Abstract: Techniques described herein relate to a method for predicting field values of documents. The method may include identifying a field prediction model generation request; obtaining, training documents from a document manager; selecting a first training document; making a first determination that the first training document is a text-based document; performing text-based data extraction to identify first words and first boxes included in the first training document; identifying first keywords and first candidate words included in the first training document based on the first words and the first boxes; and generating a first annotated training document using the first keywords and the first candidate words, wherein the first annotated training document comprises color-based representation masks for the first keywords, the first candidate words, and first general words included in the first training document.

Abstract: Techniques are provided for allocating resources for one or more workloads. One method comprises obtaining a current performance of a workload; determining an adjustment to a current allocation of a resource allocated to the workload by evaluating a representation of a relationship between: (i) the current allocation of the resource allocated to the workload, (ii) a performance metric, and (iii) the current performance of the workload; and initiating an application of the determined adjustment to the current allocation of the resource for the workload. The performance metric may comprise a nominal value of a predefined service metric and the current performance of the workload may comprise a current value of a variable that tracks a given predefined service metric of the workload. An amount (or percentage) of the adjustment permitted for each iteration may be controlled. A sum of allocated resources can be constrained to an amount of available resources.

Abstract: Methods for compression switching that includes distributing a model to client nodes, which use the model to generate a gradient vector (GV) based on a client node data set. The method includes receiving a model update that includes a gradient sign vector (GSV) based on the gradient vector; generating an updated model using the GSV; and distributing the updated model to the client nodes. The client node uses the updated model to generate a second GV based on a second client node data set. The method also includes a determination that a compression switch condition exists; based on the determination, transmitting an instruction to the client node to perform a compression switch; receiving, in response to the instruction, another model update including a subset GSV based on the second gradient vector; generating a second updated model using the subset GSV; and distributing the second updated model to the client nodes.

Abstract: Techniques described herein relate to a method for managing data of data nodes. The method includes obtaining, by a data node manager, a soft labeling request; in response to obtaining the soft labeling request: sending, by the data node manager, requests for processed data to data nodes associated with the data node manager; obtaining, by the data node manager, processed data from the data nodes; merging, by the data node manager, the processed data to obtain processed data; performing, by the data node manager, clustering on the processed data to obtain soft label metadata; associating, by the data node manager, the soft label metadata with live data associated with the data nodes; and performing, by the data node manager, labeling actions using the live data and the soft label metadata.

Abstract: An information handling system for managing detection of objects includes a storage and a processor. The storage is for storing an encoder; a critical class classifier; a general classifier; and a decoder. The processor obtains data that may include one or more of the objects; encodes the data using the encoder to obtain encoded data; obtains a critical class classification for the encoded data using the critical class classifier; obtains a general classification for the encoded data using the general classifier; conditions the encoded data to obtain conditioned encoded data; decodes the conditioned encoded data using the decoder to obtain reconstructed data; makes a determination that the reconstructed data and the critical class classification indicate that the data is an unknown classification; classifies the data as being an unknown classification based on the determination; and performs an action set based on the unknown classification of the data.

Abstract: A global prediction manager for generating predictions using data from data zones includes storage for storing a model repository comprising a global model set and a prediction manager. The prediction manager obtains a local model set from a data zone of the data zones indicating that the global model set is unacceptable; makes a determination that the local model set is acceptable; in response to the determination: distributes the local model set to at least one second data zone of the data zones; obtains compressed telemetry data, that was compressed using the local model set, from the data zone and the at least one second data zone; and generates a global prediction regarding a future operating condition of the data zones using: the compressed local telemetry data and the local model set.

Abstract: Techniques are provided for assigning workloads in a multi-node processing environment using resource allocation feedback from each node. One method comprises obtaining feedback from distributed nodes that process workloads, wherein the feedback for a given node indicates (i) an allocation of resources, and (ii) a number of executing workloads. In response to receiving a given workload to be processed, candidate nodes are identified to execute the given workload; and the given workload is assigned to a given candidate node based on an amount of available resources on each candidate node and/or a stability of resource adjustments made for each candidate node. The stability of the resource adjustments made for each candidate node can be evaluated based on a maximum resource adjustment made for a given candidate node relative to a maximum resource adjustment made for each of the candidate nodes.

