Reshi: Recommending Resources for Scientific Workflow Tasks on Heterogeneous Infrastructures

Scientific workflows typically comprise a multitude of different processing steps which often are executed in parallel on different partitions of the input data. These executions, in turn, must be scheduled on the compute nodes of the computational infrastructure at hand. This assignment is complicated by the facts that (a) tasks typically have highly heterogeneous resource requirements and (b) in many infrastructures, compute nodes offer highly heterogeneous resources. In consequence, predictions of the runtime of a given task on a given node, as required by many scheduling algorithms, are often rather imprecise, which can lead to sub-optimal scheduling decisions. We propose Reshi, a method for recommending task-node assignments during workflow execution that can cope with heterogeneous tasks and heterogeneous nodes. Reshi approaches the problem as a regression task, where task-node pairs are modeled as feature vectors over the results of dedicated micro benchmarks and past task executions. Based on these features, Reshi trains a regression tree model to rank and recommend nodes for each ready-to-run task, which can be used as input to a scheduler. For our evaluation, we benchmarked 27 AWS machine types using three representative workflows. We compare Reshi’s recommendations with three state-of-the-art schedulers. Our evaluation shows that Reshi outperforms HEFT by a mean makespan reduction of 7.18% and 18.01% assuming a mean task runtime prediction error of 15%.

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

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.

Geoflow - Novel Workflow Implementations To Facilitate Big EO Data Workflows in Nextflow
FORCE on Nextflow: Scalable Analysis of Earth Observation data on Commodity Clusters

Modern Earth Observation (EO) often analyses hundreds of gigabytes of data from thousands of satellite images. This data usually is processed with hand-made scripts combining several tools implementing the various steps within such an analysis. A fair amount of geographers' work goes into optimization, tuning, and parallelization in such a setting. Development becomes even more complicated when compute clusters become necessary, introducing issues like scheduling, remote data access, and generally a greatly increased infrastructure complexity. Furthermore, tailor-made systems are often optimized to one specific system and cannot easily be adapted to other infrastructures. Data Analysis Workflow engines promise to relieve the workflow developer from finding custom solutions to these issues and thereby improve scalability, reproducibility, and reusability of workflows while reducing development cost at the infrastructure side. On the other hand, they require the workflow to be programmed in a particular language, to obey certain principles of distributed processing, and to properly configure and tune the execution stack, which puts additional burden to data scientists. Here, we study this trade-off using a concrete EO workflow for long-term vegetation dynamics in the Mediterranean. The original workflow was programmed with FORCE, a custom-made framework for assembling and executing EO workflows on stand-alone servers. We ported it to the scientific workflow system Nextflow, which is capable of seamlessly orchestrating workflows over a large variety of infrastructures. We discuss the pitfalls we faced while porting the workflow, advantages and disadvantages of such an approach, and compare in detail the efficiency of both implementations on various infrastructures. We quantify the overhead in execution time incurred by the workflow engine and give hints on how to deal with heterogeneous tasks. Overall, our Nextflow implementation shows promising behavior in terms of reusability and scalability, though this does not apply to all workflow stages.