In one embodiment, an apparatus comprises a storage device and a processor. The storage device stores a plurality of images captured by a camera. The processor: accesses visual data associated with an image captured by the camera; determines a tile size parameter for partitioning the visual data into a plurality of tiles; partitions the visual data into the plurality of tiles based on the tile size parameter, wherein the plurality of tiles corresponds to a plurality of regions within the image; compresses the plurality of tiles into a plurality of compressed tiles, wherein each tile is compressed independently; generates a tile-based representation of the image, wherein the tile-based representation comprises an array of the plurality of compressed tiles; and stores the tile-based representation of the image on the storage device.

A workload adaptation service of a provider network may perform smart deployment of industrial an IoT workload across resources of a provider network and a client network. The workload adaptation service may receive, from a user, an indication of one or more constraints for performance of a workload (e.g., daily upload limit, maximum acceptable latency). The service determines a deployment for the workload across client resources and provider resources based at least on the constraints. The service deploys portions of the workload to the client network and other portions to the provider network according to the deployment. The workload adaptation service may also perform dynamic adaptation of the IoT workload by moving portions of the workload from the client network to the provider network or vice-versa, based on workload performance metrics or based on changes to the available resources.

A computer-implemented method and a computer program product for estimating attributes of running workloads on platforms in a system of multiple platforms as a service. A computer receives definitions of respective workloads and respective platforms that are eligible to run a set of the respective workloads. The computer maps the respective workloads and the respective platforms to attributes of running the respective workloads on the respective platforms. The computer estimates the attributes and storing the attributes in a matrix. The computer updates the attribute in the matrix, in response to a triggering event for modifying the matrix.

A computer-implemented method, a computer program product, and a computer system for optimizing workload placements in a system of multiple platforms as a service. A computer first places respective workloads on respective platforms that yield lowest costs for the respective workloads. The computer determines whether mandatory constraints are satisfied. The computer checks best effort constraints, in response to the mandatory constraints being satisfied. The computer determines a set of workloads for which the best effort constraints are not satisfied and determines a set of candidate platforms that yield the lowest costs and enable the best effort constraints to be satisfied. From the set of workloads, the computer selects a workload that has a lowest upgraded cost and updates the workload by setting an upgraded platform index.

A computer-implemented method, a computer program product, and a computer system for placements of workloads in a system of multiple platforms as a service. A computer detects a triggering event for modifying a matrix that pairs respective workloads on respective platforms and includes attributes of running respective workloads on respective platforms. The computer recalculates the attributes in the matrix, in response to the triggering event being detected. The computer determines optimal placements of the respective workloads on the respective platforms, based on information in the matrix. The computer places the respective workloads on the respective platforms, based on the optimal placements.

A system for providing data protection services for data stored by control plane applications hosted by composed information handling systems includes persistent storage and a system control processor manager. The system control processor manager obtains an intent based data protection request; identifies allocable computing resources of information handling systems; obtains a data protection policy based on the allocable computing resources and the intent based data protection request; obtains a data protection architecture based on the allocable computing resources, the data protection policy, and the intent based data protection request; and instantiates the data protection architecture to obtain a composed information handling system of the composed information handling systems to service the intent based data protection request.

Systems and methods allow users to leverage multiple disparate cloud solutions, offered by disparate service providers, in a unified and cohesive manner. A system includes an engine configured to receive performance metrics from two or more disparate cloud services, select target resources among the two or more disparate cloud services to run tasks based on the performance metrics, a multiservice load balancing scheme, and task parameters. Resources can be scaled up or down in the two or more disparate cloud services based on task loads.

A system and a method for processing a message on a processing platform, such as a Kafka processing platform, are provided. The method includes: acquiring a plurality of partitions from the messaging platform; designating a first partition from among the plurality of partitions as a sticky partition; generating a plurality of routing keys that are configured to route messages to the sticky partition; using a first routing key from among the plurality of routing keys to identify a first service subscription; subscribing to a second service using the first routing key; and receiving a message transmitted by the second service.

An electric vehicle computing sharing system (100) is adapted to receive a signal indicating the electric vehicle (110, 120, 130) is connected to a charging station (115, 125, 135). The computing sharing system (100) may be further adapted to receive information about the electric vehicle (110, 120, 130). The computing sharing system (100) may be further adapted to determine a predicted charging duration (535) for the electric vehicle (110, 120, 130). The computing sharing system (100) may be further adapted to identify a task for execution by a computing resource of the electric vehicle (110, 120, 130) based on the predicted charging duration (535). The computing sharing system (100) may be further adapted to transmit the task to the electric vehicle (110, 120, 130). The computing sharing system (100) may be further adapted to receive a result for the task from the electric vehicle (110, 120, 130).

A processor may analyze, using an AI system, an application, where the application includes one or more application modules. The processor may determine, using the AI system, that an application module is critical based on a contextual scenario. The AI system may be trained utilizing data regarding heat generation of hardware on which the application module is operating. The processor may identify, using the AI system, required resources of the hardware for the application module to function during the contextual scenario. The processor may allocate an availability of the required resources for the application module.