Task (computing)

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

System and method of automatically determining and displaying tasks to healthcare providers in a care-giving setting

Abstract: A method of determining and displaying a plurality of tasks to a user in a healthcare environment is provide that includes receiving an order associated with a patient, determining a type of the order, and identifying a task template associated with the order. The method further includes determining if an overriding template exists for the order, linking one of the task template or the overriding template to the order, and associating one or more task records with the linked template. The method also includes determining a plurality of tasks corresponding to the task records, adding the plurality of tasks to a work list, filtering the plurality of tasks on the work list for a patient list based on an attribute of the user and displaying the filtered list to the user.

MEDUSA: A Predictable and High-Performance DRAM Controller for Multicore Based Embedded Systems

Abstract: Commercial-Off-The-Shelf (COTS) DRAM controllers are optimized for high memory throughput, but they do not provide predictable timing among memory requests from different cores in multicore systems. Therefore, memory requests from a critical real-time task on one core can be substantially delayed by memory requests from on-real-time tasks on the other cores. In this work, we propose a DRAM controller design, called MEDUSA, to provide predictable memory performance in multicore based real-time systems. MEDUSA can provide high time predictability when needed for real-time tasks but also strive to provide high average performance for non-real-time tasks through a close collaboration between the OSand the DRAM controller. In our approach, the OS partially partitions DRAM banks into two groups: reserved banks and shared banks. The reserved banks are exclusive to each core to provide predictable timing while the shared banks are shared by all cores to efficiently utilize the resources. MEDUSA has two separate queues for read and write requests, and it prioritizes reads over writes. In processing read requests, MEDUSA employs a two-level scheduling algorithm that prioritizes the memory requests to the reserved banks in a Round Robin fashion to provide strong timing predictability. In processing write requests, MEDUSA largely relies on the FR-FCFS for high throughput but makes an immediate switch to read upon arrival of read requests to the reserved banks. We implemented MEDUSA in a Gem5 full-system simulator and a Linux kernel and performed experiments using a set of synthetic and SPEC2006 benchmarks to analyze the performance impact of MEDUSA on both real-time and non-real-time tasks. The results show that MEDUSA achieves up to 95% better worst-case performance for real-time tasks while achieving up to 31% throughput improvement for non-real-time tasks.

Method and system for managing switch workloads in a cluster

Abstract: In general, embodiments of the invention relate to a switch that includes a processor, ports, and memory that includes instructions, which when executed by the processor perform a method. The method includes obtaining, via a port, a global task-server mapping from a first server, detecting that one of the ports is congested. The method further includes, based on the detecting, identifying a task associated with the one of the ports using the global-task server mapping, generating a task listing based on the identifying, generating an alert including the task listing, and transmitting the alert to an administrator.

Optimization Techniques for Task Allocation and Scheduling in Distributed Multi-Agent Operations

Abstract: This thesis examines scenarios where multiple autonomous agents collaborate in order to accomplish a global objective. In the environment that we consider, there is a network of agents, each of which offers different sets of capabilities or services that can be used to perform various tasks. In this environment, an agent formulates a problem, divides it into a precedence-constrained set of sub-problems, and determines the optimal allocation of these sub-problems/tasks to other agents so that they are completed in the shortest amount of time. The resulting schedule is constrained by the execution delay of each service, job release times and precedence relations, as well as communication delays between agents. A Mixed Integer-Linear Programming (MILP) approach is presented in the context of a multi-agent problem-solving framework that enables optimal makespans to be computed for complex classifications of scheduling problems that take many different parameters into account. While the algorithm presented takes exponential time to solve and thus is only feasible to use for limited numbers of agents and jobs, it provides a flexible alternative to existing heuristics that model only limited sets of parameters, or settle for approximations of the optimal solution. Complexity results of the algorithm are studied for various scenarios and inputs, as well as recursive applications of the algorithm for hierarchical decompositions of large problems, and optimization of multiple objective functions using Multiple Objective Linear Programming (MOLP) techniques. Technical Supervisor: John J. Turkovich Title: Technical Staff, Charles Stark Draper Laboratory Thesis Supervisor: Munther Dahleh Title: Professor of Electrical Engineering and Computer Science, MIT