Abstract: A header matrix prepended to a machine learning model allows the relative importance of a dataset's features to be determined or inferred. The header matrix begins as an Identity matrix. Gradients associated with a backpropagation are stored in the header matrix and accumulated in an accumulation matrix. The relative importance of each feature of the dataset can be determined or inferred from the accumulation matrix.

Abstract: An information handling system for managing a storage system includes storage and a storage manager. The storage is for storing a concurrency effect model for the storage system. The storage manager obtains a request for a latency prediction for the storage system; obtains media access time estimates for the storage system using a trace that specifies: a series of accesses; and responses, by the storage system, to the series of accesses; obtain concurrency estimates for the trace using the media access time estimates; obtains modifications for the media access time estimates using: the concurrency effect model; and the concurrency estimates; updates the media access time estimates using the modifications to obtain a latency prediction for the storage system; and performs an action set, based on the latency prediction, to manage operation of the storage system.

Abstract: A method for managing a storage system includes monitoring the storage system to obtain a set of input/output (I/O) telemetry entries, determine a workload signature based on the set of I/O telemetry entries, obtaining, based on the workload signature, a set of performance metrics, performing a parameter analysis to determine a set of alternative storage system parameterizations, based on the set of alternative storage system parameterization, generating a set of alternative performance metrics, updating a performance index structure based on the performance metrics, and the set of alternative performance metrics to obtain an updated performance index structure, and initiating a storage system update based on the updated performance index structure.

Abstract: A method for managing a storage system includes monitoring the storage system to obtain a set of input/output (I/O) telemetry entries, determine a workload signature based on the set of I/O telemetry entries, obtaining, based on the workload signature, a set of performance metrics, performing a parameter analysis to determine a set of alternative storage system parameterizations, based on the set of alternative storage system parameterization, generating a set of alternative performance metrics, updating a performance index structure based on the performance metrics, and the set of alternative performance metrics to obtain an updated performance index structure, and initiating a storage system update based on the updated performance index structure.

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 serverless application center is provided for multi-cloud deployment of serverless applications. One method comprises providing source code, obtained from a first user acting in a developer role, for a multi-cloud serverless application in a serverless application repository; deploying, in response to a request from a second user acting in an end user role, source code for the multi-cloud serverless application from the serverless application repository to one or more clouds in plurality of distinct cloud environments based on the request; and implementing role-based access for users acting in said developer role and said end user role. A user interface can allow the second user to (i) review a published list of serverless applications available in the serverless application repository; and/or (ii) search a plurality of serverless applications available in the serverless application repository.

Abstract: An information handling system for managing a storage system includes storage and a storage manager. The storage is for storing a concurrency effect model for the storage system. The storage manager obtains a request for a latency prediction for the storage system; obtains media access time estimates for the storage system using a trace that specifies: a series of accesses; and responses, by the storage system, to the series of accesses; obtain concurrency estimates for the trace using the media access time estimates; obtains modifications for the media access time estimates using: the concurrency effect model; and the concurrency estimates; updates the media access time estimates using the modifications to obtain a latency prediction for the storage system; and performs an action set, based on the latency prediction, to manage operation of the storage system.

Abstract: A multi-cloud framework is provided for microservice-based applications. An exemplary method comprises maintaining a structural state of an application comprising a plurality of microservices hosted in a plurality of distinct cloud environments. The structural state of the application is maintained over time and comprises, for each microservice, an indication of the cloud environment that hosts the respective microservice. A source code is maintained for each of the plurality of microservices of the application and deployment instructions are maintained for each of the plurality of distinct cloud environments. The plurality of microservices of the application are deployed using the structural state of the application, the source code for each of the plurality of microservices and the deployment instructions for each of the plurality of distinct cloud environments.

Abstract: Hydratable neural networks are disclosed. A neural network can be trained in an adequate computing environment. The trained neural network is dehydrated into a hydration package. The hydration package is made portable. The hydration package can be extracted or imported into an end device such that the end device includes a personalized neural network.

Abstract: Techniques are provided for a multi-cloud operations center for function-based applications. One method includes obtaining source code for a function of multiple functions of an application, where the function is hosted in a first cloud environment of multiple distinct cloud environments, generating adapted source code from the source code to migrate the function to a second cloud having a different cloud environment; and deploying the function to the second cloud having the different cloud environment using the adapted source code. The source code may be automatically updated for the at least one function for the multiple distinct cloud environments. The function may have a corresponding network address that identifies the function across multiple distinct cloud environments, and network address redirection is performed based on a given cloud environment on which the function is deployed.

