Abstract: A power management module can select one of a plurality of different operational modes for a hardware component in a computer system based on application performance and total computer system power consumption determined for each of the operational modes.

Abstract: A system management command is stored in a management partition of a global memory by a first node of a multi-node computing system. The global memory is shared by each node of the multi-node computing system. In response to an indication to access the management partition, the system management command is accessed from the management partition by a second node of the multi-node computing system. The system management command is executed by the second node. Executing the system management command includes managing the second node.

Abstract: A bit vector for a Bloom filter is determined by performing one or more hash function operations on a set of ternary content addressable memory (TCAM) words. A TCAM array is partitioned into a first portion to store the bit vector for the Bloom filter and a second portion to store the set of TCAM words. The TCAM array can be searched using a search word by performing the one or more hash function operations on the search word to generate a hashed search word and determining whether bits at specified positions of the hashed search word match bits at corresponding positions of the bit vector stored in the first portion of the TCAM array before searching the second portion of the TCAM array with the search word.

Abstract: A system comprises a plurality of functional units powered via a power source. The system further comprises a first functional unit and a second functional unit, wherein the second functional unit is to promote the first functional unit to a management unit based on a management requirement of the system. The management unit is to administrate operations of the system. Once the first functional unit is promoted, the management unit is isolated from the functional units that were not promoted via a virtual network path and a power management unit.

Abstract: A collection of job slots correspond to placement of observed jobs associated with a plurality of job categories in a data processing environment. An incoming job is received, and based on a job category of the incoming job, the incoming job is assigned to a particular one of the job slots to perform placement of the incoming job on physical resources.

Abstract: A measure of performance may be calculated for a composite cloud service of a particular cloud in a federated cloud environment. A determination may be made as to whether the measure of performance indicates breaching of a performance policy associated with the composite cloud service.

Abstract: Elastic insight to Information Technology (“IT”) performance data is provided. Local performance data is continuously pushed from a front-end component to a back-end component. Global performance data is continuously pushed from the back-end component to the front-end component. The local performance data and the global performance data are aggregated at the front-end component by product, product family, and product solution. The aggregated data is monitored at the front-end component to identify a performance bottleneck.

Abstract: Methods, systems, and machine-readable and executable instructions are provided for balancing the allocation of a number of virtual machines (VMs) in clouds systems. Balancing the allocation of VMs in cloud systems can include determining a number of relationships of a plurality of nodes in a cloud system. Balancing the allocation of VMs in cloud systems can include assigning a number of VMs to the plurality of nodes in response to a request based on a criteria of the request and the number of relationships of the plurality of nodes to balance VMs in the cloud system, where the criteria include a type and a priority for each of the number of VMs.

Abstract: Example embodiments relate to autonomous metric tracking and adjustment. In some examples, a computing node may include a processor to run a main operating system and an application that runs on top of the main operating system. The computing node may include a hardware-level controller that dynamically adjusts individual hardware components of the computing node via control signals that do not pass through the main operating system. The adjustments may be based on a target metric from a scheduling service external to the computing node and individual performance metrics from the computing node.

Abstract: The mapping of High Performance Computing (“HPC”) applications to platforms is provided. An HPC application characterization module determines an HPC application signature to characterize the HPC application. An HPC application mapping module selects a platform from a plurality of platforms to execute the HPC application based on the HPC application signature and a set of benchmarks. An HPC application monitoring module monitors the execution of the HPC application on the selected platform.

Abstract: A technique includes providing a candidate configuration for virtual machines specifying where the virtual machines are stored in a plurality of hardware containers. The candidate configuration is selectively modified to generate another candidate configuration specifying where the virtual machines are stored in the plurality of hardware containers based at least in part on communication capacities that are associated with the hardware containers. The placement of the virtual machines is determined based at least in part on the selective modification.

Abstract: Assigning nodes to jobs based on reliability factors includes calculating the maximum value of a processor utilization efficiency and assigning an optimal number of spare nodes to the job based on the value of the processor utilization efficiency.

Abstract: Examples of the present disclosure include methods, devices, and/or systems. Identifying network communication patterns can include analyzing a distributed computer program of a network, estimating virtual network communication traffic based on the analysis, and mapping the virtual network communication traffic to a physical network link. Identifying network communications patterns can also include identifying the network communication pattern and categorizing the physical communication network link based on an estimated communication intensity of the mapped communication traffic and the network communication pattern. Identifying network communication patterns can further include optimizing an energy used by the network based on the categorization.

Abstract: Example embodiments relate to autonomous metric tracking and adjustment. In some examples, a computing node may include a processor to run a main operating system and an application that runs on top of the main operating system. The computing node may include a hardware-level controller that dynamically adjusts individual hardware components of the computing node via control signals that do not pass through the main operating system. The adjustments may be based on a target metric from a scheduling service external to the computing node and individual performance metrics from the computing node.

Abstract: Assessing the impact of an incident in a Service Level Agreement (SLA) by a system including a plurality of nodes organized in a hierarchical structure is disclosed. An incident record for an incident related to a service at a first node is received and an actual impact of the incident at the first node is calculated. The calculated actual impact is transferred to a parent node until a root node is reached. The actual impact of the incident is calculated at the parent node and a final actual impact and a total financial impact for the SLA are calculated at the root node. The actual impact at each node, the final actual impact, and the total financial impact are calculated dynamically while the incident is in progress.

Abstract: Quantifying power usage for a service. An example method may include identifying a dependency model for the service, the dependency model based in part on infrastructure providing the service. The method may also include determining power usage for the service using the dependency model.

Abstract: A technique includes using a computer agent to observe diagnoses of computer-related incidents. Based on the observation, patterns are identified in the diagnoses, and based at least in part on the patterns, the diagnoses are selectively automated.

Abstract: A power management module can select one of a plurality of different operational modes for a hardware component in a computer system based on application performance and total computer system power consumption determined for each of the operational modes.

Abstract: Methods, systems, and machine-readable and executable instructions are provided for balancing the allocation of a number of virtual machines (VMs) in clouds systems. Balancing the allocation of VMs in cloud systems can include determining a number of relationships of a plurality of nodes in a cloud system. Balancing the allocation of VMs in cloud systems can include assigning a number of VMs to the plurality of nodes in response to a request based on a criteria of the request and the number of relationships of the plurality of nodes to balance VMs in the cloud system, where the criteria include a type and a priority for each of the number of VMs.

Abstract: Systems, methods, and machine-readable and executable instructions are provided for dynamically balancing execution resources to meet a budget and/or a QoS of projects. An example method can include analyzing a submitted program for a project, where the program comprises data to execute the project and a specification for the project, determining a computing resource allocation based upon the submitted data and the specification, and deploying for execution the submitted data to the determined computing resource allocation. The method can include monitoring progress during the execution of the data to determine a probability of project completion satisfying the specification, and dynamically balancing the execution resources to meet the budget and/or the QoS of the project to satisfy the specification.