Fog and edge computing paradigms were adopted to enable Internet of Things (IoT) applications, improving response time and reducing network load. Task allocation algorithms are used on IoT-enabled networks to determine the optimal software placement. However, managing such a network is considerably more complex than allocating the tasks. To simplify management, we propose a general Monitor -Analyze -Plan -Execute over a Knowledge base (MAPE-K) framework in which all requirements for task allocation are fulfilled, and where components can easily be adapted to the use case at hand. This research identifies several pitfalls and proposes solutions. Additionally, we apply this approach to a distributed testbed, comparing it to traditional cloud approaches. ACKNOWLEDGMENTThis research received funding from the Flemish Government (AI Research Program). This article describes work in the context of the DEDICAT 6G project under the European Union (EU) H2020 research and innovation programme (Grant Agreement No. 101016499). The contents of this publication are the sole responsibility of the authors and do not in any way reflect the views of the EU.