Abstract: A serverless application center is provided for multi-cloud deployment of serverless applications. One method comprises providing source code, obtained from a first user acting in a developer role, for a multi-cloud serverless application in a serverless application repository; deploying, in response to a request from a second user acting in an end user role, source code for the multi-cloud serverless application from the serverless application repository to one or more clouds in plurality of distinct cloud environments based on the request; and implementing role-based access for users acting in said developer role and said end user role. A user interface can allow the second user to (i) review a published list of serverless applications available in the serverless application repository; and/or (ii) search a plurality of serverless applications available in the serverless application repository.

Abstract: Techniques are provided for a multi-cloud operations center for function-based applications. One method comprises obtaining source code for a function of multiple functions of an application, where the function is hosted in a first cloud environment of multiple distinct cloud environments, generating adapted source code from the source code to migrate the function to a second cloud having a different cloud environment; and deploying the function to the second cloud having the different cloud environment using the adapted source code. The source code may be automatically updated for the at least one function for the multiple distinct cloud environments. The function may have a corresponding network address that identifies the function across multiple distinct cloud environments, and network address redirection is performed based on a given cloud environment on which the function is deployed.

Abstract: Simulation data is summarized and queried. A user provides an indication of simulation data that will be subsequently queried. The queried simulation data comprises (i) a set of key attributes, (ii) a set of events, and/or (iii) a set of causality relationships between a plurality of the events. First level summaries summarize simulation executions of scenarios of a combinatorial process and comprise (i) a summary of the frequency distribution of key attribute values, (ii) a timestamp for each event, and (iii) an indication of causality between events observed during the simulation. Second level summaries summarize executions of the given scenario and comprise (i) a consolidated distribution probability for the key attributes, (ii) a frequency distribution of occurrences of the events in a single execution, and (iii) a frequency of observations of the causality between pairs of events.

Abstract: A multi-cloud framework is provided for microservice-based applications. An exemplary method comprises maintaining a structural state of an application comprising a plurality of microservices hosted in a plurality of distinct cloud environments. The structural state of the application is maintained over time and comprises, for each microservice, an indication of the cloud environment that hosts the respective microservice. A source code is maintained for each of the plurality of microservices of the application and deployment instructions are maintained for each of the plurality of distinct cloud environments. The plurality of microservices of the application are deployed using the structural state of the application, the source code for each of the plurality of microservices and the deployment instructions for each of the plurality of distinct cloud environments.

Abstract: Scale-out network attached storage (NAS) file systems can employ an Ingest, Transform, Store (ITS) framework for data processing. In one aspect, the ITS-NAS file systems comprise NAS nodes and high performance computing (HPC) nodes that operate under a common operating system and that are coupled to each other via a common high-bandwidth, low-latency private network infrastructure. The NAS nodes can present data to the HPC nodes as well as dispatch the execution of transform services to the HPC nodes. The ITS-NAS file systems enable massive parallelization of operations on files, for example, complex distributed operations on large files and/or simple parallel operations on large collections of small files, all within the same hardware and software architecture.

Abstract: Methods and apparatus are provided for automatic combination of sub-process simulation results and heterogeneous data sources. An exemplary method comprises obtaining, for a process comprised of a sequence of a plurality of sub-processes, an identification of relevant input and output features for each sub-process; obtaining an execution map for each sub-process, wherein each execution map stores results of an execution of a given sub-process; and, in response to a user query regarding a target feature and a user-provided initial scenario: composing a probability distribution function for the target feature representing a simulation of the process based on a sequence of the execution maps, by matching input features of each execution map with features from the initial scenario or the output of previous execution maps; and processing the probability distribution function to answer the user query.

Abstract: A hot folder mechanism is employed to provide a truly integrated architecture for easy-to-use, easy-to-deploy scale-out computation and scale-out storage. A folder of an Ingest, Transform, Store (ITS)-Network attached storage (NAS) system can be configured as “hot.” The configured hot folder can then detect changes on its content, analyze such content, perform transform services on the content, and output the computation results as files on other specified output folders. In one aspect, file system nodes of the ITS-NAS can present the content to high performance computing (HPC) compute nodes of the ITS-NAS as well as to dispatch the execution of transform services to the HPC compute nodes.