Autonomicity is a golden featurewhen 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 achievedwithout proper modeling techniques that alloweach 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 Cloudservice 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.
|Titolo:||MARC: A resource consumption modeling service for self-aware autonomous agents|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||01.1 Articolo in Rivista|
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|master.pdf||Articolo principale||Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)||Accesso riservato|