Task (computing)

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

Bidirectional dynamic offloading of tasks between a host and a mobile device

Abstract: One or more functions are exposed by a mobile device to a host connected to the mobile device A function of the one or more functions is executed at the mobile device in response to a request from the host, wherein the function is associated with a host task The result of the function is returned to the host

Joint Task Partitioning and User Association for Latency Minimization in Mobile Edge Computing Networks

Abstract: Mobile edge computing (MEC) is a promising solution to support emerging delay-sensitive mobile applications, such as self-driving, augment/virtual reality, and various Internet of Things (IoT) applications. By deploying MEC servers at network edge, e.g., close to cellular base stations (BSs), the computational tasks generated by these applications can be offloaded to edge nodes (ENs) and be quickly executed there. At the same time, with the projected large number of IoT devices, the communication and computational resources allocated to each user can be quite limited, making it challenging to provide low-latency MEC services. In this paper, we investigate the problem of task partitioning and user association in an MEC system, aiming to minimize the average latency of all users. We assume that each task can be partitioned into multiple subtasks that can be executed on local devices (e.g., vehicles), MEC servers, and/or cloud servers; each user can be associated with one of the nearby ENs. The subtasks can be independent of or dependent on each other. For each case, we formulate the joint optimization of task partitioning ratios and user association as a mixed integer programming problem. Each problem is solved by decomposing it into two subproblems. The lower-level subproblem is task partitioning under a given user association, which can be solved optimally. The higher-level subproblem is user association, we propose a dual decomposition-based approach and a matching-based approach to derive near-optimal solutions. Simulation results show that compared to benchmark schemes, the proposed schemes reduce the average latency by about 50% and 40% for the cases of independent and dependent subtasks, respectively.

A Priority-Based Budget Scheduler with Conservative Dataflow Model

Abstract: Currently, the guaranteed throughput of a stream processing application, mapped on a multi-processor system, can be computed with a conservative dataflow model, if only time division multiplex (TDM) schedulers are applied. A TDM scheduler is a budget scheduler. Budget schedulers can be characterized by two parameters: budget and replenishment interval. This paper introduces a priority-based budget scheduler (PBS), which is a budget scheduler that additionally associates a priority with every task. PBS improves the guaranteed minimum throughput of a stream processing application compared to TDM, given the same amount of resources. We construct a conservative dataflow model for a task scheduled by PBS. This dataflow model generalizes previous work, because it is valid for a sequence of execution times instead of one execution time per task which results in an improved accuracy of the model. Given this dataflow model, we can compute the guaranteed minimum throughput of the task graph that implements the stream processing application. Experiments confirm that a significantly higher guaranteed minimum throughput of the task graph can be obtained with PBS instead of TDM schedulers and that a conservative bound on the guaranteed throughput of the task graph can be computed with a dataflow model. Furthermore, our bound on the guaranteed throughput of the task graph is accurate, if the buffer capacities in the task graph do not affect the guaranteed throughput.

Multi-task Multi-view Learning for Heterogeneous Tasks

Abstract: Multi-task multi-view learning deals with the learning scenarios where multiple tasks are associated with each other through multiple shared feature views. All previous works for this problem assume that the tasks use the same set of class labels. However, in real world there exist quite a few applications where the tasks with several views correspond to different set of class labels. This new learning scenario is called Multi-task Multi-view Learning for Heterogeneous Tasks in this study. Then, we propose a Multi-tAsk MUlti-view Discriminant Analysis (MAMUDA) method to solve this problem. Specifically, this method collaboratively learns the feature transformations for different views in different tasks by exploring the shared task-specific and problem intrinsic structures. Additionally, MAMUDA method is convenient to solve the multi-class classification problems. Finally, the experiments on two real-world problems demonstrate the effectiveness of MAMUDA for heterogeneous tasks.

Method and system to manage tasks

Abstract: A method and system to manage tasks are described. The method may comprise providing a graphical user interface to a user, receiving user input via the graphical user interface to identify a composite task and at least two individual tasks, and associating the at least two individual tasks with the composite or virtual task. An operation performed on the composite task may be automatically performed on the individual tasks.