English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

When self-adaptive systems encounter changes within their surroundingenvironments, they enact tactics to perform necessary adaptations. For example,a self-adaptive cloud-based system may have a tactic that initiates additionalcomputing resources when response time thresholds are surpassed, or there maybe a tactic to activate a specific security measure when an intrusion isdetected. In real-world environments, these tactics frequently experiencetactic volatility which is variable behavior during the execution of thetactic.  Unfortunately, current self-adaptive approaches do not account for tacticvolatility in their decision-making processes, and merely assume that tacticsdo not experience volatility. This limitation creates uncertainty in thedecision-making process and may adversely impact the system's ability toeffectively and efficiently adapt. Additionally, many processes do not properlyaccount for volatility that may effect the system's Service Level Agreement(SLA). This can limit the system's ability to act proactively, especially whenutilizing tactics that contain latency.  To address the challenge of sufficiently accounting for tactic volatility, wepropose a Tactic Volatility Aware (TVA) solution. Using Multiple RegressionAnalysis (MRA), TVA enables self-adaptive systems to accurately estimate thecost and time required to execute tactics. TVA also utilizes AutoregressiveIntegrated Moving Average (ARIMA) for time series forecasting, allowing thesystem to proactively maintain specifications.

Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility