Bringing intelligent decision-making to industrial facilities involves coordinating a complex ecosystem of applications with different performance requirements that often depend on each other. A promising approach to managing this complexity is leveraging loosely coupled software architectures, such as microservices, with distributed infrastructures, such as the Edge-to-Cloud Continuum. The order in which microservices are provisioned in the Edge-to-Cloud Continuum can significantly impact outcomes when distributing tasks to agents. Poor provisioning orderings can lead to non-critical microservices overburdening servers with lower latency while leaving latencysensitive microservices with no option but to run on more distant servers. To address this challenge, we introduce a novel provisioning ordering mechanism within a Multi-Armed Bandit (MAB) algorithm, helping to conquer the problem. Simulations based on real-world specifications indicate a $\mathbf{3 8 \%}$ reduction in latency violations and up to 6% decrease in power consumption compared to an unordered approach.

A testbed with open-source tools and commercial equipment is designed to investigate the timing of ML-based xApp operations in DDoS attack detection during the Random Access procedure. Results show high-precision and high-recall ML classifiers operating faster than the Medium Access Control (MAC) layer's Contention Resolution timer. For classifiers with over 97% precision and recall, XGBoost has the fastest execution time and the narrowest estimated run time probability density function.