Requests for computing resources and other resources can be predicted and managed. For example, a system can determine a baseline prediction indicating a number of requests for an object over a future time-period. The system can then execute a first model to generate a first set of values based on seasonality in the baseline prediction, a second model to generate a second set of values based on short-term trends in the baseline prediction, and a third model to generate a third set of values based on the baseline prediction. The system can select a most accurate model from among the three models and generate an output prediction by applying the set of values output by the most accurate model to the baseline prediction. Based on the output prediction, the system can cause an adjustment to be made to a provisioning process for the object.

Transaction authorization systems may include a transaction processor and an authorization server system. The transaction processor obtains transaction requests authorizations for those requests from the authorization server system. The transaction processor may require an authorization be provided within a threshold time; otherwise, the transaction may be processed without authorization. The authorization server system may be hosted using one or more nodes in a distributed system. Degradation of the performance of the distributed system may cause the performance of the authorization server system to fall below the required performance threshold and transactions may not be authorized before automatic processing. Transaction authorization systems may monitor the health of the individual nodes and/or the distributed system and automatically adjust the routing of authorizations based on current and/or future performance degradation. The transaction authorization system may also allocate additional resources and/or reroute authorizations to a separate distributed system to avoid performance degradations.

An information processing apparatus includes: a memory; and a processor coupled to the memory and configured to: divide a job in units of computing nodes for a plurality of computing nodes; determine execution of scale-out or scale-in on the basis of a load in a case where each of the computing nodes is caused to execute a job obtained by the division; execute, in a case where determining execution of the scale-out, the scale-out according to the division of the job in units of computing nodes; and execute, in a case where determining execution of the scale-in, the scale-in according to the division of the job in units of computing nodes.

In an approach for generating differentiated workload telemetry data, a processor corresponds one or more services with a workload related telemetry generating an event emitter. A processor performs a correlation analysis of corresponding relationship and connection among connected resources and current traffic into and out of the one or more services. A processor labels domain context for each telemetry event. A processor communicates each telemetry event to a global event handler. A processor performs a cross-correlation in real-time of telemetry data with the global event handler. A processor updates a real-time differentiated workload report.

An apparatus comprises at least one processing device that includes a processor coupled to a memory. The processing device is configured to identify a plurality of resource objects associated with a processing device, to group correlated resource objects according to processing device utilization of the resource objects, to assign a first weight to a first resource object grouping, wherein the first weight is associated with a performance impact of the first resource object grouping on the processing device, and to release at least some of the first resource object grouping to provide additional resources to a second resource object grouping, the additional resources resulting from the releasing, wherein the first object grouping is selected for the releasing based on a comparison between the first weight and a second weight associated with the second resource object grouping, wherein the releasing is performed to improve performance of the processing device.

Various embodiments comprise systems and methods to sample data outputs of a data pipeline. In some examples, data monitoring circuitry monitors the data pipeline wherein the data pipeline receives an input data set, processes the input data set, and responsively generates and transfers an output data set. The data monitoring circuitry ingests the output data set, determines an amount of available computing resources, and selects an amount of the values from the output data set based on the amount of available computing resources. The data monitoring circuitry generates a quality score for the selected values based on a data quality, generates a confidence score based on the amount of the selected values, and reports the quality score and the confidence score.

A method and system elects a master node from a plurality of nodes in a distributed system. A serverless elector function periodically outputs an election API call to a load balancer. The load balancer elects a master node from a plurality of candidate nodes each time the load balancer receives the election API call.

A transfer method is performed by an information processing apparatus. The method includes: selecting, based on a load status of the information processing apparatus, candidate transfer data that is among the received data and to be transferred to one or more other information processing apparatuses; selecting, based on load statuses of multiple other information processing apparatuses, one or more candidate transfer destination apparatuses among the multiple other information processing apparatuses as candidate transfer destinations of the data; determining, based on throughput between the information processing apparatus and the candidate transfer destination apparatuses, data to be transferred among the candidate transfer data, transfer destination apparatuses of the data to be transferred among the candidate transfer destination apparatuses, and the sizes of data groups including the data to be transferred; and transferring, to the transfer destination apparatuses determined for the determined data groups, the determined data to be transferred.

Techniques discussed herein relate to providing composable edge devices. In some embodiments, a user request specifying a set of services to be executed at a cloud-computing edge device may be received by a computing device operated by a cloud computing provider. A manifest may be generated in accordance with the user request. The manifest may specify a configuration for the cloud-computing edge device. Another request can be received specifying the same or a different set of services to be executed at another edge device. Another manifest which specifies the configuration for that edge device may be generated and subsequently used to provision the request set of services on that device. In this manner, manifests can be used to compose the platform to be utilized at any given edge device.

One embodiment comprises a stateless container of binaries and a broker. The stateless container of binaries includes a code memory having stored thereon code for a first version of a first functional component of a content management system, the first functional component executable to provide a first version of a service. The broker may be executable to: receive a request for the service from a client application, the request associated with a user of the content management system; determine that the first version of the service is accessible with regard to the user; determine an available first server that hosts the first version of the service; provide an indication of the first version of the service to the client application; and provide an IP address and a port number associated with the available first server to the client application.