Improvements in containerization and orchestration have facilitated the management
of distributed measurement platforms, helping rationalize networks and services monitoring,
a vital but demanding activity. Nonetheless, orchestration in this domain, still requires
optimization, which we address in this paper. More precisely, we invest our efforts
in the optimization of allocating and scheduling network and service measurement applications,
deployed in a pool of physical machines in the network. First, we formulate the problem
into an Integer Linear Programming model and prove it to be NP-Hard. Then, we propose
an approximate resolution based on the Ant Colony System (ACS) metaheuristic. Finally,
we introduce an open-source distributed measurement platform implementing our algorithm.
Results show that the ACS algorithm provides efficient orchestration solutions that
outperform those provided by a Branch and Bound algorithm and a First Fit Decreasing
algorithm.