{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hyperdrive-scheduling-serverless-functions-in","title":"HyperDrive: Scheduling Serverless Functions in the Edge-Cloud-Space 3D Continuum","arxiv_id":"2410.16026","date":"2024-10-21","proceeding":null,"authors":["Thomas Pusztai","Cynthia Marcelino","Stefan Nastic"],"abstract":"The number of Low Earth Orbit~(LEO) satellites has grown enormously in the past years. Their abundance and low orbits allow for low latency communication with a satellite almost anywhere on Earth, and high-speed inter-satellite laser links~(ISLs) enable a quick exchange of large amounts of data among satellites. As the computational capabilities of LEO satellites grow, they are becoming eligible as general-purpose compute nodes. In the 3D continuum, which combines Cloud and Edge nodes on Earth and satellites in space into a seamless computing fabric, workloads can be executed on any of the aforementioned compute nodes, depending on where it is most beneficial. However, scheduling on LEO satellites moving at approx. 27,000 km/h requires picking the satellite with the lowest latency to all data sources (ground and, possibly, earth observation satellites). Dissipating heat from onboard hardware is challenging when facing the sun and workloads must not drain the satellite's batteries. These factors make meeting SLOs more challenging than in the Edge-Cloud continuum, i.e., on Earth alone. We present HyperDrive, an SLO-aware scheduler for serverless functions specifically designed for the 3D continuum. It places functions on Cloud, Edge, or Space compute nodes, based on their availability and ability to meet the SLO requirements of the workflow. We evaluate HyperDrive using a wildfire disaster response use case with high Earth Observation data processing requirements and stringent SLOs, showing that it enables the design and execution of such next-generation 3D scenarios with 71% lower network latency than the best baseline scheduler.","url_abs":"https://arxiv.org/abs/2410.16026v1","url_pdf":"https://arxiv.org/pdf/2410.16026v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"hyperdrive-scheduling-serverless-functions-in","repo_url":"https://github.com/polaris-slo-cloud/hyper-drive","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}