The vendor lock-in has been a major problem since cloud computing has evolved as on the one hand side hinders a quick transi tion between cloud providers and at the other hand side hinders an ap plication deployment over various clouds at the same time (cross-cloud deployment). While the rise of cross-cloud deployment tools has to some extend limited the impact of vendor lock-in and given more freedom to operators, the fact that applications now are spread out over more than one cloud platform tremendously complicates matters: Either the operator has to interact with the interfaces of various cloud providers or he has to apply custom management tools. This is particularly true when it comes to the task of auto-scaling an application and adapting it to load changes. This paper introduces a novel approach to monitoring and adaptation management that is able to flexibly gather various mon itoring data from virtual machines distributed across cloud providers, to dynamically aggregate the data in the cheapest possible manner, and finally, to evaluate the processed data in order to adapt the application according to user-defined rules.