Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility
This addresses uncertainty in decision-making for self-adaptive systems, particularly in cloud-based or SLA-sensitive environments, but appears incremental as it builds on existing methods like MRA and ARIMA.
The paper tackles the problem of tactic volatility in self-adaptive systems, which current approaches ignore, leading to uncertainty and inefficiency in adaptation; the proposed Tactic Volatility Aware (TVA) solution uses Multiple Regression Analysis and ARIMA to estimate costs and times, enabling proactive maintenance.
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.