A Shadow-Like Task Migration Model Based on Context Semantics for Mobile and Pervasive Environments

A Shadow-Like Task Migration Model Based on Context Semantics for Mobile and Pervasive Environments
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移动和普适环境中基于上下文语义的类影子任务迁移模型

DOI:
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发表时间:
2011
影响因子:
0.7
通讯作者:
*Feilong Tang, Song Guo, Minyi Guo et al.
*Feilong Tang, Song Guo, Minyi Guo et al.
中科院分区:
计算机科学4区
文献类型:
--
作者:
*Feilong Tang, Song Guo, Minyi Guo et al.

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普适计算是一种以用户为中心的移动的计算模式,当用户移动时,任务应该以阴影的方式在不同的平台上迁移。在本文中,我们提出了一个上下文敏感的任务迁移模型,恢复程序状态和重新绑定资源的任务迁移的上下文语义的基础上,通过插入资源描述和状态描述部分的源程序。基于该模型,我们设计并开发了一个任务迁移框架xMozart,它在上下文感知方面扩展了Mozart平台。我们的方法可以恢复任务状态和重新绑定资源的上下文感知的方式,以及支持多模态I/O交互。大量的实验表明,我们的方法可以迁移任务,恢复他们从最后的断点像阴影移动沿着与用户。
Pervasive computing is a user-centric mobile computing paradigm, in which tasks should be migrated over different platforms in a shadow-like way when users move around. In this paper, we propose a context-sensitive task migration model that recovers program states and rebinds resources for task migrations based on context semantics through inserting resource description and state description sections in source programs. Based on our model, we design and develop a task migration framework xMozart which extends the Mozart platform in terms of context awareness. Our approach can recover task states and rebind resources in the context-aware way, as well as support multi-modality I/O interactions. The extensive experiments demonstrate that our approach can migrate tasks by resuming them from the last broken points like shadows moving along with the users.
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