Abstract: Embodiments of systems, apparatuses and methods provide enhanced function as a service (FaaS) to users, e.g., computer developers and cloud service providers (CSPs). A computing system configured to provide such enhanced FaaS service include one or more controls architectural subsystems, software and orchestration subsystems, network and storage subsystems, and security subsystems. The computing system executes functions in response to events triggered by the users in an execution environment provided by the architectural subsystems, which represent an abstraction of execution management and shield the users from the burden of managing the execution. The software and orchestration subsystems allocate computing resources for the function execution by intelligently spinning up and down containers for function code with decreased instantiation latency and increased execution scalability while maintaining secured execution.

Abstract: Techniques for live adaptive training in a production system are described. In an example, a training application executed on a workstation receives, based at least in part on an identifier of a user of the workstation, a training module and a trigger rule to present the training module. The training application exchanges, with a workstation application executed on the workstation, data about an event determined by the workstation application. The training application also determines a match between the trigger rule and the event. Based at least in part on the match, the training application initiates a presentation of the training module. In a further example, when the training module is presented, a transition rule may be used by the training application, based on an event from the workstation application, to determine a next training segment from the training module to present.

Abstract: Disclosed are various embodiments for determining capacities for work buffers. Data is received that indicates past work cycles for a first stage and a second stage of a pipelined process. The pipelined process includes a work buffer between the first stage and the second stage. Staffing levels for the first stage and the second stage are received. An optimal buffer capacity for the work buffer is generated based at least in part on a predicted workflow variance for the pipelined process, the staffing levels, and the past work cycles.