# Towards providing reliable job completion time predictions using PCS

arXiv: https://arxiv.org/abs/2401.10354 · HTML: https://mlsanity.com/paper/2401.10354/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 100 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 18 of 18)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [Serving DNNs like Clockwork: Performance Predictability from the Bottom Up](https://mlsanity.com/paper/2006.02464/) (2020)
- [Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads](https://mlsanity.com/paper/1901.05758/) (2019)
- [Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning](https://mlsanity.com/paper/2008.12260/) (2020)
- [Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads](https://mlsanity.com/paper/2008.09213/) (2020)
- [BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing](https://mlsanity.com/paper/2112.08541/) (2021)
- [Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs](https://mlsanity.com/paper/2204.12013/) (2022)
- [Themis: Fair and Efficient GPU Cluster Scheduling](https://mlsanity.com/paper/1907.01484/) (2019)
- [A Runtime-Based Computational Performance Predictor for Deep Neural Network Training](https://mlsanity.com/paper/2102.00527/) (2021)
- [Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications](https://mlsanity.com/paper/1902.04610/) (2019)
- [Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning](https://mlsanity.com/paper/2210.00093/) (2022)
- [HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline](https://mlsanity.com/paper/2001.02338/) (2020)
- [The Case for Task Sampling based Learning for Cluster Job Scheduling](https://mlsanity.com/paper/2108.10464/) (2021)
- [How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study](https://mlsanity.com/paper/2306.03163/) (2023)
- [UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification](https://mlsanity.com/paper/2210.15056/) (2022)

## Abstract (arXiv)

In this paper we build a case for providing job completion time predictions to cloud users, similar to the delivery date of a package or arrival time of a booked ride. Our analysis reveals that providing predictability can come at the expense of performance and fairness. Existing cloud scheduling systems optimize for extreme points in the trade-off space, making them either extremely unpredictable or impractical. To address this challenge, we present PCS, a new scheduling framework that aims to provide predictability while balancing other traditional objectives. The key idea behind PCS is to use Weighted-Fair-Queueing (WFQ) and find a suitable configuration of different WFQ parameters (e.g., class weights) that meets specific goals for predictability. It uses a simulation-aided search strategy, to efficiently discover WFQ configurations that lie on the Pareto front of the trade-off space between these objectives. We implement and evaluate PCS in the context of DNN job scheduling on GPUs. Our evaluation, on a small scale GPU testbed and larger-scale simulations, shows that PCS can provide accurate completion time estimates while marginally compromising on performance and fairness.