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.

Example implementations relate to consensus protocols in a stretched network. According to an example, a distributed system includes continuously monitoring network performance and/or network latency among a cluster of a plurality of nodes in a distributed computer system. Leadership priority for each node is set based at least in part on the monitored network performance or network latency. Each node has a vote weight based at least in part on the leadership priority of the node. Each node's vote is biased by the node's vote weight. The node having a number of biased votes higher than a maximum possible number of votes biased by respective vote weights received by any other node in the cluster is selected as a leader node.

A device implementing adaptive memory performance control by thread group may include a memory and at least one processor. The at least one processor may be configured to execute a group of threads on one or more cores. The at least one processor may be configured to monitor a plurality of metrics corresponding to the group of threads executing on one or more cores. The metrics may include, for example, a core stall ratio and/or a power metric. The at least one processor may be configured to determine, based at least in part on the plurality of metrics, a memory bandwidth constraint with respect to the group of threads executing on the one or more cores. The at least one processor may be configured to, in response to determining the memory bandwidth constraint, increase a memory performance corresponding to the group of threads executing on the one or more cores.

A computer-based system and method for real-time monitoring of computer resource usage, including obtaining, by a monitoring application executed by a processor, from a plurality of applications, each application executed by a processor, a report upon the accessing of at least one accessed resource by at least one accessing user; and generating, by the monitoring application based on the report, a map of resources accessed by the plurality of applications. If a notification that a resource has been compromised is obtained, a list of all applications that have accessed the resource may be generated based on the map.

A method for cloud service includes receiving, by processing circuitry that operates for a cloud service, a request including a first reporting descriptor object including (i) a first variables descriptor object associated with variables, (ii) a first events descriptor object associated with events, and (iii) a reporting interval. The method also includes generating, by the processing circuitry, at a time within the reporting interval, a message including a second reporting descriptor object with (i) a second variables descriptor object having updated values for the variables and (ii) a second events descriptor object associated with a subset of the events. The method also includes sending, by the processing circuitry, the message to a recipient.

Systems, methods, and devices are described coordinating a distributed accelerator. A command that includes instructions for performing a task is received. One or more sub-tasks of the task are determined to generate a set of sub-tasks. For each sub-task of the set of sub-tasks, an accelerator slice of a plurality of accelerator slices of a distributed accelerator is allocated, sub-task instructions for performing the sub-task are determined. Sub-task instructions are transmitted to the allocated accelerator slice for each sub-task. Each allocated accelerator slice is configured to generate a corresponding response indicative of the allocated accelerator slice having completed a respective sub-task. In a further example aspect, corresponding responses are received from each allocated accelerator slice and a coordinated response indicative of the corresponding responses is generated.

Disclosed herein are system, apparatus, article of manufacture, method, and/or computer program product embodiments for monitoring long-standing computing resources. An apparatus may operate by receiving a cloud monitoring notification, where the cloud monitoring notification may indicate an occurrence of a monitored condition. The apparatus may then operate by scanning a cluster computing system for resource having a client assigned resource identifier and a computing resource attribute based on a resource identifier scan parameter and a resource attribute scan parameter. The apparatus may further operate by generating a resource notification request based on the scanning of the cluster computing system and transmitting the resource notification request to a communications system to notify a user that the resource has a computing resource attribute that match the resource attribute scan parameter.

A computer based system and method for multilateral computing resource reallocation and asset transaction migration may include: receiving a resource transaction request; determining a policy for the request; identifying, in a resource monitoring database, resources to service the request and choosing resources matching the policy determined for the request; and documenting the choosing of resources in the monitoring database. Embodiments may further include automatically reallocating occupied resources to alternative transactions and/or migrating currently-running tasks to idle resources, for example according to predefined conditions. Embodiments of the invention may allow performing various dynamic, granular computational resource and/or asset reallocation and/or transaction migration procedures which may involve dynamic composition granular individual resources and/or assets (e.g. of multiple types and/or sizes) into functional resources (to be used by, e.g., various workload execution instances) by a resource reallocation hub, which may further include various dedicated modules and/or engines and/or components.

Examples described relate to determining a future operation failure in a cloud system. In an example, a historical utilization of resources for performing an operation in a cloud system may be determined. A current utilization of resources in the cloud system may be determined. Based on the historical utilization of resources for performing the operation in the cloud system and the current utilization of resources in the cloud system, a determination may be made whether a future performance of the operation in the cloud system is likely to be a failure. In response to a determination that the future performance of the operation in the cloud system is likely to be a failure, an alert may be generated.

Embodiments described herein are generally directed to a controller of a managed container service that facilitates selection among bare metal machines available within a private cloud. According to an example, a request is received by a Container-as-a-Service controller from a CaaS portal to create a cluster based at least in part on resources of a private cloud of a customer of a managed container service. An inventory of bare-metal machines available within the private cloud is received from a Bare-Metal-as-a-Service (BMaaS) provider associated with the private cloud. A particular bare metal machine is identified for the cluster by selecting among the available bare-metal machines based on cluster information associated with the request, the inventory, and a best fit algorithm configured in accordance with a policy established by the customer.