Abstract: Methods, systems, and computer-readable storage media for extracting and matching key attributes from documents are disclosed. Primary documents from various data sources are received. Primary key attributes are extracted from primary documents using fine-tuned Large Language Models (LLMs). Secondary key attributes are determined from secondary documents, using fine-tuned LLMs. Primary key attributes and secondary key attributes are validated, using fine-tuned LLMs. A similarity between primary key attributes, and secondary key attributes is determined based on results of validation. Ontology scores for each of primary key attributes and secondary key attributes are computed, based on similarity. Matched attribute pairs are determined by comparing ontology scores with first predefined threshold value. Gap level between each of matched attribute pairs is identified using fine-tuned LLMs. Transitions to be applied to each of matched attribute pairs are recommended, based on gap level.

Abstract: Systems and methods for processing documents using Large Language Models (LLMs) are disclosed. A system receives primary documents and creates a training dataset by extracting primary key attributes from the primary documents. The system further determines an appropriate LLM, fine-tunes the determined appropriate LLM using the training dataset, evaluates a performance of the fine-tuned LLM, and creates the determined appropriate LLM by updating the appropriate LLM based on the evaluation. The system further receives secondary documents and extracts secondary key attributes from the secondary documents using the updated fine-tuned LLM. The system further filters similar key attributes from the secondary key attributes, determines a proficiency level of the filtered similar key attributes using the updated fine-tuned LLM, and determines final extracted data based on the determined proficiency level.

Abstract: The technology described herein is directed towards resource consumption forecasting, e.g., with KUBERNETES (K8S). Artificial intelligence/machine learning model-based forecasting of future resource needs can operate with respect to multi-cloud and hybrid cloud environments. Future resource consumption needs such as CPU/memory/disk/network usage is based on a weighted combination of historical resource consumption records, platform (e.g., K8S) configuration changes, target business demands and different software versions of the platform. Based on the forecasted resource consumption needs, IT departments can perform budget planning following the company's budget cycles. Different strategy level considerations such as minimizing total cost or maximizing delivery speed can be designed in resource plans. The system adapts to dynamic environment changes.

Abstract: Architectures and techniques are described that can generate or receive a dependency graph of a microservices platform. The dependency graph can be constructed based on run time operation of microservices such that the nodes of the graph can represent microservices and the edges can represent the interactions during run time. The dependency graph along with an anomaly pattern can be embedded into an embedding space, and based on an examination of the embedding space, it can be determined whether the anomaly pattern exists in the dependency graph.

Abstract: Architectures and techniques are described that can generate or receive a dependency graph of a microservices platform. The dependency graph can be constructed based on run time operation of microservices such that the nodes of the graph can represent microservices and the edges can represent the interactions during run time. The dependency graph along with an anomaly pattern can be embedded into an embedding space, and based on an examination of the embedding space, it can be determined whether the anomaly pattern exists in the dependency graph.

Abstract: A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: receiving data center asset workload data; capturing a set of the data center asset workload data at predefined time intervals; monitoring data center workload performance using the set of the data center asset workload data; and, predicting an anomaly associated with data center workload performance based upon the monitoring.

Abstract: A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: receiving data center asset telemetry information; processing the data center asset telemetry information to provide a performance indicator metric; training a resource utilization machine learning model using the performance indicator metric; and, using the resource utilization machine learning model to provide a data center asset resource utilization result.

Abstract: A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: receiving data center asset telemetry information; processing the data center asset telemetry information to provide a performance indicator metric; training a resource utilization machine learning model using the performance indicator metric; and, using the resource utilization machine learning model to provide a data center asset resource utilization result.

Abstract: A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: receiving data center asset workload data; capturing a set of the data center asset workload data at predefined time intervals; monitoring data center workload performance using the set of the data center asset workload data; and, predicting an anomaly associated with data center workload performance based upon the monitoring.