Task (computing)

About: Task (computing) is a research topic. Over the lifetime, 9718 publications have been published within this topic receiving 129364 citations.

Portable electronic device for assisting persons with learning disabilities and attention deficit disorders

Abstract: A portable electronic device (10) assists persons with learning disabilities and attention deficit disorders in performing daily living tasks. These tasks can include, e.g., making a bed, applying makeup, brushing teeth, getting dressed, and eating a meal, or hundreds of other tasks. The device is designed to allow users to develop a personal schedule of these tasks and special events. It alerts users at predetermined times to perform scheduled tasks and coaches and motivates the user in completing the tasks through text, audio and animation. The user is given a predetermined period of time to complete the task and rewarded with points if the task is completed on time. The device also records the user's performance of tasks and creates a task log of the user's performance over a given period of time.

Unit of work for preserving data integrity of a data-base by creating in memory a copy of all objects which are to be processed together

Abstract: A Unit of Work object class for an object oriented database management system provides concurrent processing through Unit of Work levels and instances while maintaining the integrity of the data in the database. Each new Unit of Work assigned to a task is an instance of the Unit of Work object class. A Unit of Work manager controls each step such that manipulation of the data occurs to the copies at that particular level for that particular instance. Only after all levels have been completed satisfactorily will a "Commit" occur to the data in the database. If completion is not satisfactory, Rollback of the levels occur, thus preserving data integrity. The Unit of Work manager can also switch control between Unit of Work instances, thus permitting simultaneous performance of tasks.

Load distribution method and system for distributed threaded task operation in network information processing apparatuses with virtual shared memory

Abstract: In a load distribution method, the load in the entire distributed system is uniformly distributed. In a system in which a plurality of information processing apparatuses (nodes) are connected with a network, the degree of distribution of each distributed task is controlled by expanding or compressing the task. Load distribution is performed by expanding a distributed task operating in a heavily loaded node to a lightly loaded node, and compressing the distributed task from the heavily loaded node and transferring threads within the distributed task. Load distribution servers execute the load distribution method.

Evaluation of a flexible task scheduling algorithm for distributed hard real-time systems

Abstract: Most systems which are required to operate under severe real-time constraints assume that all tasks and their characteristics are known a priori. Scheduling of such tasks can be done statistically. Further, scheduling algorithms operating under such conditions are usually limited to multiprocessor configurations. The authors present a scheduling algorithm which works dynamically and on loosely coupled distributed systems for tasks with hard real-time constraints; i.e., the tasks must meet their deadlines. It uses a scheduling component local to every node and a distributed scheduling scheme which is specifically suited to hard real-time constraints and other timing considerations. Periodic tasks, nonperiodic tasks, scheduling overheads, communication overheads due to scheduling and preemption are all accounted for in the algorithm. Simulation studies are used to evaluate the performance of the algorithm.

Workload adaptive cloud computing resource allocation

Abstract: A workload associated with a task is assessed with respect to each of a plurality of computing paradigms offered by a cloud computing environment. Adaptive learning is employed by maintaining a table of Q-values corresponding to the computing paradigms and the workload is distributed according to a ratio of Q-values. The Q-values may be adjusted responsive to a performance metric and/or a value, reward, and/or decay function. The workload is then assigned to available computing paradigms to be performed with improved utilization of resources.