# Shockwave: Fair and efficient cluster scheduling for dynamic adaptation in machine learning ## Metadata Presented in [arxiv:2210.00093](https://arxiv.org/abs/2210.00093). (Accepted in NSDI 2023) Authors: Pengfei Zheng, Rui Pan, Tarannum Khan, Shivaram Venkataraman, Aditya Akella, *University of Wisconsin-Madison, The University of Texas at Austin* Code: ## Understanding the paper ### TL;DRs This paper presents **Shockwave**, an efficient and *fair* scheduler for *DL training jobs* with *elastic resource requirements*.\ It *extends classic market theory from static settings to dynamic settings* to co-optimize efficiency and fairness; *utilizes stochastic dynamic programming* to handle dynamic changes. ### Limitations of Existing Work * Dynamically adjusting *model structure* or *hyper-parameters* (e.g., batch size) can significantly *accelerate training* without *sacrificing accuracy*. * Existing schedulers *fail to provide fairness and degrade system efficiency* when *the training throughput changes over time* under dynamic adaptation. ### Assumptions * Treat dynamic adaptation as a part of the user’s program that cannot be modified. * Based on the measurement: automatically perform dynamic adaptation can hurt training accuracy. ### Challenges to Extend Market Theory * The market needs to know *utility values* in the future to compute *market equilibrium*; but it is hard to predict utility in the future since dynamic adaptations in jobs are non-deterministically triggered. * Solution: Use *Bayesian statistics* to predict the patterns. * Solving the dynamic market equilibrium for an (infinitely) long time horizon is difficult and impractical. * Solution: Only plan the schedule for *a finite length window* (e.g., 30-60 mins). ### Evaluation * Baselines * OSSP (Open Shop Scheduling) * AlloX * Themis * MSS (Max-Sum-Throughput) * GandivaFair * Pollux * Improve makespan by 1.3x and fairness by 2x with existing *fair schedulers*. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://paper.lingyunyang.com/reading-notes/conference/nsdi-2023/shockwave.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.