Critical networked services are usually regulated by Service Level Agreements (SLAs). In order to ensure SLAs are being met, it is necessary to monitor Service Level Objectives (SLOs). Active measurement mechanisms are usually chosen to perform this monitoring task, which requires measurement probes to be activated in network devices. However, these probes are expensive in terms of computational resources consumption, thus, active measurement mechanisms usually can cover only a fraction of what could be measured, which can lead to SLA violations being missed. Besides that, highly dynamic networking patterns require the ongoing selection of the candidate network destinations for probing and their respective prioritization, a practice that is not well suited for human administrators because configuring the probes is labor-intensive and error-prone. A possibility to improve the detection of SLA violations is the employment of Peer-to-Peer (P2P) technology in order to steer tasks related to a distributed decision making process for probe activation. In this context, a P2P management overlay can be used to coordinate the probe activation and to share measurement results among the network devices. For a node to rely on measurement data from a peer to determine which probes to configure, it needs to know which of the peers are best correlated with itself, i.e., which nodes have the most significance in terms of being indicative of service level violations that might be observed by the node itself. We propose an autonomic P2P solution to coordinate the placement of active measurement probes in large-scale networks. The edge nodes of the network cooperate via a P2P management overlay to determine what destinations should be monitored. Each edge node determines autonomously what destinations to probe considering local measurements and measurement data from other edge nodes. The measurements considered are traffic information from passive measurement results and past service level measurement results from active measurement results. The proposed solution is evaluated using simulation and the results show its feasibility and interesting features.