis, a technical structure of the statement lies in its quantification of performance metrics (e.g., response rate) and the specific threshold set (e.g., less than 30 ms). This provides a clear and measurable goal for assessing the performance of the web applications. The implicit rule specifies the response rate of the web applications that should be less than 30 milliseconds to meet the performance criteria.

In summary, the semantic analysis specifies the meaning and context of the statement within a domain of IT application management and performance optimization. The structure analysis may examine the grammatical and logical organization of the statement, along with its technical specifications regarding performance metrics and thresholds. The system is configured to handle more complex scenarios and applications where the one or more data are available to make decisions based on reasoning and act of the one or more data.

FIG.8illustrates a block diagram800representing a process (i.e., pre-process) of one or more data by the artificial intelligence (AI) model to determine one or more semantics and structure of one or more natural language texts, in accordance with an embodiment of the present disclosure. The one or more data obtained from various sources may be inputted through the preprocessing flow. The one or more data may be pre-processed when the one or more data are not well defined. This is the system's way of learning about core objectives, the metrics associated with the rules of the target system, and uses the information to generate knowledge base along with the large language model which has capabilities in understanding, evaluating, and recommending how the target system is compliant with the defined goals. The one or more data that are ingested and understood may be stored in a vector database802.

FIG.9illustrates a block diagram900representing a fine-tuning process of the artificial intelligence (AI) model, in accordance with an embodiment of the present disclosure. The large language model may understand the one or more semantics, structure, and context of the specific language which is trained. Extending the large language models to understand the semantic, structure and context of “SLA, SLO automation” domain and further understanding the real-time data which comes about as a part of monitoring the applications provide the models the ability to (a) understand a current state of the target system, (b) evaluate how compliant is the target system from the defined goals, (c) recommend the one or more actions based on intelligent insights, (d) continuous learning and evaluate new information, and (e) drive the target system towards optimized compliance. In an embodiment, the vector database802may have at least one of: specific data, generic and other data types.

The large language model is configured to perform complex reasoning tasks requiring expert knowledge across a wide range of fields, including in specialized domains such as programming and creative writing. The large language model may interact with humans through intuitive chat interfaces, which has led to rapid and widespread adoption among public. In an embodiment, the capabilities of large language models are focusing the seemingly straightforward nature of the training methodology. In an embodiment, auto-regressive transformers are pretrained on an extensive corpus of self-supervised data, followed by alignment with human preferences via techniques such as Reinforcement Learning with Human Feedback (RLHF). Further, the large language models may be a Linguistic Latent Attribute model (Llama 2) that is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters.

In an embodiment, the large language model (i.e., trained first artificial intelligence (AI) model) is fine-tuned with the determined one or more semantics and structure using one or more techniques including at least one of: few shots learning, chain of thoughts, tree of thoughts, ReACT, symbolic reasoning, self-consistency, automatic reasoning, and tool use. One or more trajectories902, as shown inFIG.9, are used to fine-tune the Llama 2 model and the trajectories902may have information associated with SLAs, SLOs and metrics and the information is updated in the knowledge graph904.

FIG.10illustrates a block diagram1000representing generation of one or more instruction sets for fine-tuning the artificial intelligence (AI) model based on the one or more trajectories, in accordance with an embodiment of the present disclosure. Training the large language model for downstream or specialized tasks may require the generation of the one or more instruction sets. The generation of the one or more instruction sets may provide power of reasoning and acting for the large language model, without which the large language model may be ineffective.

The one or more instruction sets are used for fine tuning the large language model are based on ReACT trajectories. The one or more data which are stored in the vector database802may have private data (i.e., obtained from the documents) and public data (i.e., obtained from gpt3, and google search). The one or more data702need to be converted into the one or more instruction sets, as a first set and the one or more data obtained from the vector database802need to be converted into one or more question answer formats1002to cover the reasoning and facts. The one or more question answer formats1002may include HotpotQA, StrategyQA, and Massive Multitask Language Understanding (MMLU).

The HotpotQA is a question-answering dataset configured to test complex reasoning and the ability to retrieve relevant facts from multiple documents to answer a query. The StrategyQA is configured to challenge the reasoning capabilities of the large language models by requiring the large language models to understand implicit questions and perform multi-step inference to arrive at a yes/no answer, often without direct evidence. The Massive Multitask Language Understanding (MMLU) is a diverse collection of multiple-choice questions across 57 subjects, aimed at evaluating an AI's breadth of knowledge and its understanding in a wide range of topics from academic subjects to professional domains.

The question sets from HotpotQA, StrategyQA, and Massive Multitask Language Understanding (MMLU), which each question set present unique challenges in reasoning and knowledge. The question sets are used to generate the one or more data for training the large language models. By utilizing various prompting techniques1004including at least one of: Chain of Thought (CoT), ReAct, and Reflexion, different styles of problem-solving are invoked, leading to diverse trajectories. These trajectories simulate a human-like reasoning process in the large language model, allowing the large language model to generate answers or solutions that are more aligned with how a person might think through a problem. This diversity in training helps improve the large language model's ability to handle a wide range of tasks and increases its overall robustness and effectiveness. Below is table explaining the specifics on QnA sets.

