  Increasing scale and heterogeneity in data centers have led to the
development of federated clusters such as KubeFed, Hydra, and Pigeon, that
federate individual data center clusters. In our work, we introduce Megha, a
novel decentralized resource management framework for such federated clusters.
Megha employs flexible logical partitioning of clusters to distribute its
scheduling load, ensuring that the requirements of the workload are satisfied
with very low scheduling overheads. It uses a distributed global scheduler that
does not rely on a centralized data store but, instead, works with eventual
consistency, unlike other schedulers that use a tiered architecture or rely on
centralized databases. Our experiments with Megha show that it can schedule
tasks taking into account fairness and placement constraints with low resource
allocation times - in the order of tens of milliseconds.