Abstract: Techniques described herein relate to a method for managing data of data nodes. The method includes obtaining, by a data node manager, a soft labeling request; in response to obtaining the soft labeling request: sending, by the data node manager, requests for processed data to data nodes associated with the data node manager; obtaining, by the data node manager, processed data from the data nodes; merging, by the data node manager, the processed data to obtain processed data; performing, by the data node manager, clustering on the processed data to obtain soft label metadata; associating, by the data node manager, the soft label metadata with live data associated with the data nodes; and performing, by the data node manager, labeling actions using the live data and the soft label metadata.

Abstract: Techniques are provided for adaptive resource allocation for multiple workloads. One method comprises obtaining a dynamic system model based on a relation between an amount of a resource for multiple iterative workloads and a predefined service metric; obtaining an instantaneous value of the predefined service metric; applying to a given controller associated with a given iterative workload of the multiple iterative workloads: (i) the dynamic system model, (ii) an interference effect of one or more additional iterative workloads on the given iterative workload, and (iii) a difference between the instantaneous value and a target value for the predefined service metric. The given controller applies an adjustment to the amount of the resource for the given iterative workload based at least in part on the difference. The resource allocation for the multiple iterative workloads can be performed in a sequence substantially in parallel with an execution of the iterative workloads.

Abstract: Techniques described herein relate to a method for predicting field values of documents. The method may include identifying a field prediction model generation request; obtaining, training documents from a document manager; selecting a first training document; making a first determination that the first training document is a text-based document; performing text-based data extraction to identify first words and first boxes included in the first training document; identifying first keywords and first candidate words included in the first training document based on the first words and the first boxes; and generating a first annotated training document using the first keywords and the first candidate words, wherein the first annotated training document comprises color-based representation masks for the first keywords, the first candidate words, and first general words included in the first training document.

Abstract: Techniques are provided for allocation of shared computing resources using source code feature extraction and machine learning techniques. An exemplary method comprises obtaining source code for execution in a shared computing environment; extracting a plurality of discriminative features from the source code; obtaining a trained machine learning model; and generating a prediction of an allocation of one or more resources of the shared computing environment needed to satisfy one or more service level agreement requirements for the source code. The generated prediction is optionally adjusted using a statistical analysis of an error curve, based on one or more error boundaries obtained by the trained machine learning model.

Abstract: Techniques described herein relate to a method for resource allocation using fingerprint representations of telemetry data. The method may include receiving, at a resource allocation device, a request to execute a workload; obtaining, by the resource allocation device, telemetry data associated with the workload; identifying, by the resource allocation device, a breakpoint based on the telemetry data; identifying, by the resource allocation device, a workload segment using the breakpoint; generating, by the resource allocation device, a fingerprint representation using the workload segment; performing, by the resource allocation device, a search in a fingerprint catalog using the fingerprint representation to identify a similar fingerprint; obtaining, by the resource allocation device, a resource allocation policy associated with the similar fingerprint; and performing, by the resource allocation device, a resource policy application action based on the resource allocation policy.

Abstract: Techniques described herein relate to a method for updating ML models based on drift detection. The method may include training a ML model; storing the trained ML model associated with a confidence threshold and a fresh indication; receiving a drift signal from an edge node; making a determination, that drift is detected for the ML model; updating the trained ML model in the shared communication layer to be associated with a drifted indication; receiving batch data from edge nodes in response to the updating; generating an updated data set comprising previous data and the batch data; training the ML model using the updated data set; updating the trained ML model in the shared communication layer to be associated with an outdated indication; and storing, by the model coordinator, the updated trained ML model in the shared communication layer associated with a confidence threshold and a fresh indication.