QnA Technique# of QnA SetPrivate, Public splitHotpotQA49275/417MMLU33578/257StrategyQA45371/382

The multi-prompt method1004may be configured to apply few-shot prompting to generate ReAct trajectories with a strong large language model, including GPT-4. This process includes creating thought-action-observation rounds to guide the large language model through task-solving processes. The multi-prompt method1004may be further configured to convert Chain of Thought (CoT) prompts into one-round ReAct trajectories where the “thought” is the reasoning step, and the “action” is the answer to the question. The multi-prompt method1004may be further configured to provide Reflexion trajectories by prompting for reflections at specific rounds, allowing the large language model to adjust its strategy in solving the one or more tasks.

In an embodiment, the large language model may utilize a search tool (e.g., Google search tool) including SerpAPI, to assist the large language model in retrieving the knowledge, which is a part of action steps in ReAct trajectories. The search tool may provide concise and relevant results to support task-solving capabilities of the large language model. In another embodiment, the large language model may utilize the prompting methods1004and tasks to generate a wide range of the ReAct trajectories, which are instrumental in the fine-tuning process. The aim is to expose the large language model to a broad spectrum of problem-solving scenarios. In an embodiment, the diverse set of ReAct trajectories as training data to fine-tune a smaller language model through a process analogous to knowledge distillation. This may result in the smaller language model learning to solve one or more tasks more effectively by mimicking the thought-action-observation process. The fine-tuning process explicitly aims to enhance data diversity by combining multiple training tasks and prompting methods.

In an embodiment, the fine-tuned language model is deployed for inference without the need for few-shot prompting, thereby making the process more efficient. In an embodiment, the fine-tuned agent may be capable of adapting its method based on task complexity, providing strong generalization and robustness because of the diverse learning support that the language model is received during training. In an embodiment, the performance of the fine-tuned language model is evaluated using a set of 500 dev questions from HotpotQA and questions from other datasets. In an embodiment, the evaluation focuses on exact match (EM) scores to determine an accuracy of the fine-tuned language model in answering the questions correctly.

In an embodiment, the evaluation results may be iterated to optimize the fine-tuning process. If the fine-tuned language model provides significant improvement in terms of efficiency and accuracy, the system validates the effectiveness of the fine-tuned language model. Based on the above procedures, the final ReAct trajectories are generated, leading to the fine-tuned language model that may outperform both few-shot prompted language models and those that have not undergone such systematic fine-tuning. This approach aims to harness the strengths of reasoning and acting in tandem to improve the overall performance of language agents.

In an embodiment, the large language models under new context based on fine-tuning which essentially is sending a set of prompts (e.g., few shot learning) to complete chain of thought derivation from human way of thinking which involves thought, action, observation and taking the trajectory (e.g., ReACT) with Reflexion which involves feedback and self-reflection specific data. More often ReACT and Reflexion are advanced prompting techniques which are also known as the trajectories in that the problem statement is outlined and a series of sequential actions to achieve the desired goal is provided.

FIG.11illustrates a schematic diagram1100representing the one or more semantics and structure of the one or more natural language texts associated with the one or more service level agreements (SLAs) and the one or more service level objectives (SLOs), embedded into the fine-tuned artificial intelligence (AI) model, in accordance with an embodiment of the present disclosure. The fine-tuned language model is trained on private data from the one or more documents (which generally includes contract definitions for SLA, SLO and goals to be achieved by corporate) and public data associated with the SLA, SLO and metrics associated with domain of choice. This combined private and public data provides rich semantics and structure understanding of SLA and SLO, which is embedded into the fine-tuned language model and the knowledge graph.

The fine-tuned language model good at providing the information associated with the SLA, SLO and associated metrics, is static due to the nature of data is inputted. In an embodiment, the fine-tuned language model may reason to a certain extent with such static data. The static model is fine-tuned with the relevant information is an initial step of the process. For generating the one or more actions to ensure the SLO and associated metrics measurements meet the SLA, requires comprehensive understanding of the real-time monitoring data of the application for the language model. In an embodiment, the static model may be fine-tuned with relevant real-time data to generate the dynamic model.

The real-time data obtained from the one or more monitoring platforms tends to repeatable and verbose in nature due to the frequent captures occurring typically in minutes. Sending the entire dataset for fine-tuning the static model may not be efficient in terms of both cost and accuracy. Moreover, the large language models may not determine time series data or concept of time. Managing the time for LLM requires a distinct set of components.

FIG.12illustrates a block diagram1200representing generation and execution of the dynamic model with the one or more real-time data, such as those show inFIG.11, in accordance with an embodiment of the present disclosure. The one or more data obtained from the one or more monitoring platforms are generally captured every minute, and the one or more data may tend to be very verbose and nature. The relevant data for decision making is usually about 40% of the actual incoming data. The incoming data arrives at a rapid rate needs to be classified, reduced and cull out the relevant data that needs to be used for fine-tuning which poses challenges to the large language model.

In order to extract the relevant data, the system utilizes a small language model (SLM). The small language model (SLM) may perform categorization, data relevancy assessment, reinforcement learning, and other specific areas of IT security and governance to streamline the processing and analysis of security-related data. By leveraging specialized SLMs for specific data types and integrating reinforcement learning techniques, the architecture ensures that only relevant and important data, along with created prompts are passed on to the Large Language Model (LLM) for further analysis. This results in improved decision-making, reduced response times, and enhanced security posture.

