Lotaru: Locally Estimating Runtimes of Scientific Workflow Tasks in Heterogeneous Clusters

05/23/2022
by   Jonathan Bader, et al.
0

Many scientific workflow scheduling algorithms need to be informed about task runtimes a-priori to conduct efficient scheduling. In heterogeneous cluster infrastructures, this problem becomes aggravated because these runtimes are required for each task-node pair. Using historical data is often not feasible as logs are typically not retained indefinitely and workloads as well as infrastructure changes. In contrast, online methods, which predict task runtimes on specific nodes while the workflow is running, have to cope with the lack of example runs, especially during the start-up. In this paper, we present Lotaru, a novel online method for locally estimating task runtimes in scientific workflows on heterogeneous clusters. Lotaru first profiles all nodes of a cluster with a set of short-running and uniform microbenchmarks. Next, it runs the workflow to be scheduled on the user's local machine with drastically reduced data to determine important task characteristics. Based on these measurements, Lotaru learns a Bayesian linear regression model to predict a task's runtime given the input size and finally adjusts the predicted runtime specifically for each task-node pair in the cluster based on the micro-benchmark results. Due to its Bayesian approach, Lotaru can also compute robust uncertainty estimates and provides them as an input for advanced scheduling methods. Our evaluation with five real-world scientific workflows and different datasets shows that Lotaru significantly outperforms the baselines in terms of prediction errors for homogeneous and heterogeneous clusters.

READ FULL TEXT
research
09/13/2023

Lotaru: Locally Predicting Workflow Task Runtimes for Resource Management on Heterogeneous Infrastructures

Many resource management techniques for task scheduling, energy and carb...
research
08/16/2022

Reshi: Recommending Resources for Scientific Workflow Tasks on Heterogeneous Infrastructures

Scientific workflows typically comprise a multitude of different process...
research
11/09/2021

Tarema: Adaptive Resource Allocation for Scalable Scientific Workflows in Heterogeneous Clusters

Scientific workflow management systems like Nextflow support large-scale...
research
10/10/2018

Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach

Many algorithms in workflow scheduling and resource provisioning rely on...
research
03/31/2022

An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers

Learning knowledge representation of scientific paper data is a problem ...
research
10/28/2020

Rosella: A Self-Driving Distributed Scheduler for Heterogeneous Clusters

Large-scale interactive web services and advanced AI applications make s...
research
03/24/2021

SCHeMa: Scheduling Scientific Containers on a Cluster of Heterogeneous Machines

In the era of data-driven science, conducting computational experiments ...

Please sign up or login with your details

Forgot password? Click here to reset