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Large-scale interactive web services and advanced AI applications makesophisticated decisions in real-time, based on executing a massive amount ofcomputation tasks on thousands of servers. Task schedulers, which often operatein heterogeneous and volatile environments, require high throughput, i.e.,scheduling millions of tasks per second, and low latency, i.e., incurringminimal scheduling delays for millisecond-level tasks. Scheduling is furthercomplicated by other users' workloads in a shared system, other backgroundactivities, and the diverse hardware configurations inside datacenters.  We present Rosella, a new self-driving, distributed approach for taskscheduling in heterogeneous clusters. Rosella automatically learns the computeenvironment and adjusts its scheduling policy in real-time. The solutionprovides high throughput and low latency simultaneously because it runs inparallel on multiple machines with minimum coordination and only performssimple operations for each scheduling decision. Our learning module monitorstotal system load and uses the information to dynamically determine optimalestimation strategy for the backends' compute-power. Rosella generalizespower-of-two-choice algorithms to handle heterogeneous workers, reducing themax queue length of O(log n) obtained by prior algorithms to O(log log n). Weevaluate Rosella with a variety of workloads on a 32-node AWS cluster.Experimental results show that Rosella significantly reduces task responsetime, and adapts to environment changes quickly.

Rosella: A Self-Driving Distributed Scheduler for Heterogeneous Clusters