FIG.13illustrates a block diagram1300representing extraction of the one or more relevant real-time data from the one or more real-time data using the small language model (SLM), in accordance with an embodiment of the present disclosure. The one or more first real-time data are initially obtained from the one or more monitoring platforms using a data ingestion layer. In an embodiment, the data ingestion layer is configured to determine whether the one or more first real-time data are obtained efficiently and to preprocess the one or more first real-time data to determine whether the one or more first real-time data comprise consistency and compatibility across the one or more monitoring platforms.

Further, the one or more first real-time data may be categorized based on at least one of: one or more types of the one or more first real-time data and the one or more monitoring platforms, using a categorization and routing layer1302. The categorization and routing layer1302is configured to utilize sophisticated algorithms to classify the one or more real-time data accurately and to optimize a routing process of the one or more first real-time data to determine whether the one or more first real-time data are directed to a corresponding small language model (SLM) for analysis of the one or more first real-time data.

Further, the small language model is configured to process the one or more types of the one or more first real-time data and add one or more securities (e.g., IT securities) and governance criteria1306to the one or more first real-time data. In an embodiment, processing of the one or more types of the one or more first real-time data may include (a) assessing the relevancy of the one or more first real-time data using the one or more pre-defined criteria, (b) mitigating the noise by filtering the one or more repetitive data points associated with the one or more first real-time data, and (c) training each small language model (SLM) to recognize the one or more patterns and anomalies within the one or more domains associated with the one or more first real-time data, to identify the one or more security-related events.

Further, the small language model is configured to mitigate the one or more data volumes by eliminating the one or more repetitive data points associated with the one or more first real-time data to determine the importance of each data point associated with the one or more first real-time data, using a data relevance assessment and reduction layer1308. By focusing on data relevancy, the data relevance assessment and reduction layer1308ensures that only the most pertinent information is retained for further analysis, thereby improving the signal-to-noise ratio and enhancing decision-making capabilities.

Further, the small language model utilizes a reinforcement layer1304with reinforcement learning techniques to continuously train and optimize the performance of the small language model based one or more feedback and results associated with the extraction of the one or more second real-time data using a reinforcement learning layer1304through a language model (LM) agent. By adapting to changing data patterns and evolving threat landscapes, the reinforcement layer1304ensures that the SLMs remain effective and relevant over time. The small language model (SLM) is optimized by learning the one or more second real-time data from one or more historical data to analyse security-related one or more second real-time data.

The instance model which has the knowledge of the real-time data (i.e., the one or more relevance real-time data) may be better equipped to provide information and decision making for the one or more organizations. In an embodiment, the instance static model is fined-tuned, by the fine-tuning subsystem218, with real-time data specific to SLA, SLO and metrics which then becomes the instance specific language model. Similarly, the one or more knowledge graphs generated are meta model which hold structure and semantic of language texts. The meta model is a higher-level abstraction defining one or more structure and relationships common to one or more language models within the one or more domains. The one or more knowledge graphs are one or more ontology views with one or more optimized level concepts and one or more meta-relations comprising at least one of: one or more entities, one or more attributes of the one or more entities, and one or more relationships between the one or more entities. The dynamic data obtained from various monitoring platforms help in generating the instance-view KG with fine-grained instances and relations.

FIG.14illustrates a schematic diagram1400representing a fine-tuning process of the dynamic model to arrive at an instance language model and updation of a knowledge graph to arrive at an instance knowledge graph, in accordance with an embodiment of the present disclosure.

FIG.15illustrates a block diagram1500representing an integration process of the one or more real-time data, in accordance with an embodiment of the present disclosure. The real-time data (i.e., instance data) for SLA, SLO, and metrics are captured by monitoring and observability platform. In an embodiment, there are one or more integration processes (e.g., New Relic Integration) which may need code to obtain the monitored specific data for current phase. The instance data pull component1504is configured to extract bulk and real-time data from the one or more monitoring platforms (e.g., New Relic Platform)1502. The Observability Platform1502utilizes NERD API to extract the instance data. In an embodiment, the one or more instance data are stored in interim database1506.

The one or more instance data stored in the interim database1506need to be translated, as shown in1508, into one or more formats for fine-tuning the instance model. The one or more instance data need to be mapped to specific SLA, SLO and metric available in the static model fine-tuned. The mapped data need to be converted into HotpotQA, as shown in1510, and used to fine tune the instance model. The instance model is fine-tuned, as shown in1512, on the real-time data received on a continuous basis. In an embodiment, the one or more knowledge graphs are updated with the instance data to update one or more instance view graphs.

Even though the fine-tuned language model has reasoning capabilities, the language model (e.g., the large language model) may have fall short in the realm of effective action. In order to address the fall short, a concept of Augmented Deep Active learning for text and Planning Trajectories (ADAPT) is proposed. The ADAPT is configured to establish a comprehensive framework that enables LLMs not only to reason and act but also to undergo self-correction and self-reinforcement. The LLMs are perpetually fed and fine-tuned with the real-time data for recognizing the dynamic nature of data.

The fine-tuned language model is optimized to seamlessly orchestrate reasoning and action based the reinforcement learning using a robust framework. The framework is initially configured to generate one or more prompt templates for one or more reasonings that learn dynamically from one or more memory buffers. The framework is further configured to organize one or more future trajectories over an extended horizon using the fine-tuned first artificial intelligence (AI) model (i.e., the fine-tuned large language model). At each juncture, the fine-tuned large language model is configured to generate the one or more actions based on the organized one or more future trajectories. The fine-tuned large language model is configured to collect one or more feedback based on the generated one or more actions for the one or more services and to store the collected one or more feedback in the one or more memory buffers. Subsequently, the reasoning process is recurred to recalibrate the one or more future trajectories, leveraging the updated state from the collected one or more feedback. In an embodiment, the orchestrated interplay between reasoning and action not only enhances the model's adaptability but also ensures a continuous refinement of its decision-making process in response to real-world dynamics.

