Abstract: An embodiment includes creating an action item record corresponding to an action item of an action plan record that is responsive to a service request. The action item record comprises a service requirement of the action item. The embodiment executes a querying process that searches vendor records for candidate vendors associated with the service requirement and returns a set of candidate vendors. The embodiment updates the action item record with the set of candidate vendors and determines an optimal vendor team based at least in part on reputation data and cost data associated with each of the candidate vendors. The embodiment updates the action plan record to include the optimal vendor team, which triggers creation of a vendor team dispatch request.

Abstract: An example operation may include one or more of storing sensor data on a blockchain, the sensor data sensed of an object which is cooperatively managed by a plurality of providers, detecting an event with respect to the object, determining, via chaincode deployed on a blockchain peer, one or more providers from among the plurality of providers that are liable for the event based on the sensor data stored on the blockchain, and outputting a notification to a computer system of the blockchain identifying the determined one or more providers.

Abstract: Server resources in a data center are disaggregated into shared server resource pools. Servers are constructed dynamically, on-demand and based on workload requirements, by allocating from these resource pools. A disaggregated compute system of this type keeps track of resources that are available in the shared server resource pools, and it manages those resources based on that information. Each server entity built is assigned with a unique server ID, and each resource that comprises a component thereof is tagged with the identifier. As a workload is processed by the server entity, its composition may change, e.g. by allocating more resources to the server entity, or by de-allocating resources from the server entity. Workload requests are associated with the unique server ID for the server entity. When a workload request is received at a resource, it matches its unique server ID to that of the request before servicing the request.

Abstract: An example operation may include one or more of storing sensor data on a blockchain, the sensor data sensed of an object which is cooperatively managed by a plurality of providers, detecting an event with respect to the object, determining, via chaincode deployed on a blockchain peer, one or more providers from among the plurality of providers that are liable for the event based on the sensor data stored on the blockchain, and outputting a notification to a computer system of the blockchain identifying the determined one or more providers.

Abstract: Server resources in a data center are disaggregated into shared server resource pools. Servers are constructed dynamically, on-demand and based on a tenant's workload requirements, by allocating from these resource pools. The system also includes a license manager that operates to manage a pool of licenses that are available to be associated with resources drawn from the server resource pools. Upon provisioning of a server entity composed of resources drawn from the server resource pools, the license manager determines a license configuration suitable for the server entity. In response to receipt of information indicating a change in a composition of the server entity (e.g., as a workload is processed), the license manager determines whether an adjustment to the license configuration is required. If so, an adjusted license configuration for the server entity is determined and tracked to the tenant. The data center thus allocates appropriate licenses to server entities as required.

Abstract: A method, system and computer program product are disclosed for recovery in a virtualized environment using remote direct memory access (RDMA). In one embodiment, the method comprises operating a virtual computer system on a physical computer system, and the virtual system maintains in a memory area a record of a state of the virtual system. In this method, when defined error conditions occur on the virtual system, RDMA is used to pull the record of the state of the virtual system from that memory area onto a standby computer. This record on the standby computer is used to re-initialize the virtual computer. Embodiments of the invention provide methods that provide a very fast recovery from a virtual machine fault or error, while requiring much fewer resources than standard approaches. In embodiments of the invention, one spare real computer system can be used for backing up several virtual systems.

Abstract: A memory management service occupies a configurable portion of an overall memory system in a disaggregate compute environment. The service provides optimized data organization capabilities over the pool of real memory accessible to the system. The service enables various types of data stores to be implemented in hardware, including at a data structure level. Storage capacity conservation is enabled through the creation and management of high-performance, re-usable data structure implementations across the memory pool, and then using analytics (e.g., multi-tenant similarity and duplicate detection) to determine when data organizations should be used. The service also may re-align memory to different data structures that may be more efficient given data usage and distribution patterns. The service also advantageously manages automated backups efficiently.

