Abstract: Embodiments for optimizing dynamic resource allocations in a disaggregated computing environment. A data heat map associated with a data access pattern of data elements associated with a workload is constructed. A locality of the data elements associated with the workload within the disaggregated computing environment is determined using the data heat map. Particular resources within pools of resources are assigned to a dynamically constructed disaggregated system in real-time based upon the locality of the data elements in relation to other ones of the resources within the pools of resources such that the dynamically constructed disaggregated system executes the workload using an optimized set of the particular resources.

Abstract: Embodiments for performing rolling software upgrades in a disaggregated computing environment. A rolling upgrade manager is provided for upgrading one or more disaggregated servers. A designated memory area is used for storing an updated software component, and a disaggregated server is switched to the designated memory area from a currently assigned memory area when performing the software upgrade. A process state and program data is maintained in the currently assigned memory area while maintaining the updated software component in the designated memory area such that the process state and program data are read from the currently assigned memory area and the updated software component is read from the designated memory area during currently executing operations of the disaggregated server.

Abstract: A computer-implemented method includes obtaining fault information regarding a fault associated with a first drone. The computer-implemented method additionally includes obtaining context parameter data of the first drone. The computer-implemented method additionally includes, responsive to obtaining the fault information and the context parameter data, determining to apply a first test case of a plurality of test cases based on a first risk value determined for the first test case using the context parameter data. The first test case is associated with the fault. The computer-implemented method additionally includes causing the first drone to initiate execution of the first test case.

Abstract: Identify individual machines of a multi-machine computing system. Construct a graph of dependencies among the machines. Obtain estimated total administration times and administration priorities for each of the machines. Identify availability of administration resources to assist in administration of one or more of the machines. Select a first set of machines for administration in response to the graph, administration priorities, estimated total administration times, and availability of the first set of administration resources, and administer the first set of machines in parallel using the first set of administration resources. Update the graph in response to administration of the first set of machines. Select a subsequent set of machines for administration in response to the updated graph, administration priorities, estimated total administration times, and availability of a subsequent set of administration resources.

Abstract: For power management in a computing system, component utilization is dynamically managed within the computing system according to a calculated aggregate energy consumed by each one of a set of processors. Each of a plurality of energy factors are measured individually between each one of the set of processors to accumulate the calculated aggregate energy in real time.

Abstract: Embodiments for performing rolling software upgrades in a disaggregated computing environment. A rolling upgrade manager is provided for upgrading one or more disaggregated servers. A designated memory area is used for storing an updated software component, and a disaggregated server is switched to the designated memory area from a currently assigned memory area when performing the software upgrade.

Abstract: Respective memory devices are assigned to respective processor devices in a disaggregated computing system, the disaggregated computing system having at least a pool of the memory devices and a pool of the processor devices. An iterative learning algorithm is used to define data boundaries of a dataset for performing an analytic function on the dataset simultaneous to a primary compute task, unrelated to the analytic function, being performed on the dataset in the pool of memory devices using memory bandwidth not currently committed to the primary compute task, thereby efficiently employing the unused memory bandwidth to prevent underutilization of the pool of memory devices.

Abstract: Various embodiments for optimizing memory bandwidth in a disaggregated computing system, by a processor device, are provided. Respective memory devices are assigned to respective processor devices in the disaggregated computing system, the disaggregated computing system having at least a pool of the memory devices and a pool of the processor devices. An iterative learning algorithm is used to define data boundaries of a dataset for performing an analytic function on the dataset using memory bandwidth not currently committed to a primary compute task.

Abstract: Embodiments for disaster recovery in a disaggregated computing system. Memory resources are allocated at a secondary, disaster recovery site for data received from a primary site. The data from the primary site is continuously replicated to the allocated memory resources at the disaster recovery site without requiring any compute resources to be attached to the allocated memory resources. Responsive to determining a disaster recovery failover is in progress, the compute resources are assigned to the allocated memory resources for performing a failover workload, and the failover workload is executed at the disaster recovery site.

Abstract: Embodiments for replicating data in a disaggregated computing system. A memory pool is allocated, where the memory pool includes allocated memory elements at a first site and allocated memory elements at a second site. The allocated memory elements are mapped at the first site to the allocated memory elements at the second site. A replication operation is initiated to mirror data stored within the allocated memory elements at the first site to the allocated memory elements at the second site. The allocated memory elements at the first site are directly connected through an independent networking connection to the allocated memory elements at the second site such that the replication operation is processed exclusively through compute resources at the first site.

Abstract: Embodiments for disaster recovery in a disaggregated computing system. A memory is allocated at a secondary, disaster recovery site for data received from a primary site. A degree of resiliency is defined for respective workloads associated with the data at the primary site to specify how critical each respective workload is to execute in case of disaster, and the data is replicated to the allocated memory at the disaster recovery site according to the degree of resiliency.

