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: 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 analytic function is performed on data resident in the pool of the memory devices using memory bandwidth not currently committed to a primary compute task.

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: 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: 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: 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 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 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: 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: 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: For power management in a disaggregated computing system, upon detecting an electrical power reduction to the disaggregated computing system, respective workloads being performed by respective processors within a set of processors are prioritized according to a service level agreement (SLA) of the respective workloads. Backup electrical power is dynamically allocated to the respective processors performing the respective workloads based upon a priority of the SLA of the respective workloads.

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: Server resources in a data center are disaggregated into shared server resource pools. Servers are constructed dynamically, on-demand and based on workload requirements, by allocating from these resource pools. A disaggregated compute system of this type keeps track of resources that are available in the shared server resource pools, and it manages those resources based on that information. Each server entity built is assigned with a unique server ID, and each resource that comprises a component thereof is tagged with the identifier. As a workload is processed by the server entity, its composition may change, e.g. by allocating more resources to the server entity, or by de-allocating resources from the server entity. Workload requests are associated with the unique server ID for the server entity. When a workload request is received at a resource, it matches its unique server ID to that of the request before servicing the request.

Abstract: Various embodiments for performing hardware upgrades in a disaggregated computing environment. A workload is run on a disaggregated computing system while providing a new component to at least one of a plurality of component pools used by the disaggregated computing system. Point-to-point circuit wire level switching is used to switch the disaggregated system from an assigned component residing in a first of the plurality of component pools to the new component residing in a second of the plurality of component pools without interrupting the running workload.

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 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: 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 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 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 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.