Kubernetes cluster optimization using hybrid shared-state scheduling framework

This paper presents a novel approach for scheduling the workloads in a Kubernetes cluster, which are sometimes unequally distributed across the environment or deal with fluctuations in terms of resources utilization. Our proposal looks at a hybrid shared-state scheduling framework model that delegates most of the tasks to the distributed scheduling agents and has a scheduling correction function that mainly processes the unscheduled and unprioritized tasks. The scheduling decisions are made based on the entire cluster state which is synchronized and periodically updated by a master-state agent. By preserving the Kubernetes objects and concepts, we analyzed the proposed scheduler behavior under different scenarios, for instance we tested the failover/recovery behavior in a deployed Kubernetes cluster. Moreover, our findings show that in situations like collocation interference or priority preemption, other centralized scheduling frameworks integrated with the Kubernetes system might not perform accordingly due to high latency derived from the calculation of load spreading. In a wider context of the existing scheduling frameworks for container clusters, the distributed models lack of visibility at an upper-level scheduler might generate conflicting job placements. Therefore, our proposed scheduler encompasses the functionality of both centralized and distributed frameworks. By employing a synchronized cluster state, we ensure an optimal scheduling mechanism for the resources utilization.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Network Architectures and Service

SciPipe : A workflow library for agile development of complex and dynamic bioinformatics pipelines

Background: The complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines, assist with job automation, and aid reproducibility of analyses. Many contemporary workflow tools are specialized or not designed for highly complex workflows, such as with nested loops, dynamic scheduling, and parametrization, which is common in, e.g., machine learning. Findings: SciPipe is a workflow programming library implemented in the programming language Go, for managing complex and dynamic pipelines in bioinformatics, cheminformatics, and other fields. SciPipe helps in particular with workflow constructs common in machine learning, such as extensive branching, parameter sweeps, and dynamic scheduling and parametrization of downstream tasks. SciPipe builds on flow-based programming principles to support agile development of workflows based on a library of self-contained, reusable components. It supports running subsets of workflows for improved iterative development and provides a data-centric audit logging feature that saves a full audit trace for every output file of a workflow, which can be converted to other formats such as HTML, TeX, and PDF on demand. The utility of SciPipe is demonstrated with a machine learning pipeline, a genomics, and a transcriptomics pipeline. Conclusions: SciPipe provides a solution for agile development of complex and dynamic pipelines, especially in machine learning, through a flexible application programming interface suitable for scientists used to programming or scripting