The learning and planning phases operate as a series of states, initially focusing on sequential complexity. The Markov Decision Process (MDP) becomes instrumental in aiding the development of the one or more future trajectories based on the one or more memory buffers. The process includes (a) generating the one or more future trajectories over a long horizon based on the one or more memory buffers and (b) Replanning the one or more future trajectories from the new state post the LLM's initial action.

FIG.16illustrates a block diagram1600representing an integration process of a reinforcement learning (RL) into the dynamic model (i.e., large language model (LLM)), in accordance with an embodiment of the present disclosure. Incorporating the reinforcement learning (RL)1602into the dynamic model (e.g., the large language model (LLM)) helps overcome challenges in language generation, especially when the LLM generates unclear or incorrect responses. By incorporating the RL1602, the dynamic model may actively learn from human preferences and refine its responses over time. When the LLM generates uncertain or suboptimal outputs, the RL1602facilitates the collection of valuable human feedback, allowing the dynamic model to adapt and enhance its language generation capabilities. This iterative learning process contributes to the creation of more contextually accurate, relevant, and user-friendly responses.

FIG.17illustrates a block diagram1700representing a process of the reinforcement learning (RL)1602into the dynamic model (i.e., large language model (LLM)), in accordance with an embodiment of the present disclosure. The reinforcement learning (LR)1602may play a crucial role in the dynamic model. In automated method, if the dynamic model requires several iterations during a process of matching response quality, an impute logging mechanism is implemented. Specifically, if the iteration count exceeds threshold limits, the reinforcement learning (LR)1602may intervene to generate a response. The impute logging is diligently tracked in all instances to monitor the iteration count and maintain the quality of responses. Even when the response is achieved within a limited number of iterations, the reinforcement learning (LR)1602is implemented on demand to enhance the quality of the response. The impute Logging is crucial in this context as well, ensuring that the intervention and its impact on response quality is tracked appropriately.

FIG.18illustrates a block diagram1800representing an integration process of direct proximal optimization (DPO) within the large language model (LLM), in accordance with an embodiment of the present disclosure. In the incorporation of Reinforcement Learning (RL)1602within the Large Language Model (LLM), a pivotal aspect involves the integration of Direct Proximal Optimization (DPO). The DPO is employed to directly optimize one or more policies by satisfying preferences through a straightforward classification objective. This entails fitting an implicit reward model1802, from which the optimal policy may be extracted in a closed form. The innovative approach presented herein leverages the DPO to enhance the performance of the LLM, marking a distinctive advancement in the field. The DPO is embedded within the LLM model to eliminate constraints of traditional RL separate loops, fostering a streamlined and cohesive framework. This integration enhances synergy between language understanding and RL-based decision making, enabling dynamic policy reinforcement during language generation.

The SLA, SLO automation needs a core decision making framework, which may engage with fine-tuned dynamic model in an intelligent conversation to derive recommendations action based on the current state of the system's SLO and associated metrics. While the dynamic model is getting the real time data from the small language model which in turn gets the one or more data from the one or more monitoring platforms, the act of reasoning and deciding actions needs agent which engages in the meaningful conversation. The framework which performs reasoning to take action is called (ADAPT). Below are the steps of execution for agent which executes reasoning and action for the system in question while interacting with fine-tuned dynamic model to get the intel. The agent mat start off by querying the fine-tuned dynamic model about the initial state of the application or system. The agent generally responds back with C1-S1, C2-S2, C3-S3 . . . CnSm, where C is a component. The system or application may include one or more components.

FIG.19illustrates a block diagram1900representing generation of one or more plan trajectories to accomplish in meeting the one or more service level objectives (SLOs) based on an initial state of the one or more service level objectives (SLOs) and associated metrics, using the fine-tuned large language model, in accordance with an embodiment of the present disclosure.

FIG.20illustrates a block diagram2000representing execution of an agent (Augmented Deep Active learning for text and Planning Trajectories (ADAPT) agent) to generate the one or more actions to be applied to the one or more environments116using the fine-tuned large language model, in accordance with an embodiment of the present disclosure. The large language model (LLM) is initiated by forming an updated posterior of an unknown environment from the one or more memory buffers, emphasizing an optimal trajectory that maximizes the value function during planning. The learning and planning subroutines operate in an “in context” learning manner, emulating an actor-critic update for Markov Decision Processes (MDPs). While the reinforcement learning (RL) revolves around the collecting feedback, tailoring the reinforcement learning (RL) for the large language models (LLMs) poses challenges due to discrepancies between numerical systems in the reinforcement learning (RL) and token-based descriptions in the large language models (LLMs). The large language models (LLMs) are trained on a general corpus and remain fixed throughout the interactive process, making the large language models different from traditional reinforcement learning (RL) actors and critics.

