Overview
Syllabus
Authors Introduction
Challenges in Cluster Scheduling
Learning Runtime Properties for Cluster Scheduling
Widely Used Approach for Learning: History-based Learning
History-based Learning: Assumptions and Reality
Poor Performance of the State-of-the-Art History-based Predictor
SLearn: A Novel Approach for Learning Runtime Properties
Learning in Time History vs Learning in Space SLearn
Comparing Prediction Accuracy: Large Scale Trace-based Analysis
Comparing Coefficients of Variations CoVs across Space and Time
Varying the History Length in CoV comparison
Comparing Prediction Overhead: Simulation and Testbed Experiments Using GS
SLearn's Implementation and Design
Baselines and Experimental Setup
Simulation and Testbed Experimental Results
SLearn for DAG and Future Work
SLearn Summary
Taught by
USENIX