Abstract: A method and system for assessing sequentiality of a data stream is disclosed. Specifically, the method and system disclosed herein may entail receiving an incoming request to access a page in a cache memory, wherein the page is identified by a page address of an address space in a main memory; identifying, in a memory, a bin corresponding to an address range including the page address of the page of the incoming request, wherein the bin includes k address ranges of the address space of the main memory; determining whether to update an occupation count of the bin in the memory; locating the bin in a heuristics table to obtain an estimated total number of expected proximal accesses based on an updated occupation count of the bin; and determining, based on the estimated total number of expected proximal accesses, sequentiality of the data stream to device in order to generate a policy for the cache memory.

Abstract: Techniques are provided for predicting a time to complete a data protection operation. One method comprises obtaining metadata for (i) a given data protection appliance, and/or (ii) a cluster of similar data protection appliances comprising the given data protection appliance; evaluating first level features using the obtained metadata; evaluating a second level feature using some of the evaluated first level features; and processing one or more of the first level features, and the second level feature, using a model that provides a predicted time to complete a data protection operation with respect to data of a protected device associated with the given data protection appliance. The predicted time may comprise a tolerance based on a robustness factor. The predicted time may be based on a number of protected devices that are concurrently undergoing a data protection operation with the protected device for one or more time intervals.

Abstract: Reinforcement learning agents for resource allocation for iterative workloads, such as training Deep Neural Networks, are configured. One method comprises obtaining a specification of an iterative workload comprising multiple states and a set of available actions for each state, and a domain model of the iterative workload relating allocated resources with service metrics; adjusting weights of a reinforcement learning agent by performing iteration steps for each simulated iteration of the iterative workload and using variables from the simulated iteration to refine the reinforcement learning agent; and determining a dynamic resource allocation policy for the iterative workload.

Abstract: One example method includes identifying a group of asset backups to be performed, and each asset backup is associated with a respective asset and has an associated backup time and RPO, selecting an asset backup to run first, and the asset backup that will run first is chosen based on a start deadline of that asset backup relative to respective start deadlines of one or more other asset backups, and the start deadline falls within a time slot, selecting a stream from a group of streams for the selected asset backup, and the selected stream is a stream with a lowest value of first available time slot, and backing up the asset at a backup server by running the selected asset backup, and backup begins at a start time that is a time when the selected stream becomes available, and the asset backup runs on the selected stream.

Abstract: Techniques are provided for allocating shared computing resources using source code feature extraction and cluster-based training of machine learning models. An exemplary method comprises: obtaining a source code corpus with source code segments for execution in a shared computing environment; extracting discriminative features from the source code segments in the source code corpus; obtaining a trained machine learning model, wherein the trained machine learning model is trained using samples of source code segments from clusters derived from clustering the source code corpus based on (i) a term frequency metric, and/or (ii) observed values of execution metrics; and generating, using the trained machine learning model, a prediction of an allocation of resources of the shared computing environment needed to satisfy service level agreement requirements for source code to be executed in the shared computing environment.

Abstract: Reinforcement learning is used to dynamically tune cache policy parameters. The current state of a workload on a cache is provided to a reinforcement learning process. The reinforcement learning process uses the cache workload characterization to select an action to be taken to adjust a value of one of multiple parameterized cache policies used to control operation of a cache. The adjusted value is applied to the cache for an upcoming time interval. At the end of the time interval, a reward associated with the action is determined, which may be computed by comparing the cache hit rate during the interval with a baseline hit rate. The process iterates until the end of an episode, at which point the parameters of the cache control policies are reset. The episode is used to train the reinforcement learning policy so that the reinforcement learning process converges to a trained state.

Abstract: A backup orchestrator for providing backup services to entities includes storage for storing recovery point objectives for the entities and a backup manager. The backup manager selects an optimization periodicity based a number of backups to be generated to meet a portion of the recovery point objectives; makes a determination that at least one of the portion of the recovery point objectives has a maximum allowable unbacked up period of time that is greater than the optimization periodicity; in response to the determination: load balances the number of backups across multiple optimization periods, based on the optimization periodicity, of a balanced backup schedule; selects a backup generation time for each of the to be generated backups in each of the optimization periods of the balanced backup schedule; and generates the number of backups using the balanced backup schedule.