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: 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.

Abstract: Access is obtained to a plurality of intermediately transformed electronic documents (with a plurality of sections and subsections) which have been transformed, by topical analysis and text summarization techniques, from a plurality of original electronic documents comprising at least some unstructured electronic documents. Audit and retrieval agent code is appended to the sections and subsections to create a plurality of finally transformed electronic documents. Users are allowed to access the finally transformed electronic documents. The users are provided with accountability reminders contemporaneous with the access. The access of the users to the sections and subsections of the finally transformed electronic documents is logged. An audit report is provided based on the logging. Also provided is a cloud service for enterprise-level sensitive data protection with variable data granularity, using one or more one guest virtual machine images.

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: 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: A new approach to resiliency management is provided in a data center wherein servers are constructed dynamically, on-demand and based on workload requirements and a tenant's resiliency requirements by allocating resources from these pools. In this approach, a set of functionally-equivalent “interchangeable compute units” (ICUs) are composed of resources from resource pools that have been extended to include not only different resource types (CPU, memory, accelerators), but also resources of different specifications (specs) and flavors. As a workload is being processed, the health or status of the resources are monitored. Upon a performance issue or failure event, a resiliency manager can swap out a current ICU and replace it with a functionally-equivalent ICU. Preferably, individual ICUs are hosted on one of: resources of a same type each with different specifications, and resources of a same type and specification and different flavors.

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: Server resources in a data center are disaggregated into shared server resource pools, including an accelerator (e.g., FPGA) pool. Servers are constructed dynamically, on-demand and based on workload requirements, by allocating from these resource pools. According to this disclosure, accelerator utilization in the data center is managed proactively by assigning accelerators 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 accelerators for data analytic workloads. The approach thus provides for a “micro-service” enabling data analytic workloads to automatically and transparently use FPGA 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 FPGAs to use, and then during runtime auto-scales the FPGAs based on workload.

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: A group of processors in a processor pool comprise a secure “enclave” in which user code is executable and user data is readable solely with the enclave. This is facilitated through the key management scheme described that includes two sets of key-pairs, namely: a processor group key-pair, and a separate user key-pair (typically one per-user, although a user may have multiple such key-pairs). The processor group key-pair is associated with all (or some define subset of) the processors in the group. This key-pair is used to securely communicate a user private key among the processors. The user private key, however, is not transmitted to non-members of the group. Further, preferably the user private key is refreshed periodically or upon any membership change (in the group) to ensure that non-members or ex-members cannot decipher the encrypted user key.

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: A method is provided for data matching between a set of source data structures and a set of target data structures. The method includes obtaining, using a processor device configured to perform machine learning, source to target matching results with matching scores, based on the sets of source and target data structures. The method further includes calculating, by the processor device, context information for data structure pairs based on a structure similarity and an ontology similarity between constituent data structures thereof. Each of data structure pairs include as the constituent data structures a respective source data structure and a respective target data structure from the sets of source and target data structures. The method also includes updating, by the processor device, the matching scores based on the context information. The method additionally includes controlling, by the processor device, a hardware device responsive to at least one updated matching score.

Abstract: An actuator to execute on a server may be automatically selected based on risk of failure and damage to the server. Requirement specification and environment parameters may be received. A subset of actuators may be selected based on a risk threshold from an actuator catalog database storing actuator information and actuator risk metadata associated with a plurality of actuators. The actuator risk metadata may be augmented with risk information. A ranked list of the subset of actuators may be generated based on the actuator risk metadata associated with each actuator in the subset. An actuator in the ranked list may be executed on the server.

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: An actuator to execute on a server may be automatically selected based on risk of failure and damage to the server. Requirement specification and environment parameters may be received. A subset of actuators may be selected based on a risk threshold from an actuator catalog database storing actuator information and actuator risk metadata associated with a plurality of actuators. The actuator risk metadata may be augmented with risk information. A ranked list of the subset of actuators may be generated based on the actuator risk metadata associated with each actuator in the subset. An actuator in the ranked list may be executed on the 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: Embodiments generally relate transforming data for a target schema. In some embodiments, a method includes receiving input data, where the input data includes a plurality of segments, and where the segments include a plurality of source fields containing target data. The method further includes characterizing the input data based at least in part on a plurality of predetermined metrics, where the predetermined metrics determine a structure of the input data. The method further includes mapping the target data in the source fields of the segments to a plurality of target fields of a target schema based at least in part on the characterizing. The method further includes populating the target fields of the target schema with the target data from the source fields based at least in part on the mapping.

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.