一句话：提出一种支持任务优先级、人机协作与资源可扩展/异构的资源驱动自适应方法。
资源驱动系统中的任务受有限资源约束，当任务无法获得足够资源时必须进行自适应。本文（博士论文）提出一种新的资源驱动自适应方法，旨在支持三方面能力：基于任务执行时段、相关用户角色以及最低成本适应类型等多准则进行任务优先级排序；让人工用户与软件工具协作准备适应后的任务行为，以便在资源被替换时使用；以及支持资源的可扩展性与异构性。该方法正在实现中，并将通过企业应用场景进行评估。
Resource-driven systems have tasks that are bound by limited resources. These systems must adapt their tasks that cannot gain access to sufficient resources. This dissertation proposes a new resource-driven adaptation approach, which aims to support (1) task prioritisation using multiple criteria such as the time of day that a task should be executed, the role of involved users, and selection of the least costly adaptation types; (2) collaboration between a human user and a software tool for preparing adapted task behaviour to be used when resources are substituted; and (3) resource extensibility and heterogeneity. The proposed approach is being implemented and will be evaluated with scenarios from enterprise applications.
DOI 原文 ·
@inproceedings{paperbot6448,
title = {Towards an approach for resource-driven adaptation},
author = {Paul A. Akiki},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3473098}
}