Abstract: Embodiments for disaster recovery in a disaggregated computing system. A memory pool is allocated including allocated memory elements at a secondary, disaster recovery site for data received from memory pool elements within the memory pool at a primary site. Data is continuously replicated to the allocated memory elements at the disaster recovery site. During a disaster recovery failover, a determination is made whether there are sufficient resources in the disaggregated computing system for performing workloads of a certain type. If insufficient resources are available, a disaster recovery process is initiated to re-allocate the resources for performing given workloads of the certain type.

Abstract: Embodiments for disaster recovery in a disaggregated computing system. A memory pool is allocated including allocated memory elements at a secondary, disaster recovery site for data received from memory pool elements within the memory pool at a primary site. Data is continuously replicated to the allocated memory elements at the disaster recovery site without requiring any compute resources to be attached to the allocated memory elements during the replicating. An orchestration mechanism is used to regulate an available amount of resources to be assigned to the allocated memory elements at the disaster recovery site during a failover operation for performing failover workloads associated with the replicated data upon the primary site becoming inoperable.

Abstract: Embodiments for optimizing memory placement in a disaggregated computing environment. A new workload is assigned to a subset of a plurality of processors, the subset of processors assigned a subset of a plurality of memory devices. In some embodiments, a determination is made as to whether the new workload is categorized as a memory-dependent workload having a memory need which can be met primarily by the subset of the memory devices. If the new workload is categorized as a memory-dependent workload, a determination is then made as to whether the subset of the memory devices is meeting the memory need of the new workload. When the subset of the memory devices is not meeting the memory need of the new workload, a memory related action is taken.

Abstract: Embodiments for optimizing dynamic resource allocations in a disaggregated computing environment. A data heat map associated with a data access pattern of data elements associated with a workload is constructed. A locality of the data elements associated with the workload within the disaggregated computing environment is determined using the data heat map. Particular resources within pools of resources are assigned to a dynamically constructed disaggregated system in real-time based upon the locality of the data elements in relation to other ones of the resources within the pools of resources such that the dynamically constructed disaggregated system executes the workload using an optimized set of the particular resources.

Abstract: Embodiments for optimizing dynamic resource allocations in a disaggregated computing environment. A new workload is assigned to a subset of a plurality of processors, the subset of processors assigned a subset of a plurality of cache devices. A determination is made that the new workload is categorized as a cache-friendly workload having a memory need which can be met primarily by the subset of cache devices by identifying that underlying data necessitated by the new workload resides primarily within the subset of cache devices. Pursuant to determining the new workload is the cache-friendly workload, a cache related action is performed to increase performance of the new workload executed by the subset of processors and commensurately executes additional workloads performed by other ones of the plurality of processors within the disaggregated computing environment.

Abstract: Embodiments for optimizing dynamic resource allocations in a disaggregated computing environment. Data access patterns of data elements associated with a set of workloads are tracked across predetermined windows of time. The set of workloads is categorized into at least one of a plurality of classes, each class characterized by a method of data access of the data elements during the tracking of the data access patterns. Resource allocations are optimized in the disaggregated computing environment for a current iteration of the set of workloads based on the method of data access by allocating specific resources within the disaggregated data center to the current iteration of the set of workloads according to the data access patterns in real-time.

Abstract: Embodiments for optimizing dynamic resource allocations in a disaggregated computing environment. A new workload is assigned to a subset of a plurality of processors, the subset of processors assigned a subset of a plurality of cache devices. A determination is made that the new workload is categorized as a cache-dependent workload which would be executed more efficiently were additional data elements associated with the new workload to be held in the subset of cache devices, and pursuant to determining the new workload is the cache-dependent workload, a determination is made as to whether the subset of cache devices is meeting the memory need of the new workload. Responsive to determining the subset of cache devices is not meeting the memory need of the new workload, a cache related action is performed.

Abstract: Embodiments for optimizing dynamic resource allocations for storage-dependent workloads in a disaggregated computing environment. A new workload is assigned to a subset of a plurality of processors, the subset of processors assigned a subset of a plurality of memory devices associated with a plurality of storage devices. A determination is made that the new workload is categorized as a storage-dependent workload having a storage need which can be met primarily by a subset of the storage devices after having identified whether data requests associated with the new workload can be satisfied by the subset of memory devices. Pursuant to determining the new workload is the storage-dependent workload, a storage related action is proactively performed to increase efficiency of the new workload prior to commencement of a performance of the new workload.

Abstract: Embodiments for optimizing dynamic resource allocations in a disaggregated computing environment. A data heat map associated with a data access pattern of data elements associated with a workload is maintained. The workload is classified into one of a plurality of classes, each class characterized by the data access pattern associated with the workload. The workload is then assigned to a dynamically constructed disaggregated system optimized with resources according to the one of the plurality of classes the workload is classified into to increase efficiency during a performance of the workload.