Abstract: A workload orchestrator in a disaggregated computing system manages Infrastructure Processing Units (IPUs) in a bidirectional way to provide redundancy and optimal resource configurations. Light-weight machine learning capabilities are used by the IPUs and the workload orchestrator to profile workloads, specify a redundancy level for each workload phase and predict a configuration that can provide optimal performance and security for the disaggregated computing system.

Abstract: Examples described herein relate to a computing system supporting custom page sized ranges for an application to map contiguous memory regions instead of many smaller sized pages. An application can request a custom range size. An operating system can allocate a contiguous physical memory region to a virtual address range by specifying a custom range sizes that are larger or smaller than the normal general page sizes. Virtual-to-physical address translation can occur using an address range circuitry and translation lookaside buffer in parallel. The address range circuitry can determine if a custom entry is available to use to identify a physical address translation for the virtual address. Physical address translation can be performed by transforming the virtual address in some examples.

Abstract: A computing node can execute a controller in a secure and trusted environment. The controller can cause a task to be executed on different nodes with differing computing platform software and an executable derived from a different coding language. The controller can detect anomalies in results from performance of the task using the different nodes. Any node with an anomalous result can be excluded from use and considered compromised by intrusion. The controller can also at some time interval or a pseudo-random time interval, change computing software settings and/or coding language used for applications on the node.