Load shedding is an integral component in many Data Stream Management Systems, aiming at preventing the response time from exceeding a user-specified delay target under overload situations. The currently best performing load shedder determines the correct amount of load to shed by utilizing a feedback loop for correcting the statistics-based estimations. Although this load shedder outperforms previous works in controlling response time as well as minimizing data loss, it requires a manually-tuned parameter and cannot work with complex query networks containing joins, aggregations or shared operators. In this paper, we propose SEaMLeSS — SElf Managing Load Shedding for data Stream management systems, which extends and rectifies these limitations of the state-of-the-art load shedder while making it applicable for multi-tenant servers. We implement and evaluate our extensions in AQSIOS, our experimental DSMS prototype, using both synthetic and real input patterns.