In order to address these conceptual discrepancies, the proposal is to formalize reasoning and acting under an MDP framework, with a latent variable of interest being an unknown environment. The starting point is the full history of states, providing a structured approach to optimize performance, meet SLAs, and manage resources responsibly. The idea is to formalize reasoning and acting under the MDP where the latent variable of interest is unknown environment. A starting point is a full history of states. For example, the application's operational states for this specific project are denoted as S={S1, S2, S3}, where S1 represents an initial state, S2 represents does not meet, and S3 represents a final state.

The above said states may serve as representations of the application's performance, emphasizing the goal of optimizing its functionality to consistently meet the SLA. However, achieving this objective must be balanced with resource considerations, as resources come with associated costs. Striking an optimal balance involves ensuring the application meets the SLA within defined resource constraints, considering factors including at least one of: cost, compliance, governance, and other infrastructure limitations. In essence, the challenge lies in optimizing performance while responsibly managing and allocating resources to achieve the desired operational state. In an embodiment, the information state at the initial point includes at least one of: (a) full cast of history of states and (b) action, rewards, and their linguistic summaries. The information states are loaded into the one or more memory buffers, in the Bayesian MDP. In an embodiment, the information state at the initial point is a snapshot of a situation from the beginning and includes (a) a complete record of what has happened in the past (i.e., history of states) and (b) information associated with the actions taken, the rewards received, and a summary of these in everyday language.

Further, the comprehensive information state is loaded into the one or more memory buffers, which is the decision-making system. The one or more memory buffers store all this knowledge for future reference. In the context of the Bayesian MDP, using a probabilistic approach, the information may not be fixed, acknowledging that there's uncertainty, and the representation of the knowledge as probabilities. As the system interacts with the outside environment116, the information state may collect feedback. This feedback is like the results of the actions (e.g., what worked well, what didn't, and the outcomes the system observes). with each step of our decision-making process, use the feedback gathered to determine what action would be best. The system learns from experience and performs the process based on historical data. In an embodiment, external reasoning suggests that the decision-making process is not happening in isolation. The information is considered from the external environment116, learning from the environment116and updating the one or more actions accordingly.

FIG.21illustrates a schematic diagram2100representing learning and planning procedures of the ADAPT agent, in accordance with an embodiment of the present disclosure. In the beginning, the one or more memory buffers are initialized to store information associated with agent's interactions with the external environments116. As the agent interacts with the external environment116, the one or more memory buffers sequentially record the information at each time. The recorded information may be at least one of: (a) a current state of the external environment116, the one or more actions taken by the agent based on the current belief state, (c) sensory information received from the environment116, considering partial observability in Partial Observable Markov Decision Processes (POMDPs), and an immediate reward obtained by the agent.

In an embodiment, In the POMDPs, the belief state buffer is crucial. After each observation, the belief state buffer is updated based on the current observation and the previous belief state. This reflects the agent's evolving understanding of the environment116. In another embodiment, a learning subroutine of the ADAPT agent is invoked at periodically or after each interaction. The learning subroutine is configured to access the historical data in the one or more memory buffers, including states, actions, observations, rewards, and belief states. The learning subroutine is further configured to update the internal model based on the collected feedback. In the POMDPs, the learning subroutine involves adjusting the transition and observation models considering the belief state updates.

Further, the planning subroutine is triggered to generate one or more optimal trajectories for future steps. The planning subroutine is configured to access the one or more memory buffers to retrieve one or more relevant historical data. For the POMDPs, the belief state is integrated into the planning process, allowing the agent to plan the one or more trajectories that account for uncertainty and partial observability. In each time step, the agent makes decisions based on the current belief state and planned one or more trajectories. The decision incorporates the uncertainty inherent in the POMDPs, providing a realistic representation of decision-making under partial observability.

As the agent continues to interact with the environment116, the one or more memory buffers are updates in real-time. In an embodiment, new entries are added, and older entries may be compressed or cleared, ensuring the one or more memory buffers remain manageable. In an embodiment, the system's performance is validated empirically, considering scenarios that reflect partial observability. The memory buffer's effectiveness is assessed in capturing relevant information for learning, planning, and decision-making. Based on the empirical results, the system iteratively refines at least one of: the configuration of the one or more memory buffers, the learning subroutine, and the planning subroutine parameters. In an embodiment, the system is fine-tuned to achieve better performance in real-world scenarios. Finally, the integration process is documented, the adjustments are made, and an impact of POMDP principles on one or more memory buffer operations, which are communicated to the one or more electronic devices106associated with the one or more users. In summary, the one or more memory buffers seamlessly integrate with the ADAPT agent and the POMDP, providing a comprehensive mechanism for learning, planning, and decision-making in scenarios with partial observability and complex dynamics.

In an embodiment, the state may be characterized by partial observability due to shared and virtualized nature of an infrastructure supporting the application. Observations are limited to how the application performs under constraints, influenced by a load placed on a underlying physical resource. The application states, denoted as S={Sinit, Sdoes-not-meet, Sfinal}, serve as representations of the application's operational status. In an embodiment, Sinit represents an initial state, Sfinal represents optimal performance meeting or exceeding SLA standards, and Sdoes-not-meet represents varying degrees of SLA non-compliance.

In an embodiment, the state space is continuous, allowing nuanced adjustments, especially in the Sdoes-not-meet state, which quantifies the percentage of SLA achievement. The overarching objective is SLA fulfilment, but within the crucial constraint of resource costs. This constraint adds a layer of complexity, as optimizing performance that must align with financial considerations. In an embodiment, striking a balance between meeting SLA targets and navigating constraints like cost, compliance, governance, and infrastructure limitations adds a compelling dimension to the problem at hand.

