Providentia: Using search-based heuristics to optimize satisficement and competing concerns between functional and non-functional objectives in self-adaptive systems
Providentia: Using search-based heuristics to optimize satisficement and competing concerns between functional and non-functional objectives in self-adaptive systems
复制标题
DOI：
10.1016/j.jss.2019.110497
复制
发表时间：
2020-04-01
影响因子：
3.5
通讯作者：
Cheng, Betty H. C.
中科院分区：
文献类型：
--
作者：
Bowers, Kate M.;Fredericks, Erik M.;Cheng, Betty H. C.
通常，系统可能会受到功能需求（FR）和非功能需求（NFR）的组合的影响，功能需求（FR）规定了行为，非功能需求（NFR）表征了如何满足FR。国家财务报告还提出了可能难以预测的跨领域关切，其中满意度（即，一个NFR的满意度）可能会受到一个或多个FR/NFR的满意度的影响。特别是，自适应系统（SAS）可以在运行时修改系统配置或行为，以连续满足FR和NFR。本文介绍了Providentia，一种基于搜索的技术，以优化满足NFR在SAS经历各种来源的不确定性。Providentia探索了加权FR的不同组合，以最大限度地提高NFR/FR满意度。实验结果表明，Providentia优化的目标模型显着提高满意度的SAS相比，手动和随机生成的权重和子目标。此外，我们应用超启发式（Providentia-SAW）来平衡NFR、FR和适应数量的贡献，并进一步改进Providentia技术。我们应用Providentia和Providentia-SAW在不同的应用领域，涉及远程数据镜像网络和机器人真空控制器，分别为两个案例研究。(C)2019爱思唯尔公司All rights reserved.
In general, a system may be subject to a combination of functional requirements (FRs) that dictate behavior and non-functional requirements (NFRs) that characterize how FRs are to be satisfied. NFRs also introduce cross-cutting concerns that may be difficult to predict, where the degree of satisfaction (i.e., satisficement) of one NFR may be impacted by the satisficement of one or more FRs/NFRs. In particular, self-adaptive systems (SASs) can modify system configurations or behaviors at run time to continuously satisfy FRs and NFRs. This paper presents Providentia, a search-based technique to optimize the satisficement of NFRs in an SAS experiencing various sources of uncertainty. Providentia explores different combinations of weighted FRs to maximize NFR/FR satisficement. Experimental results suggest that Providentia-optimized goal models significantly improve the satisficement of an SAS when compared with manually- and randomly-generated weights and subgoals. Additionally, we apply a hyper-heuristic (Providentia-SAW) to balance the contribution of NFRs, FRs, and the number of adaptations and further improve the Providentia technique. We apply Providentia and Providentia-SAW to two case studies in different application domains involving a remote data mirroring network and a robotic vacuum controller, respectively. (C) 2019 Elsevier Inc. All rights reserved.