  We present a federated, asynchronous, memory-limited algorithm for online
task scheduling across large-scale networks of hundreds of workers. This is
achieved through recent advancements in federated edge computing that unlocks
the ability to incrementally compute local model updates within each node
separately. This local model is then used along with incoming data to generate
a rejection signal which reflects the overall node responsiveness and if it is
able to accept an incoming task without resulting in degraded performance.
Through this innovation, we allow each node to execute scheduling decisions on
whether to accept an incoming job independently based on the workload seen thus
far. Further, using the aggregate of the iterates a global view of the system
can be constructed, as needed, and could be used to produce a holistic
perspective of the system. We complement our findings, by an empirical
evaluation on a large-scale real-world dataset of traces from a virtualized
production data center that shows, while using limited memory, that our
algorithm exhibits state-of-the-art performance. Concretely, it is able to
predict changes in the system responsiveness ahead of time based on the
industry-standard CPU-Ready metric and, in turn, can lead to better scheduling
decisions and overall utilization of the available resources. Finally, in the
absence of communication latency, it exhibits attractive horizontal
scalability.
