Abstract: System and method for managing virtual machines (VMs) is disclosed. The method includes, identifying, using a map of VMs, a set of candidate VMs that are predicted to consume less power over time than the first VM for a common workload, removing from consideration any of the candidate VM from the set of candidate VMs that fails to satisfy any predetermined criteria, performing tradeoff analysis on the remaining candidate VMs based on one or more parameters to identify the candidate VMs for redeploying the software operations and ranking the identified candidate VMs based on at least predicted power consumption data for the common workload. The method further includes, selecting, based on the ranking, one of the identified candidate VMs as the second VM, redeploying the software operations from the first VM to the second VM, rerouting data inflow to the first VM to the second VM, and terminating operations of the first VM.

Abstract: Methods, systems, and computer-readable media for ranking large language models (LLMs). Input including list of LLMs, list of hardware and artificial intelligence (AI) prompt are provided by the user for ranking the LLMs. Based on the input, first estimating minimum number of hardware units needed to process AI prompt on each LLM/hardware combination and second estimating time to process the AI prompt using each LLM/hardware combination. Based on minimum number of hardware units and time to process AI prompt, third estimating amount of energy consumed by each LLM/hardware combination. Based on energy consumed, ranking LLM/hardware combinations for AI prompt. Based on ranking, selecting LLM and hardware, submitting AI prompt to LLM on hardware, and receiving response to submitted AI prompt from LLM.

Abstract: A retraining monitoring system maintains the sustainability of a production machine learning (ML) model system that includes a production ML model retraining platform. The retraining monitoring system collects contextual data from the production ML model system and determines if one or more of a currently-selected architectural options has to be changed for sustainability. An architectural option of the production ML model retraining platform, such as, a processing location is selected from a cloud retraining platform or an on-premises retraining platform by a selection process based on a multi-armed bandit problem. An evaluation of the retraining architecture is dealt with as a reinforcement learning problem to implement one of a periodic retraining architecture or a reactive retraining architecture.

Abstract: Methods, systems, and computer-readable storage media for processing a text prompt including a set of text Information Elements (IEs). Original instruction semantic IEs and original contextual IEs are identified within the text IEs. Some of the original instruction semantic IEs are identified for removal from the text prompt, based on semantic proximity values and internal consistency values of the instruction semantic IEs relative to first predefined criteria, while leaving surviving instruction semantic IEs. Similarly, some of the original contextual IEs are identified for removal from the text prompt due to weak connections with the surviving instruction semantic IEs and other of the contextual IEs based on second predefined criteria, while leaving surviving contextual IEs. Further, a revised text prompt corresponding to the surviving instruction semantic IEs and the surviving contextual IEs is generated and submitted as a query to a GAI system programmed to answer the query.

Abstract: A method for detection of a corrupted sensor including providing a first sensor; identifying one or more correlating sensors to the first sensor; determining a correlation between the first sensor and the correlating sensors according to historical sensor values; obtaining a calculated value of the first sensor based on values of the correlating sensors and the correlation; obtaining a measured value of the first sensor; and determining whether the first sensor is corrupted according to a difference between the calculated value and the measured value of the first sensor.

Abstract: In some examples, recruitment process graph based unsupervised anomaly detection may include obtaining log data associated with a recruitment process for a plurality of candidates, and generating knowledge graphs and graph embeddings. The graph embeddings may be trained to include a plurality of properties such that graph embeddings of genuine candidate hires and fraudulent candidate hires are appropriately spaced in a vector space. The trained graph embeddings may be clustered to generate a plurality of embedding clusters that include a genuine candidate cluster, and a fraudulent candidate cluster. For a new candidate graph embedding for a new candidate, a determination may be made as to whether the new candidate graph embedding belongs to the genuine candidate cluster, to the fraudulent candidate cluster, or to an anomalous cluster, and instructions may be generated to respectively retain or suspend the new candidate.

Abstract: In some implementations, an advisor system may receive a description of a problem to be solved and problem data identifying quantum computing-related and classical computing-related problems. The advisor system may perform natural language processing on the description of the problem and the problem data to respectively generate a problem embedding vector for the problem and to generate embedding vectors that represent the quantum computing-related and classical computing-related problems. The advisor system may process the problem embedding vector and the embedding vectors, with a vector matching model, to determine a semantically closest matching one of the embedding vectors to the problem embedding vector and, accordingly, may generate a recommendation that includes an indication to solve the problem with a classical computing resource, a quantum computing resource, or a combination of a classical computing resource and a quantum computing resource.

Abstract: In some implementations, a device may receive first energy consumption information relating to a set of hardware components of a computing system. The device may receive second energy consumption information relating to a set of virtual machines associated with the computing system. The device may receive third energy consumption information relating to a set of software elements associated with the computing system. The device may determine an energy consumption of the computing system based on the first energy consumption information, the second energy consumption information, and the third energy consumption information. The device may identify, based on the energy consumption of the computing system, an energy optimization associated with a usage context of the computing system. The device may transmit a set of instructions to alter one or more parameters of the computing system to implement the energy optimization for the computing system.

Abstract: In some implementations, a device may receive information identifying a computing system for energy management, the computing system having a set of hardware components, a set of virtual machines, and a set of software entities. The device may generate a digital twin of the computing system for simulation of the set of hardware components, the set of virtual machines, and the set of software entities. The device may determine, using the digital twin of the computing system, a set of energy consumption metrics, for the computing system, associated with a set of candidate parameters. The device may generate, using a recommendation engine, one or more recommendations for the computing system based on the set of energy consumption metrics associated with the set of candidate parameters. The device may transmit information associated with identifying the one or more recommendations.

