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 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: 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: Direct inter-processor communication is enabled with respect to data in a memory location without having to switch specific circuits through a switching element (e.g., an optical switch). Rather, in this approach a memory pool is augmented to include a dedicated portion that serves as a disaggregated memory common space for communicating processors. The approach obviates the requirement of switching of physical memory modules through the optical switch to enable the processor-to-processor communication. Rather, processors (communicating with another) have an overlapping ability to access the same memory module in the pool; thus, there is no longer a need to change physical optical switch circuits to facilitate the inter-processor communication. The disaggregated memory common space is shared among the processors, which can access the common space for reads and writes, although particular locations in the memory common space for reads and writes are different.

Abstract: MapReduce processing is carried out in a disaggregated compute environment comprising a set of resource pools that comprise a processor pool, and a memory pool. Upon receipt of a MapReduce job, a task scheduler allocates resources from the set of resource pools, the resources including one or more processors drawn from the processor pool, and one or more memory modules drawn from the memory pool. The task scheduler then schedules a set of tasks required by the MapReduce job. At least one particular task in the set is scheduled irrespective of a location of data required for the particular task. In association with a shuffle phase of the MapReduce job, and in connection with the particular task, at least one connection between a processor and at least one memory module is dynamically rewired based on the location of the data required for the particular task, thereby obviating network transfer of that data.

Abstract: Embodiments are provided herein for component composition of a disaggregated computing system. A plurality of general purpose links connecting a computing element to other hardware elements are provided within the disaggregated computing system. Each of the plurality of general purpose links comprise a point-to-point connection to at least one of the other hardware elements such that the plurality of general purpose links conform to a configuration used by the other hardware elements regardless of a type of data being transferred through the plurality of general purpose links.

Abstract: Server resources in a data center are disaggregated into shared server resource pools, which include a pool of secure processors. Advantageously, servers are constructed dynamically, on-demand and based on a tenant's workload requirements, by allocating from these resource pools. According to this disclosure, secure processor modules for new servers are allocated to provide security for data-in-use (and data-at-rest) in a dynamic fashion so that virtual and non-virtual capacity can be adjusted in the disaggregate compute system without any downtime, e.g., based on workload security requirements and data sensitivity characteristics. The approach herein optimizes an overall utilization of an available secure processors resource pool in the disaggregated environment. The resulting disaggregate compute system that is configured according to the approach cryptographically-protects workload data whenever it is outside the CPU chip.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate extraction of entities having defined lengths of text spans are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a configuration component that defines different hyperparameters of multiple artificial intelligence models, and determines target hyperparameters of an artificial intelligence model based on performance of the multiple artificial intelligence models. The computer executable components can further comprise an application component that employs the artificial intelligence model to extract one or more entities from a data source based on the target hyperparameters.

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 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.

Abstract: A computer security protection may be provided by dynamic computer system certification. User usage of a computer system may be monitored. Based on the monitoring a role of the user in the usage of the computer system is determined. A certification required for the role and whether the user has the certification sufficient for the role are determined. Responsive to determining that the user does not have the certification sufficient for the role, a certification process is initiated.

Abstract: For measuring component utilization in a computing system, a server energy utilization reading of a statistical significant number of servers out of a total number of servers located in the datacenter is obtained by measuring, at predetermined intervals, a collective energy consumed by all processing components within each server. The collective energy is measured by virtually probing thereby monitoring an energy consumption of individual ones of all the processing components to each collect an individual energy utilization reading, where the individual energy utilization reading is aggregated over a predetermined time period to collect an energy consumption pattern associated with the server utilization reading.

Abstract: An example operation may include one or more of receiving, by a disaster recovery orchestrator, configuration changes independent from a disaster recovery test schedule, from one or more monitoring agents at each of an information technology system, analyzing incremental configuration changes since a previous disaster recovery test for potential to limit a need for unnecessary disaster recovery retest, determining component level changes at each information technology system that impact disaster recovery test readiness, initiating a partial disaster recovery retest without regard to periodic disaster recovery test schedules, in response to incremental configuration changes that are at the information technology system component-level, and invoking a blockchain service to generate blockchain transactions, the blockchain transactions committing configuration changes, disaster recovery testing actions, and disaster recovery testing results to a shared ledger of a blockchain network.

Abstract: An example operation may include one or more receiving notifications from one or more monitoring agents, each notification comprising a monitoring agent identifier, one or more configuration changes, and a timestamp corresponding to each configuration change, identifying incremental configuration changes that may require a disaster recovery retest, requesting a partial disaster recovery retest comprising the incremental configuration changes, the partial disaster recovery retest providing test coverage for a subset of a full disaster recovery test plan, and providing a request to a blockchain network to store information for the received notifications to a shared ledger of the blockchain network.

Abstract: For power management in a disaggregated computing system, initial electrical power levels are distributed thereby allocating a voltage and a clock speed to each one of a set of processor cores in the disaggregated computing system. The voltage and the clock speed of respective processor cores within the set of processor cores are adjusted according to a workload priority of respective workloads performed by each respective one of the processor cores, wherein the workload priority is assigned based upon a service level agreement (SLA).

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: 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 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 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: For measuring component utilization in a system having a plurality of subsystems, an energy consumption of each of the plurality of subsystems is monitored whether or not each subsystem performs at least a portion of an overall computation. Respective workloads are classified based upon an energy consumption pattern associated with the monitored energy consumption of each of the plurality of subsystems.