ActivFORMS: A Formally-Founded Model-Based Approach to Engineer Self-Adaptive Systems
Self-adaptation equips a computing system with a feedback loop that enables it dealing with change caused by uncertainties during operation, such as changing availability of resources and fluctuating workloads. To ensure that the system complies with the adaptation goals, recent research suggests the use of formal techniques at runtime. Yet, existing approaches have three limitations that affect their practical applicability: (i) they ignore correctness of the behavior of the feedback loop, (ii) they rely on exhaustive verification at runtime to select adaptation options to realize the adaptation goals, which is time and resource demanding, and (iii) they provide limited or no support for changing adaptation goals at runtime. To tackle these shortcomings, we present ActivFORMS (Active FORmal Models for Self-adaptation). ActivFORMS contributes an end-to-end approach for engineering self-adaptive systems, spanning four main stages of the life cycle of a feedback loop: design, deployment, runtime adaptation, and evolution. We also present ActivFORMS-ta, a tool-supported instance of ActivFORMS that leverages timed automata models and statistical model checking at runtime. We validate the research results using an IoT application for building security monitoring that is deployed in Leuven. The experimental results demonstrate that ActivFORMS supports correctness of the behavior of the feedback loop, achieves the adaptation goals in an efficient way, and supports changing adaptation goals at runtime.
Fri 19 MayDisplayed time zone: Hobart change
11:00 - 12:30 | Runtime analysis and self-adaptationTechnical Track / NIER - New Ideas and Emerging Results / SEIP - Software Engineering in Practice / Journal-First Papers at Level G - Plenary Room 1 Chair(s): Domenico Bianculli University of Luxembourg | ||
11:00 15mTalk | Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention Technical Track Cheryl Lee The Chinese University of Hong Kong, Tianyi Yang The Chinese University of Hong Kong, Zhuangbin Chen Chinese University of Hong Kong, China, Yuxin Su Sun Yat-sen University, Yongqiang Yang Huawei Technologies, Michael Lyu The Chinese University of Hong Kong Pre-print | ||
11:15 15mTalk | Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models Technical Track Toufique Ahmed University of California at Davis, Supriyo Ghosh Microsoft, Chetan Bansal Microsoft Research, Thomas Zimmermann Microsoft Research, Xuchao Zhang Microsoft, Saravanakumar Rajmohan Microsoft 365 Pre-print | ||
11:30 15mTalk | Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data Technical Track Cheryl Lee The Chinese University of Hong Kong, Tianyi Yang The Chinese University of Hong Kong, Zhuangbin Chen Chinese University of Hong Kong, China, Yuxin Su Sun Yat-sen University, Michael Lyu The Chinese University of Hong Kong Pre-print | ||
11:45 15mTalk | LogReducer: Identify and Reduce Log Hotspots in Kernel on the Fly Technical Track Guangba Yu Sun Yat-Sen University, Pengfei Chen Sun Yat-Sen University, Pairui Li Tencent Inc., Tianjun Weng Tencent Inc., Haibing Zheng Tencent, Yuetang Deng Tencent, Zibin Zheng School of Software Engineering, Sun Yat-sen University Pre-print | ||
12:00 15mTalk | TraceArk: Towards Actionable Performance Anomaly Alerting for Online Service Systems SEIP - Software Engineering in Practice Zhengran Zeng Southern University of Science and Technology, Yuqun Zhang Southern University of Science and Technology, Yong Xu Microsoft Research, Minghua Ma Microsoft Research, Bo Qiao Microsoft Research, Wentao Zou , Qingjun Chen , Meng Zhang , Xu Zhang Microsoft Research, Hongyu Zhang The University of Newcastle, Xuedong Gao , Hao Fan , Saravan Rajmohan Microsoft 365, Qingwei Lin Microsoft Research, Dongmei Zhang Microsoft Research | ||
12:15 7mTalk | ActivFORMS: A Formally-Founded Model-Based Approach to Engineer Self-Adaptive Systems Journal-First Papers | ||
12:22 7mTalk | Auto-Logging: AI-centred Logging Instrumentation NIER - New Ideas and Emerging Results Pre-print |