Abstract: In some examples, temporal impact analysis of cascading events on metaverse-based organization avatar entities may include determining a temporal impact of a metaverse event on a specified organization avatar entity. With respect to the specified organization avatar entity, a similarity of the metaverse event may be determined in a current temporal context to past events. A reaction plan of a plurality of reaction plans may be selected from an event database and based on the determined similarity. Based on an analysis of the temporal impact with respect to the selected reaction plan, instructions may be generated to execute the selected reaction plan by a metaverse operating environment.

Abstract: A remote location monitoring system identifies a place of interest and a subset of artificial satellites that can capture images of the place of interest within a threshold period. The next available artificial satellite with image sensors that can capture images of the place of interest earliest is selected and the cache heating signal is transmitted to be stored in a cache associated with the next available image sensor. The cache heating signal activates particular image sensors for image capture and enables the transmission of meaningful images that enable monitoring the place of interest.

Abstract: In some implementations, a device may receive first energy consumption information relating to a set of hardware components of a computing system. The device may receive second energy consumption information relating to a set of virtual machines associated with the computing system. The device may receive third energy consumption information relating to a set of software elements associated with the computing system. The device may determine an energy consumption of the computing system based on the first energy consumption information, the second energy consumption information, and the third energy consumption information. The device may identify, based on the energy consumption of the computing system, an energy optimization associated with a usage context of the computing system. The device may transmit a set of instructions to alter one or more parameters of the computing system to implement the energy optimization for the computing system.

Abstract: A method and system for training a federated learning model are disclosed. The method may include receiving the global federated learning model from the server via the client. The client may control remote computing resources. The method may further include identifying a spare computing instance from the remote computing resources and determine a threshold training load for training the global federated learning model based on a training load assigned to the client. The method may further include, in response to a processing capacity of the spare computing instance being sufficient to process the threshold training load, offloading the threshold training load to the spare computing instance and training the global federated learning model on the spare computing instance with the training dataset stored in a data source maintained by the client.

Abstract: A retraining monitoring system maintains the sustainability of a production machine learning (ML) model system that includes a production ML model retraining platform. The retraining monitoring system collects contextual data from the production ML model system and determines if one or more of a currently-selected architectural options has to be changed for sustainability. An architectural option of the production ML model retraining platform, such as, a processing location is selected from a cloud retraining platform or an on-premises retraining platform by a selection process based on a multi-armed bandit problem. An evaluation of the retraining architecture is dealt with as a reinforcement learning problem to implement one of a periodic retraining architecture or a reactive retraining architecture.

Abstract: In some examples, energy efficient collaboration for environmental social and governance (ESG) data consolidation and validation may include identifying, based on a global correlation graph, correlated ESG dimensions for each ESG data analyzer of a plurality of ESG data analyzers with respect to a set of ESG dimensions on which an ESG data analyzer of the plurality of ESG data analyzers collects data for at least one organization avatar entity (OAE) of a plurality of OAEs. In this regard, decentralized groups of collaborating ESG data analyzers may be generated based on a collaboration potential between the plurality of ESG data analyzers. For an ESG data analyzer that is collecting data and based on an associated updated data model, a potential anomalous ESG event may be identified at a specific ESG dimension. Further, operation of an OAE associated with the ESG data analyzer that is collecting data may be controlled.

Abstract: In some examples, temporal impact analysis of cascading events on metaverse-based organization avatar entities may include determining a temporal impact of a metaverse event on a specified organization avatar entity. With respect to the specified organization avatar entity, a similarity of the metaverse event may be determined in a current temporal context to past events. A reaction plan of a plurality of reaction plans may be selected from an event database and based on the determined similarity. Based on an analysis of the temporal impact with respect to the selected reaction plan, instructions may be generated to execute the selected reaction plan by a metaverse operating environment.

Abstract: In some examples, continuous learning-based application related trade-off resolution and implementation may include generating, based on a plurality of historical tradeoff instances, an application feature matrix. Further, association rules for historical tradeoff instances for which decisions are not known, and a decision tree for historical tradeoff instances for which decisions are known may be generated. Decision rules may be induced, and default rules may be applied to a cold start scenario. The decision rules and the default rules may be refined to generate refined rules, and a confidence level may be determined for the refined rules. The refined rules may be prioritized based on the confidence level and applied to a new tradeoff instance to generate a resolution associated with the new tradeoff instance. The resolution may be implemented with respect to the new tradeoff instance.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Abstract: A method for detection of a corrupted sensor including providing a first sensor; identifying one or more correlating sensors to the first sensor; determining a correlation between the first sensor and the correlating sensors according to historical sensor values; obtaining a calculated value of the first sensor based on values of the correlating sensors and the correlation; obtaining a measured value of the first sensor; and determining whether the first sensor is corrupted according to a difference between the calculated value and the measured value of the first sensor.

Abstract: In some examples, energy cost reduction of metaverse operations may include generating a unified model of What-IF scenarios. For a semantic association graph of organization avatar entities and for each logically independent IF scenario of a plurality of logically independent IF scenarios of the What-IF scenarios, a sub-metaverse of semantically connected organization avatar entities may be determined. State transitions of the semantically connected organization avatar entities may be iteratively performed until the sub-metaverse reaches a stationarily stable state or an operating limit. A determination may be made as to whether a goal condition is met in the sub-metaverse. For each of the logically independent IF scenarios for which the goal condition is met, an overall energy cost may be determined, and a logically independent IF scenario that includes a minimum energy cost may be identified and used to control an operation for an organization entity.