When self-adaptive systems encounter changes withintheir surrounding environments, they enacttacticsto performnecessary adaptations. For example, a self-adaptive cloud-basedsystem may have a tactic that initiates additional computingresources when response time thresholds are surpassed, or theremay be a tactic to activate a specific security measure when anintrusion is detected. In real-world environments, these tacticsfrequently experiencetactic volatilitywhich is variable behaviorduring the execution of the tactic.Unfortunately, current self-adaptive approaches do not accountfor tactic volatility in their decision-making processes, and merelyassume that tactics do not experience volatility. This limitationcreates uncertainty in the decision-making process and mayadversely impact the system's ability to effectively and efficientlyadapt. Additionally, many processes do not properly account forvolatility that may effect the system's Service Level Agreement(SLA). This can limit the system's ability to act proactively, especially when utilizing tactics that contain latency.To address the challenge of sufficiently accounting for tacticvolatility, we propose aTactic Volatility Aware(TVA) solution.Using Multiple Regression Analysis (MRA), TVA enables self-adaptive systems to accurately estimate the cost and timerequired to execute tactics. TVA also utilizesAutoregressiveIntegrated Moving Average(ARIMA) for time series forecasting, allowing the system to proactively maintain specifications.

@inproceedings{paperbot5299,
  title = {Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility},
  author = {Jeffrey Palmerino and Qi Yu and Travis Desell and Daniel Krutz},
  booktitle = {ASE},
  year = {2019},
  doi = {10.1109/ase.2019.00092}
}