> For the complete documentation index, see [llms.txt](https://paper.lingyunyang.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://paper.lingyunyang.com/reading-notes/conference/socc-2017/slaq.md).

# SLAQ: Quality-driven scheduling for distributed machine learning

## Metadata

Presented in [SoCC 2017](https://doi.org/10.1145/3127479.3127490).

Authors: Haoyu Zhang, Logan Stafman, Andrew Or, Michael J. Freedman, *Princeton University*

## Understanding the paper

### TL;DRs

This paper presents **SLAQ**, which is a cluster scheduling framework that hosts multi-tenant approximate *ML training jobs* running on shared resources.\
It is a **fine-grained job-level scheduler**, which *focuses on the allocation of cluster resources* between competing ML jobs, but does so *short time intervals* (i.e., hundreds of milliseconds to a few seconds).

### Key Insights

* Leverage the *iterative nature* of ML training algorithms.
  * Collect quality and resource usage information from concurrent jobs.
  * Generate quality-improvement predictions for future iterations.

### Limitation of Existing Work

Existing job-level schedulers (YARN, Mesos, Apollo, Hadoop Capacity, Quincy, etc.) mostly allocate resources based on *resource fairness* or *priorities*.\
For ML training jobs, these schedulers often make *suboptimal* scheduling decisions because they are agnostic to the progress (quality improvement) within each job.

### Implementation

The system is implemented within the *Apache Spark framework* and utilizes *Spark MLlib*.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## 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, and the optional `goal` query parameter:

```
GET https://paper.lingyunyang.com/reading-notes/conference/socc-2017/slaq.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.