Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility
Tactics are the actions performed by self-adaptive systems that enable them to adapt to changes in their environments. For a self-adaptive cloud-based system, one tactic may include activating additional computing resources when response time thresholds are surpassed. In real-world environments, tactics will frequently experience tactic volatility. Unfortunately, current self-adaptive approaches do not account for tactic volatility in their decision-making processes, and merely assume that tactics have static attributes. This limitation creates uncertainty in the decision-making process and may adversely impact the system’s ability to perform the most optimal action. Additionally, many self-adaptive processes do not properly anticipate or account for future occurrences and volatility in respect to the Service Level Agreement (SLA). This can limit the system’s ability to act proactively, especially when utilizing tactics that contain latency.
To address the limitation of sufficiently accounting for tactic volatility, we propose a Tactic Volatility Aware (TVA) solution. Using Multiple Regression Analysis (MRA), TVA enables self-adaptive systems to accurately estimate the time required to execute tactics and their associated costs. TVA also utilizes Autoregressive Integrated Moving Average (ARIMA) to perform time series forecasting allowing the system to proactively maintain requirements.
Thu 14 Nov
16:00 - 17:40: Papers - Emerging Domains at Cortez 1 Chair(s): Joshua GarciaUniversity of California, Irvine | ||||||||||||||||||||||||||||||||||||||||||
16:00 - 16:20 Talk | Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility Jeffrey PalmerinoRochester Institute of Technology, Qi YuRochester Institute of Technology, Travis Desell University of North Dakota, Daniel KrutzRochester Institute of Technology Pre-print | |||||||||||||||||||||||||||||||||||||||||
16:20 - 16:40 Talk | Learning-Guided Network Fuzzing for Testing Cyber-Physical System Defences Yuqi ChenSingapore University of Technology and Design, Singapore, Chris PoskittSingapore University of Technology and Design, Jun SunSingapore Management University, Singapore, Sridhar AdepuSingapore University of Technology and Design, Singapore, Fan ZhangZhejiang University, Zhejiang Lab, and Alibaba-Zhejiang University Joint Institute of Frontier Technologies, China Pre-print File Attached | |||||||||||||||||||||||||||||||||||||||||
16:40 - 17:00 Talk | Uncertainty-wise Test Case Generation and Minimization for Cyber-Physical Systems Man ZhangKristiania University, Shaukat AliSimula Research Lab, Tao YueNanjing University of Aeronautics and Astronautics & Simula Research Laboratory Link to publication | |||||||||||||||||||||||||||||||||||||||||
17:00 - 17:20 Talk | Finding Trends in Software Research George MathewDepartment of Computer Science, North Carolina State University, Amritanshu AgrawalWayfair, Tim MenziesNorth Carolina State University Link to publication | |||||||||||||||||||||||||||||||||||||||||
17:20 - 17:30 Demonstration | XRaSE: Towards Virtually Tangible Software using Augmented Reality Rohit MehraAccenture Labs, India, Vibhu Saujanya SharmaAccenture Labs, Vikrant KaulgudAccenture Labs, India, Sanjay PodderAccenture | |||||||||||||||||||||||||||||||||||||||||
17:30 - 17:40 Demonstration | MuSC: A Tool for Mutation Testing of Ethereum Smart Contract Zixin LiNanjing University, Haoran WuState Key Laboratory for Novel Software Technology, Nanjing University, Jiehui XuNanjing University, Xingya WangState Key Laboratory for Novel Software Technology, Nanjing University, Lingming ZhangThe University of Texas at Dallas, Zhenyu ChenNanjing University |