In an embodiment, in the Markov Decision Processes (MDPs), the state space may pose challenges, especially when dealing with large or continuous state spaces. One primary issue is the computational complexity associated with exploring and representing the entire state space. As the number of states increases, algorithms may struggle to efficiently compute optimal policies, estimate value functions, or perform dynamic programming updates. However, the Partially Observable Markov Decision Processes (POMDPs) may address some of the challenges posed by the MDPs, particularly in situations where the true state is not fully observable. In many real-world scenarios, the agent may not have complete knowledge of the underlying state. The POMDPs explicitly model the partial observability by providing observations. The observations may provide indirect information about the true state, allowing the agent to update its belief or probability distribution over possible states. Instead of explicitly tracking the full state space, the POMDPs may utilize a belief state, which is a probability distribution over possible states given past observations and actions. The belief state may capture the uncertainty about the true state and is updated as the agent receives new observations.

In an embodiment, the POMDPs incorporate observation models that define the likelihood of observing certain data given the true state. These observation models allow the agent to make inferences about the state based on observations, even in cases of partial observability. The POMDPs may include the one or more actions configured to gather information and reduce uncertainty. The one or more actions, often referred to as sensing or exploration actions, enable the agent to strategically select observations that provide the most valuable information for decision-making. Instead of directly mapping from states to the one or more actions (as in MDPs), the POMDPs involve policies that operate over the belief states. A policy in the POMDP maps from belief states to the one or more actions, taking into account the uncertainty inherent in partial observability. In an embodiment, the POMDPs is solved to determine policies that maximize expected cumulative rewards over time. Various techniques, including at least one of: point-based methods, Monte Carlo methods, and approximate dynamic programming, have been developed to efficiently compute approximate solutions to POMDPs. While the POMDPs provide a framework for modelling decision-making under uncertainty, the POMDPs also introduce their own set of challenges, such as increased computational complexity due to the belief state representation. Nonetheless, the POMDPs offer a more realistic representation of decision problems in uncertain environments, making the POMDPs suitable for a wide range of applications, including robotics, autonomous systems, and human-machine interaction.

In an embodiment, the Partially Observable Markov Decision Processes (POMDPs) may address a state space problem focused in the Markov Decision Processes (MDPs) by introducing the concept of belief states and explicitly modeling the partial observability of the underlying system. In the POMDPs, instead of representing the state space directly, the agents may maintain a belief state, which is a probability distribution over possible states given the history of observations and actions. The belief state captures the agent's uncertainty about the true state, incorporating information from observations and actions. The POMDPs may incorporate observations, which are indirect and often noisy indicators of the true state. The observation models may define the probability of observing certain data given the true state. These observation models may help the agent to update its belief state based on the observed information. In an embodiment, the POMDPs may explicitly model situations where the agent cannot directly observe the true state. This is particularly relevant in real-world scenarios where complete information may be unavailable. By including observations and belief states, the POMDPs enable the agents to make decisions considering partial observability, taking into account the uncertainty about the true state.

The POMDPs allow for the inclusion of actions configured to gather information. These actions, often referred to as sensing or exploration actions, enable the agent to strategically select the observations that provide the most valuable information for decision-making. The Information-gathering actions may help in reducing uncertainty in the belief state, improving the agent's ability to make informed decisions. In the POMDPs, the policies map from belief states to the actions, acknowledging the inherent uncertainty in the observed data. Instead of selecting the actions based on the current state (as in MDPs), the POMDP policies consider the distribution of possible states captured in the belief state. In an embodiment, the POMDPs is solved to determine policies that maximize the expected cumulative rewards over time. Various techniques, including at least one of: the point-based methods, the Monte Carlo methods, and the approximate dynamic programming, have been developed to efficiently compute approximate solutions to POMDPs. The above said methods may exploit the structure of the belief space to navigate the decision-making process under partial observability. In an embodiment, by introducing the belief states and explicitly modelling partial observability, the POMDPs provide a more realistic and flexible framework for decision-making in uncertain environments, effectively addressing the challenges associated with the state space problem seen in traditional MDPs.

In an embodiment, in the context of Partially Observable Markov Decision Processes (POMDPs), an action space refers to a set of possible actions that an agent may take in each state. Unlike in fully observable environments, where the agent has complete information associated with the current state, the POMDPs involve uncertainty and partial observability. In the POMDPs, the agent may not directly observe the underlying state but may receive observations that are probabilistically related to the true state. The action space may encapsulate all the possible decisions or moves that the agent may make to influence the system. Each action in the action space is associated with a policy, defining the agent's strategy for selecting actions based on its current belief associated with the state.

In an embodiment, the complexity of the action space in the POMDPs arises from the need to make decisions under uncertainty. Since the agent may not have full knowledge of the state, the agent must consider the possible observations it might receive and select actions that are robust across different potential states. This involves reasoning about the uncertainty in the environment116and updating the belief state accordingly. The configuration of the action space is a critical aspect of POMDP formulation. The configuration requires at least one of: system dynamics, information provided by observations, and agent's objective. The agent aims to select actions that maximize expected cumulative rewards over time, taking into account the uncertainty in its knowledge.

