Abstract: Embodiments for preemptive deep diagnostics of resources in a disaggregated computing environment. Respective resources from respective pools of resources of different types are assigned to compose a disaggregated server. A workload is executed by the respective resources within the disaggregated server while the respective resources of the disaggregated server are monitored by a monitoring task. Responsive to a first resource of the respective resources generating an alert from the monitoring task, the workload is instantiated to be concurrently performed by the first resource and a second resource of the respective resources while initiating a health check diagnostic operation on the first resource.

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: 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: 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, 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 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: 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: 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 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: 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 are provided herein for efficient component communication and resource utilization in a disaggregated computing system. An application programming interface (API) is used to submit a communications request to a communications manager, the communications request associated with a connection between a first plurality of computing elements of a first type and a second plurality of computing elements of a second type. The connection between ones of the first plurality of computing elements and the second plurality of computing elements is dynamically rewired according to a grouping scheme computed using information contained within the communications request.

Abstract: Embodiments for preemptive deep diagnostics of resources in a disaggregated computing environment. Respective resources from respective pools of resources of different types are assigned to compose a disaggregated server. A workload is executed by the respective resources within the disaggregated server while the respective resources of the disaggregated server are monitored by a monitoring task. Responsive to a first resource of the respective resources generating an alert from the monitoring task, the workload is instantiated to be concurrently performed by the first resource and a second resource of the respective resources while initiating a health check diagnostic operation on the first resource.

Abstract: Embodiments are provided herein for efficient component communication and resource optimization in a disaggregated computing system. A first set of computing elements are used as in line accelerators and a second set of the computing elements are used as block accelerators within the disaggregated computing system. A switching operation is dynamically performed between the first set of computing elements and the second set of computing elements to perform a workload by rewiring one of a plurality of links associated with respective ones of the first set of computing elements and the second set of computing elements.

Abstract: Embodiments for preemptive deep diagnostics of resources in a disaggregated computing environment. Responsive to detecting a threshold breach of a recurrent event associated with a first resource of a first resource type executing a workload, an alert is generated; and responsive to receiving the alert, the execution of the workload on the first resource is ceased. Health check diagnostics are identified and invoked on the first resource based on the alert and a server telemetry. Results of the health check diagnostics are mapped to a set of learned failure patterns; and a potential failure of the first resource is predicted based on the mapping.

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: Embodiments are provided herein for efficient component communication and resource utilization in a disaggregated computing system. An application programming interface (API) is used to submit a communications request to a communications manager, the communications request associated with a connection between a first plurality of computing elements of a first type and a second plurality of computing elements of a second type. The connection between ones of the first plurality of computing elements and the second plurality of computing elements is dynamically rewired according to a grouping scheme computed using information contained within the communications request.

Abstract: Embodiments are provided herein for maximizing resource utilization in a disaggregated computing system. A general purpose link is established between a first sub-plurality of computing elements and a second sub-plurality of computing elements. The first sub-plurality belongs to a first plurality of computing components of a first type and the second sub-plurality belongs to a second plurality of computing components of a second type in the disaggregated computing system. A determination is made that the first and second sub-pluralities of computing elements are assigned to a first tenant having first data communications. The general purpose link is secured to the first tenant while allowing second data communications of a second tenant to pass through the general purpose link concurrent with the first data communications of the first tenant.

Abstract: Embodiments are provided herein for efficient component communication and resource optimization in a disaggregated computing system. A general purpose link is provided between a plurality of devices in the disaggregated computing system. The general purpose link is used to connect the plurality of devices which respectively comprise different types of devices. A first data object is stored within one of the plurality of devices according to a past use or an expected use of the data object by at least a respective one of a set of processors within the disaggregated computing system, and the general purpose link is dynamically rewired according to a location of the first data object and a location of the at least one of the respective set of processors.

Abstract: Embodiments are provided herein for efficient component communication and resource optimization in a disaggregated computing system. A general purpose link is provided to connect a computing element to a plurality of other computing elements of the disaggregated computing system. The general purpose link is dynamically switched between a plurality of different hardware protocols to communicate with the other computing elements, where respective ones of the other computing elements comprise different types of hardware elements.

Abstract: Embodiments are provided herein for dynamic memory-based communication in a disaggregated computing system. A pool of similar computing elements is configured as a large address space, the large address space segmented by an identifier. Data travel distances are optimized depending on a historical or expected use of a data object by using a grouping and amortization algorithm to relocate the data object within the pool of similar computing elements at a particular address within the large address space according to the historical or expected use.