Self-adaptive systems frequently use tactics to perform adaptations. Tactic examples include the implementation of additional security measures when an intrusion is detected, or activating a cooling mechanism when temperature thresholds are surpassed. Tactic volatility occurs in real-world systems and is defined as variable behavior in the attributes of a tactic, such as its latency or cost. A system's inability to effectively account for tactic volatility adversely impacts its efficiency and resiliency against the dynamics of real-world environments. To enable systems' efficiency against tactic volatility, we propose a Tactic Volatility Aware (TVA-E) process utilizing evolved Recurrent Neural Networks (eRNN) to provide accurate tactic predictions. TVA-E is also the first known process to take advantage of uncertainty reduction tactics to provide additional information to the decision-making process and reduce uncertainty. TVA-E easily integrates into popular adaptation processes enabling it to immediately benefit a large number of existing self-adaptive systems. Simulations using 52,106 tactic records demonstrate that: I) eRNN is an effective prediction mechanism, II) TVA-E represents an improvement over existing state-of-the-art processes in accounting for tactic volatility, and III) Uncertainty reduction tactics are beneficial in accounting for tactic volatility. The developed dataset and tool can be found at https://tacticvolatility.github.io/
翻译:自适应系统经常使用战术进行适应。 战术实例包括:在发现入侵时执行额外的安全措施,或在温度阈值超过温度阈值时启动冷却机制。 战术波动发生在现实世界系统中,被定义为战术属性的可变行为,如潜伏或成本。 一个系统无法有效地说明战术波动对实际世界环境中的动态影响其效率和复原力。 为使系统能有效防止战术波动,我们提议采用战术波动意识(TVA-E)程序,利用不断发展的经常神经网络(eNNN)提供准确的战术预测。 TVA-E也是第一个已知的利用不确定性减少战术为决策进程提供更多信息并减少不确定性的过程。 TVA-E很容易地将战术波动因素纳入大众适应进程,使其能够立即使大量现有的自适应系统受益。 使用52 106战术记录的模拟显示:I(TVA-ENN)是一个有效的预测机制,二) TVA-E代表了现有州-战术策略的改进。 用于减少当前国家-战术性战略波动的Annex-stoltal 会计方法的改进。