Abstract: Server resources in a data center are disaggregated into shared server resource pools. Servers are constructed dynamically, on-demand and based on workload requirements and a tenant's resiliency requirements (e.g., as specified in an SLA), by allocating from these resource pools. A disaggregated compute system of this type keeps track of resources that are available in the shared server resource pools, and it manages those resources based on that information and the health of the resources. As a workload is processed by the server entity and component resources fail, the server entity composition is changed, e.g. by allocating other resources to the server entity, or by transitioning to other server entities, to ensure that a resiliency requirement is maintained.

Abstract: A method, system and computer program product are disclosed for recovery in a virtualized environment using remote direct memory access (RDMA). In one embodiment, the method comprises operating a virtual computer system on a physical computer system, and the virtual system maintains in a memory area a record of a state of the virtual system. In this method, when defined error conditions occur on the virtual system, RDMA is used to pull the record of the state of the virtual system from that memory area onto a standby computer. This record on the standby computer is used to re-initialize the virtual computer. Embodiments of the invention provide methods that provide a very fast recovery from a virtual machine fault or error, while requiring much fewer resources than standard approaches. In embodiments of the invention, one spare real computer system can be used for backing up several virtual systems.

Abstract: A method, system and computer program product are disclosed for recovery in a virtualized environment using remote direct memory access (RDMA). In one embodiment, the method comprises operating a virtual computer system on a physical computer system, and the virtual system maintains in a memory area a record of a state of the virtual system. In this method, when defined error conditions occur on the virtual system, RDMA is used to pull the record of the state of the virtual system from that memory area onto a standby computer. This record on the standby computer is used to re-initialize the virtual computer. Embodiments of the invention provide methods that provide a very fast recovery from a virtual machine fault or error, while requiring much fewer resources than standard approaches. In embodiments of the invention, one spare real computer system can be used for backing up several virtual systems.

Abstract: A memory management service occupies a configurable portion of an overall memory system in a disaggregate compute environment. The service provides optimized data organization capabilities over the pool of real memory accessible to the system. The service enables various types of data stores to be implemented in hardware, including at a data structure level. Storage capacity conservation is enabled through the creation and management of high-performance, re-usable data structure implementations across the memory pool, and then using analytics (e.g., multi-tenant similarity and duplicate detection) to determine when data organizations should be used. The service also may re-align memory to different data structures that may be more efficient given data usage and distribution patterns. The service also advantageously manages automated backups efficiently.

Abstract: Server resources in a data center are disaggregated into shared server resource pools, including a graphics processing unit (GPU) pool. Servers are constructed dynamically, on-demand and based on workload requirements, by allocating from these resource pools. According to this disclosure, GPU utilization in the data center is managed proactively by assigning GPUs to workloads in a fine granularity and agile way, and de-provisioning them when no longer needed. In this manner, the approach is especially advantageous to automatically provision GPUs for data analytic workloads. The approach thus provides for a “micro-service” enabling data analytic workloads to automatically and transparently use GPU resources without providing (e.g., to the data center customer) the underlying provisioning details. Preferably, the approach dynamically determines the number and the type of GPUs to use, and then during runtime auto-scales the GPUs based on workload.

Abstract: Server resources in a data center are disaggregated into shared server resource pools. Servers are constructed dynamically, on-demand and based on a tenant's workload requirements, by allocating from these resource pools. The system also includes a license manager that operates to manage a pool of licenses that are available to be associated with resources drawn from the server resource pools. Upon provisioning of a server entity composed of resources drawn from the server resource pools, the license manager determines a license configuration suitable for the server entity. In response to receipt of information indicating a change in a composition of the server entity (e.g., as a workload is processed), the license manager determines whether an adjustment to the license configuration is required. If so, an adjusted license configuration for the server entity is determined and tracked to the tenant. The data center thus allocates appropriate licenses to server entities as required.

Abstract: A memory management service occupies a configurable portion of an overall memory system in a disaggregate compute environment. The service provides optimized data organization capabilities over the pool of real memory accessible to the system. The service enables various types of data stores to be implemented in hardware, including at a data structure level. Storage capacity conservation is enabled through the creation and management of high-performance, re-usable data structure implementations across the memory pool, and then using analytics (e.g., multi-tenant similarity and duplicate detection) to determine when data organizations should be used. The service also may re-align memory to different data structures that may be more efficient given data usage and distribution patterns. The service also advantageously manages automated backups efficiently.

