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Control planes of cloud frameworks trade off between scheduling granularityand performance. Centralized systems schedule at task granularity, but onlyschedule a few thousand tasks per second. Distributed systems schedule hundredsof thousands of tasks per second but changing the schedule is costly.  We present execution templates, a control plane abstraction that can schedulehundreds of thousands of tasks per second while supporting fine-grained,per-task scheduling decisions. Execution templates leverage a program'srepetitive control flow to cache blocks of frequently-executed tasks. Executinga task in a template requires sending a single message. Large-scale schedulingchanges install new templates, while small changes apply edits to existingtemplates.  Evaluations of execution templates in Nimbus, a data analytics framework,find that they provide the fine-grained scheduling flexibility of centralizedcontrol planes while matching the strong scaling of distributed ones. Executiontemplates support complex, real-world applications, such as a fluid simulationwith a triply nested loop and data dependent branches.

Execution Templates: Caching Control Plane Decisions for Strong Scaling of Data Analytics