Improving the DecisionMaking Process of SelfAdaptive Systems by Accounting for Tactic Volatility When selfadaptive systems encounter changes withintheir surrounding environments, they enacttacticsto performnecessary adaptations. For example, a selfadaptive cloudbasedsystem 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 realworld environments, these tacticsfrequently experiencetactic volatilitywhich is variable behaviorduring the execution of the tactic.Unfortunately, current selfadaptive approaches do not accountfor tactic volatility in their decisionmaking processes, and merelyassume that tactics do not experience volatility. This limitationcreates uncertainty in the decisionmaking process and mayadversely impact the systemu0027s ability to effectively and efficientlyadapt. Additionally, many processes do not properly account forvolatility that may effect the systemu0027s Service Level Agreement(SLA). This can limit the systemu0027s 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 selfadaptive 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.

Adaptive Trading of Cloud of Things Resources Cloud of Things (CoT) consists of heterogeneous Cloud and Internet of Things (IoT) resources. CoT increasingly requires adaptive runtime management due to the CoT dynamism, environmental uncertainties and unpredictable changes in IoT resources. Adapting to these changes benefits particularly trading of CoT resources where the adaptability of traded resources and applications remains a challenge. Runtime changes in CoT trading environments can impact vital aspects including resource allocation, resource utilisation and application performance. This paper adopts monitoring, analysis, planning and execution (MAPE) model from autonomic computing to support adaptations when trading CoT resources. This is achieved by applying the MAPE model to systematically capture and identify changes in CoT environment. Based on the identified adaptations, an adaptive model is proposed to react to these changes.