Abstract: Server resources in a data center are disaggregated into shared server resource pools. Servers are constructed dynamically, on-demand and based on workload requirements and a tenant's resiliency requirements (e.g., as specified in an SLA), by allocating from these resource pools. A disaggregated compute system of this type keeps track of resources that are available in the shared server resource pools, and it manages those resources based on that information and the health of the resources. As a workload is processed by the server entity and component resources fail, the server entity composition is changed, e.g. by allocating other resources to the server entity, or by transitioning to other server entities, to ensure that a resiliency requirement is maintained.

Abstract: Server resources in a data center are disaggregated into shared server resource pools. Servers are constructed dynamically, on-demand and based on workload requirements, by allocating from these resource pools. A disaggregated compute system of this type keeps track of resources that are available in the shared server resource pools, and it manages those resources based on that information. Each server entity built is assigned with a unique server ID, and each resource that comprises a component thereof is tagged with the identifier. As a workload is processed by the server entity, its composition may change, e.g. by allocating more resources to the server entity, or by de-allocating resources from the server entity. Workload requests are associated with the unique server ID for the server entity. When a workload request is received at a resource, it matches its unique server ID to that of the request before servicing the request.

Abstract: Server resources in a data center are disaggregated into shared server resource pools, including a graphics processing unit (GPU) pool. Servers are constructed dynamically, on-demand and based on workload requirements, by allocating from these resource pools. According to this disclosure, GPU utilization in the data center is managed proactively by assigning GPUs to workloads in a fine granularity and agile way, and de-provisioning them when no longer needed. In this manner, the approach is especially advantageous to automatically provision GPUs for data analytic workloads. The approach thus provides for a “micro-service” enabling data analytic workloads to automatically and transparently use GPU resources without providing (e.g., to the data center customer) the underlying provisioning details. Preferably, the approach dynamically determines the number and the type of GPUs to use, and then during runtime auto-scales the GPUs based on workload.

Abstract: A memory management service occupies a configurable portion of an overall memory system in a disaggregate compute environment. The service provides optimized data organization capabilities over the pool of real memory accessible to the system. The service enables various types of data stores to be implemented in hardware, including at a data structure level. Storage capacity conservation is enabled through the creation and management of high-performance, re-usable data structure implementations across the memory pool, and then using analytics (e.g., multi-tenant similarity and duplicate detection) to determine when data organizations should be used. The service also may re-align memory to different data structures that may be more efficient given data usage and distribution patterns. The service also advantageously manages automated backups efficiently.

Abstract: A method, system and computer program product are disclosed for recovery in a virtualized environment using remote direct memory access (RDMA). In one embodiment, the method comprises operating a virtual computer system on a physical computer system, and the virtual system maintains in a memory area a record of a state of the virtual system. In this method, when defined error conditions occur on the virtual system, RDMA is used to pull the record of the state of the virtual system from that memory area onto a standby computer. This record on the standby computer is used to re-initialize the virtual computer. Embodiments of the invention provide methods that provide a very fast recovery from a virtual machine fault or error, while requiring much fewer resources than standard approaches. In embodiments of the invention, one spare real computer system can be used for backing up several virtual systems.

Abstract: A method for configuring a large hybrid memory subsystem having a large cache size in a computing system where one or more performance metrics of the computing system are expressed as an explicit function of configuration parameters of the memory subsystem and workload parameters of the memory subsystem. The computing system hosts applications that utilize the memory subsystem, and the performance metrics cover the use of the memory subsystem by the applications. A performance goal containing values for the performance metric is identified for the computing system. These values for the performance metrics are used in the explicit function of performance metrics, configuration parameters and workload parameters to calculate values for the configuration parameters that achieve the identified performance goal. The calculated values of the configuration parameters are implemented in the memory subsystem.