In an embodiment, the action space may include four distinct actions, each associated with a corresponding function. The four distinct actions may include scale_up action, scale_down action, restart_service action, and rollback_updates action. The scale_up action may suggest increasing capacity or resources allocated to the system. The scale_up action is commonly employed to handle increased demand or workload. Scaling up may involve adding more servers, increasing computing power, or expanding resources to improve system performance. The scale_down action may involve reducing the allocated resources or capacity of the system. Scaling down is often used when the demand or workload decreases, allowing for cost savings using fewer resources. The restart_service action implies stopping and then restarting a particular service or component within the system. Restarting a service may help resolve issues, apply updates, or refresh the system to an initial state. The rollback_updates action may involve reverting to a previous state or version of the system, typically undoing recent updates or changes. The rolling back updates is a strategy used when new changes introduce unexpected issues, and returning to a known and stable state is necessary.

These actions are commonly associated with managing the scalability, reliability, and stability of the system. The selection of action depends on the specific requirements, challenges, and goals of the system and the environment116in which the action is performed. Incorporating these actions into an action space allows an agent or the system to dynamically respond to changing conditions, optimizing its performance and resource utilization.

In an embodiment, the ADAPT (i.e., the reason for acting and acting for the reason) architecture, configured in a context of the reinforcement learning, may provide a framework for combining reasoning and decision-making. While the POMDPs (Partially Observable Markov Decision Processes) and the ADAPT share a common goal of addressing decision problems under uncertainty, the POMDPs and ADAPT together operate at different levels and may be complementary. In an embodiment, the POMDPs are configured to handle decision problems where the underlying system is only partially observable. The POMDPs explicitly model uncertainties in the state space and observations, allowing agents to make decisions under incomplete information. In an embodiment, the Augmented Deep Active learning for text and Planning Trajectories (ADAPT) agent with the Partial Observable Markov Decision Processes (POMDPs) is configured to monitor the one or more services for adjusting one or more governance principles in the one or more environments (i.e., in one or more uncertain environments).

The POMDPs may utilize a belief state, which is a probability distribution over possible states given past observations and actions. The belief state may capture the agent's uncertainty about the true state of the system. The ADAPT's emphasis on reasoning aligns with the concept of belief states in the POMDPs. The reasoning may involve understanding the current state of the system, considering available information, and making the decisions based on a logical thought process. The ADAPT may introduce an idea of “Reason for Acting,” emphasizing the importance of understanding the reasons behind an action. This aligns with the notion in the POMDPs that agents make the decisions based on their belief about the true state, derived from observations. The ADAPT may provide an architecture where reasoning and acting are integrated, allowing for a more coherent and explainable decision-making process. This integration may complement the POMDPs by providing a mechanism for agents to reason about their beliefs before taking actions.

The ADAPT may emphasize self-correction and learning from experience. In the context of the POMDPs, agents may use feedback from observations to update their belief states and improve their decision-making strategies over time. In an embodiment, both ADAPT and POMDPs may be adapted to manage the one or more real-time data. The POMDPs may incorporate observations into the belief state, and ADAPT's self-correction mechanism allows the agents to adapt to changing conditions. The ADAPT's approach to acting for a reason resonates with the flexibility of action selection in POMDPs. In an embodiment, the agents may select the actions based on their belief state and the information gained from observations.

In an embodiment, the ADAPT is a broader architecture that encompasses reasoning and acting more generally, whereas the POMDPs specifically address decision problems under partial observability. The integration of the ADAPT with the POMDPs would involve leveraging the principles of reasoning and self-correction in conjunction with the belief state representation and decision-making strategies inherent in the POMDPs. The specific implementation details may depend on the application and the requirements of the decision problem at hand.

FIG.22illustrates a block diagram2200representing an end-to-end execution for generating the one or more actions to be applied to the one or more environments116, in accordance with an embodiment of the present disclosure. The end-to-end execution for generating the one or more actions involves in generating the static model and the knowledge graph. The autonomic system is built with various processes starting from ingesting the Service Level Agreement (SLA) to taking corrective actions based on the insights to address any malfunctions within the system. The one or more SLAs, including the metric-level Service Level Objectives (SLOs), serve as the contractual agreement defining the structure and operational parameters for the overall contract. This primarily static information may provide a high-level blueprint for achieving the SLO objectives. The Artificial Intelligence (AI) model may parse and analyze this static agreement, learning its structure and semantics, as an initial step toward building the AI model aimed at achieving the SLOs. These foundational models extend existing language models to understand SLAs, SLOs, and metrics from various documents, including contractual agreements, software architecture, and infrastructure architecture. Concurrently, a knowledge graph is generated using the same information gathered for the static model.

Further, the dynamic data or the real-time data are obtained from the one or more monitoring platforms. The dynamic data gathering and filtering involves handling the real-time data captured from the one or more monitoring platforms, often received at a rapid rate. The real-time data captured from the one or more monitoring platforms may include considerable amount of irrelevant information. The small language model (SLM) component is utilized for categorizing, assessing data relevance, and employing the reinforcement learning techniques to filter out irrelevant real-time data. By leveraging specialized SLMs for specific data types and integrating the reinforcement learning, The relevant real-time data, along with generated prompts, are passed on to the large language model (LLM) for further analysis. This optimized process leads to improved decision-making, reduced response times, and enhanced security posture.

