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arXiv · 1705.01662

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

Omid Mashayekhi·Hang Qu·Chinmayee Shah·Philip Levis
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Abstract

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

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Omid Mashayekhi, Hang Qu, Chinmayee Shah, Philip Levis. 2017-05-04. Execution Templates: Caching Control Plane Decisions for Strong Scaling of Data Analytics. https://arxiv.org/abs/1705.01662

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