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

Authors: 

Omid Mashayekhi, Hang Qu, Chinmayee Shah, and Philip Levis, Stanford University

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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BibTeX
@inproceedings {203221,
author = {Omid Mashayekhi and Hang Qu and Chinmayee Shah and Philip Levis},
title = {Execution Templates: Caching Control Plane Decisions for Strong Scaling of Data Analytics},
booktitle = {2017 {USENIX} Annual Technical Conference ({USENIX} {ATC} 17)},
year = {2017},
isbn = {978-1-931971-38-6},
address = {Santa Clara, CA},
pages = {513--526},
url = {https://www.usenix.org/conference/atc17/technical-sessions/presentation/mashayekhi},
publisher = {{USENIX} Association},
}