Abstract: A system and method for dynamically determining parameters of crafting activities for individual users in a virtual space are disclosed. The crafting activity parameters may be determined based on rate of usage of the virtual space by the users. In some examples, a user usage rate of the virtual space may be determined to measure an amount of time a user spend in the virtual space during a period of real-world time. Based on the determined user usage rate of the virtual space, crafting activity parameters may be determined, for example, to balance differences in time spent in the virtual space by the users. In some implementations, for such determinations of the crafting activity parameters, one or more user normalization standards may be obtained.

Abstract: Systems herein include thin clients that operate with managed profile-based virtual machines. This can allow users to utilize personal user devices in an enterprise environment without subjecting sensitive enterprise credentials to the user device. A management server can determine a profile associated with the user device. Based on the profile, a virtual machine can be instantiated at a thin server, remotely from the thin client. The profile-specific virtual machine can include a particular guest operating system, guest applications, security features, or functionality. The instance of the virtual machine can communicate graphics information from a guest application to the thin client, and the thin client can communicate user interface events to the instance for controlling the guest application.

Abstract: Method and system for deallocating shared system resources. In an example, the system includes a memory storing a system resource allocation database, a processor running a scheduler, including an I/O and a scheduler engine. The scheduler receives a request to deallocate a first plurality of system resources, which includes a first and a second system resource. The scheduler then updates the system resource allocation database at a starting time by marking the first and the second system resource both as conditionally available including inserting a first time-to-live timestamp and a second time-to-live timestamp associated with the first and the second system resource respectively.

Abstract: A task worker running on a worker server receives a process specification over a network. The process specification specifies a task to be executed by the task worker. The executed task includes generating an output data object for an output data stream based in part on an input data object from an input data stream. The process specification is accessed to specify the required fields to be read from for executing the task and to specify the generated the fields in the input data object that will be written to during or subsequent to the executing of the task. The task worker executes the task and generates the output data object. The output data object is then transmitted to the output stream based on the stream configuration.

Abstract: Systems and methods are described for transforming a data set within a data source into a series of task calls to an on-demand code execution environment or other distributed code execution environment. Such environments utilize pre-initialized virtual machine instances to enable execution of user-specified code in a rapid manner, without delays typically caused by initialization of the virtual machine instances, and are often used to process data in near-real time, as it is created. However, limitations in computing resources may inhibit a user from utilizing an on-demand code execution environment to simultaneously process a large, existing data set. The present application provides a task generation system that can iteratively retrieve data items from an existing data set and generate corresponding task calls to the on-demand computing environment, while ensuring that at least one task call for each data item within the existing data set is made.

Abstract: A workflow is designated for execution across a plurality of autonomous computational entities automatically. Among other things, the cost of computation is balanced with the cost of communication among computational entities to reduce total execution time of a workflow. In other words, a balance is struck between grouping tasks for execution on a single computational entity and segmenting tasks for execution across multiple computational entities.