  When self-adaptive systems encounter changes within their surrounding
environments, they enact tactics to perform necessary adaptations. For example,
a self-adaptive cloud-based system may have a tactic that initiates additional
computing resources when response time thresholds are surpassed, or there may
be a tactic to activate a specific security measure when an intrusion is
detected. In real-world environments, these tactics frequently experience
tactic volatility which is variable behavior during the execution of the
tactic.
  Unfortunately, current self-adaptive approaches do not account for tactic
volatility in their decision-making processes, and merely assume that tactics
do not experience volatility. This limitation creates uncertainty in the
decision-making process and may adversely impact the system's ability to
effectively and efficiently adapt. Additionally, many processes do not properly
account for volatility 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 tactic volatility, we
propose a Tactic Volatility Aware (TVA) solution. Using Multiple Regression
Analysis (MRA), TVA enables self-adaptive systems to accurately estimate the
cost and time required to execute tactics. TVA also utilizes Autoregressive
Integrated Moving Average (ARIMA) for time series forecasting, allowing the
system to proactively maintain specifications.
