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.