Abstract: Embodiments for efficient resource placement in a disaggregated computing environment. Responsive to receiving an alert of a threshold breach of one or more events associated with a suspicious resource of a first resource type while executing a workload, a known good resource is selected from an available resource pool of the first resource type, where the selecting is performed to optimize a usage of the first resource type. The known good resource from the available resource pool of the first resource type is assigned to the workload such that execution of the workload is transferred from being performed by the suspicious resource to the known good resource.

Abstract: Embodiments for component lifecycle optimization in a disaggregated computing environment. A monitoring and machine learning process is performed to learn a respective lifecycle of different resource types as the different resource types are assigned to respective workloads. The monitoring and machine learning process is used to develop a set of learned failure patterns for determining a mitigation action to perform as new faults are encountered within each of the different resource types while executing the respective workloads. The mitigation action is performed to optimize a remaining lifecycle of respective ones of the different resource types according to the set of learned failure patterns.

Abstract: Embodiments for preemptive substitution of resources in a disaggregated computing environment. Failure patterns and mitigation actions are analyzed for specific failures of respective resources within the disaggregated computing environment. Responsive to determining a failure threshold has been reached for a first resource of a first type of the respective resources, a mitigation action is performed according to the analyzed failure patterns. A result of the mitigation action is determined and the result is used to improve the failure pattern analyzation.

Abstract: Embodiments are provided herein for facilitating high link bandwidth utilization in a disaggregated computing system. A plurality of general purpose links are used to connect respective pluralities of computing elements. A traffic pattern between respective ones of a first plurality of computing elements of a first type and respective ones of a second plurality of computing elements of a second type is detected. The first and second pluralities of computing elements are dynamically connected through the respective ones of the plurality of general purpose links according to the detected traffic pattern.

Abstract: Embodiments are provided herein for efficient component communication and resource utilization in a disaggregated computing system. A general purpose link is provided between a plurality of devices in the disaggregated computing system such that the general purpose link is used to connect the plurality of devices. For those of the plurality of devices communicating with one another within a same pool, the connection of the general purpose link is established using a backplane to facilitate the communication; and for those of the plurality of devices communicating with one another within differing pools, the connection of the general purpose link is established through an optical switching device to facilitate the communication.

Abstract: Embodiments for preemptive deep diagnostics of resources in a disaggregated computing environment. A set of new resources of a first type is provided to an available resource pool within the disaggregated computing environment. An estimate for an expected time to failure (ETTF) for each one of the set of new resources is computed, and respective ones of the new resources from the available resource pool are provisioned to respective workloads based on the ETTF.

Abstract: Various embodiments for agile component-level resource provisioning in a disaggregated cloud computing environment, by a processor device, are provided. Respective members of pools of hardware resources within the disaggregated cloud computing environment are allocated to each respective one of a plurality of tenants according to one of a plurality of service level agreement (SLA) classes. Each respective one of the plurality of SLA classes is characterized by a given response time for the allocation of the respective members of the pools of hardware resources corresponding to a requested workload by the tenant.

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: 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 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 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 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 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 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. 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.