Further, the dynamic model is continuously updated with the real-time data specific to SLAs, SLOs, and metrics, enhancing its ability to provide timely and accurate decision-making support for the enterprise. This involves fine-tuning the initial static model with the real-time data, resulting in an instance-specific language model. Similarly, the knowledge graph evolves into a meta-model, providing a framework for creating specific models within the domain by defining entities, attributes, and relationships. The dynamic data that are produced by the services, are collected by the one or more monitoring platforms contribute to building an instance-view knowledge graph with fine-grained instances and relations.

Further, in decision making using the dynamic model, the ADAPT agent interacts with the dynamic model to retrieve the initial state of SLOs and metrics. Based on this initial state, the ADAPT agent queries the dynamic model to formulate an initial plan to achieve the goal of meeting the SLOs. The Dynamic Model employs learning and planning procedures, resembling actor-critic updates for the Partially Observable Markov Decision Processes (POMDPs), to generate the optimal actions, to be applied to the one or more environments116, based on the current state and probabilities of meeting the SLOs. In an embodiment, the actor-critic RL loop may be used to generate the actions becoming more accurate and precise. The Critic model evaluates suggested actions, providing feedback to refine the decision-making process iteratively until all components are in a satisfactory state. This iterative process ensures continual improvement and adaptation of the policy of taking actions, by the actor, to adapt with the evolving conditions within the environment116and in turn improves the learning and planning procedure. The improved actions are taken on the one or more environments116with an objective to the meet the SLO thereby completing the closed loop.

FIG.23illustrates a flow chart2300representing a computer-implemented method for automatically managing the one or more applications in the one or more environments116using the artificial intelligence (AI) driven autonomic application management framework, in accordance with an embodiment of the present disclosure.

At step2302, the one or more items of data from at least one of: one or more electronic devices106associated with the one or more users and the one or more databases104. In an embodiment, the one or more data may include information associated with the one or more service level agreements (SLAs) corresponding to the one or more applications in the one or more environments116. The one or more service level agreements (SLAs) associated with the one or more applications may include the one or more natural language texts.

At step2304, the one or more semantics and structure of the one or more natural language texts associated with the one or more service level agreements (SLAs) are determined based on analysis of the one or more natural language texts associated with the one or more service level agreements (SLAs), using the first artificial intelligence (AI) model (i.e., the large language model (LLM)).

At step2306, the one or more service level objectives (SLOs) and associated metrics corresponding to one or more services specified in the one or more service level agreements (SLAs) are extracted based on the determined one or more semantics and structure of the one or more natural language texts associated with the one or more service level agreements (SLAs), using the first artificial intelligence (AI) model.

At step2308, the one or more first real-time data including the one or more actual performance levels, and the one or more service level indictors (SLIs), of the one or more services associated with the one or more applications, are obtained from the one or more monitoring platforms.

At step2310, the first artificial intelligence (AI) model (e.g., the large language model (LLM)) determines whether the one or more actual performance levels of the one or more services associated with the one or more applications, are compliant with the one or more expected performance levels by comparing the one or more actual performance levels with one or more pre-defined key performance indicators (KPIs) and the one or more pre-defined goals of the one or more services defined in at least one of: the service level agreements (SLAs) and the service level objectives (SLOs).

In an embodiment, the first artificial intelligence (AI) model is trained with the one or more pre-defined rules and criteria to assess the one or more pre-defined key performance indicators (KPIs) and the one or more pre-defined goals defined in at least one of: the service level agreements (SLAs) and the service level objectives (SLOs).

At step2312, the one or more service level objectives (SLOs) and associated metrics corresponding to the one or more services specified in the one or more service level agreements (SLAs), are automatically updated based on the deviations of the one or more actual performance levels of the one or more services from the one or more expected performance levels, using the first artificial intelligence (AI) model.

At step2314, the one or more insights associated with one or more actions, to be applied to one or more corresponding services, to be performed to automatically manage the one or more applications, are generated, based on the automatic updates on the one or more service level objectives (SLOs) and associated metrics corresponding to the one or more services specified in the one or more service level agreements (SLAs), using the first artificial intelligence (AI) model.

At step2316, the information associated with the one or more actions, to be applied to the one or more corresponding services to automatically manage the one or more applications, are provided to the one or more environments116.

The present invention has following advantages. The present disclosure provides the computer-implemented system102and the computer-implemented method2300for the artificial intelligence (AI) driven autonomic application management framework to automatically manage the one or more applications in the one or more environments116. The present disclosure with the computer-implemented system102is configured to eliminate the need for specialized syntax expertise. Through dynamic resource management, the artificial intelligence (AI) driven autonomic application management framework ensures optimal resource utilization without manual intervention, enhancing both efficiency and cost-effectiveness.

Furthermore, the present disclosure seamlessly integrates critical monitoring and security tools into the application system, eliminating challenges of standalone packaging. Automated issue resolution and efficient error root-cause analysis streamline operations, reducing manual effort and improving reliability. The present invention is configured to perform user-friendly service level agreements (SLAs) to service level objectives (SLOs) conversion, continuous performance monitoring, and the ability to self-heal and self-correct due to closed-loop nature. The present disclosure enables generating a legal document, based on translating the document into metrics to be measured, without expertise or training required to write in a specific language.

The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.

The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer-readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention. When a single device or article is described herein, it will be apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be apparent that a single device/article may be used in place of the more than one device or article, or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.

The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.

Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limited, of the scope of the invention, which is outlined in the following claims.