Autonomicity is a golden feature when dealing with a high level of complexity. This complexity can be tackled partitioning huge systems in small autonomous modules, i.e., agents. Each agent then needs to be capable of extracting knowledge from its environment and to learn from it, in order to fulfill its goals: this could not be achieved without proper modeling techniques that allow each agent to gaze beyond its sensors. Unfortunately, the simplicity of agents and the complexity of modeling do not fit together, thus demanding for a third party to bridge the gap. Given the opportunities in the field, the main contributions of this work are twofold: (1) we propose a general methodology to model resource consumption trends and (2) we implemented it into MARC, a Cloud-service platform that produces Models-as-a-Service, thus relieving self-aware agents from the burden of building their custom modeling framework. In order to validate the proposed methodology, we set up a custom simulator to generate a wide spectrum of controlled traces: this allowed us to verify the correctness of our framework from a general and comprehensive point of view.

中文翻译：

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马克

在处理高度复杂性时，自主性是一个黄金特征。这种复杂性可以通过将大型系统划分为小型自治模块（即代理）来解决。然后，每个智能体都需要能够从其环境中提取知识并从中学习，以实现其目标：如果没有适当的建模技术允许每个智能体超越其传感器，这是无法实现的。不幸的是，代理的简单性和建模的复杂性并不一致，因此需要第三方来弥合差距。鉴于该领域的机会，这项工作的主要贡献有两个：（1）我们提出了一种模拟资源消耗趋势的通用方法，（2）我们将其实施到 MARC 中，这是一个生成模型即模型的云服务平台。一个服务，从而将具有自我意识的代理从构建自定义建模框架的负担中解脱出来。为了验证所提出的方法，我们设置了一个自定义模拟器来生成广泛的受控跟踪：这使我们能够从一般和全面的角度验证我们框架的正确性。