@inproceedings{lubranoAdvancedResourceAllocation2024,
  title = {Advanced {{Resource Allocation}} in the {{Context}} of {{Heterogeneous Workflows Management}}},
  booktitle = {Proceedings of the 2nd {{Workshop}} on {{Workflows}} in {{Distributed Environments}}},
  author = {Lubrano, Francesco and Vercellino, Chiara and Vitali, Giacomo and Viviani, Paolo and Scionti, Alberto and Terzo, Olivier},
  year = {2024},
  month = apr,
  pages = {14--20},
  publisher = {ACM},
  address = {Athens Greece},
  doi = {10.1145/3642978.3652835},
  urldate = {2024-04-23},
  isbn = {9798400705465},
  abstract = {In High-Performance Computing (HPC), workflows are utilized to define and manage a set of interdependent computations which allow the users to extract insights from (scientific) numerical simulations or data analytics. HPC platforms can perform extreme-scale simulations, combining Artificial Intelligence (AI) training and inference and data analytics (we refer to heterogeneous workflows), by providing tools and computing resources which serve a variety of use-cases spanning very diverse application domains (e.g., weather forecasting, quantum mechanics, etc.). Executing such workflows at scale requires to handle dependencies, job submission automation, I/O mechanisms. Despite State-of-the-Art batch schedulers can be configured and integrated with tools accomplishing this automation, a number of cases where resource allocation can lead to inefficiencies still exist. In this paper, to overcome these limitations, we present the WARP (Workflow-aware Advanced Resource Planner), a tool that integrates with workflow management tools and batch schedulers, to reserve in advance resources for an optimal execution of jobs, based on their duration, dependencies and machine load. WARP has been designed to minimize the overall workflow execution, without violating the priority policies for cluster users imposed by the system administrators.},
  langid = {english}
}

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HPC platforms can perform extreme-scale simulations, combining Artificial Intelligence (AI) training and inference and data analytics (we refer to heterogeneous workflows), by providing tools and computing resources which serve a variety of use-cases spanning very diverse application domains (e.g., weather forecasting, quantum mechanics, etc.). Executing such workflows at scale requires to handle dependencies, job submission automation, I/O mechanisms. Despite State-of-the-Art batch schedulers can be configured and integrated with tools accomplishing this automation, a number of cases where resource allocation can lead to inefficiencies still exist. In this paper, to overcome these limitations, we present the WARP (Workflow-aware Advanced Resource Planner), a tool that integrates with workflow management tools and batch schedulers, to reserve in advance resources for an optimal execution of jobs, based on their duration, dependencies and machine load. WARP has been designed to minimize the overall workflow execution, without violating the priority policies for cluster users imposed by the system administrators.","langid":"english","bibtex":"@inproceedings{lubranoAdvancedResourceAllocation2024,\n title = {Advanced {{Resource Allocation}} in the {{Context}} of {{Heterogeneous Workflows Management}}},\n booktitle = {Proceedings of the 2nd {{Workshop}} on {{Workflows}} in {{Distributed Environments}}},\n author = {Lubrano, Francesco and Vercellino, Chiara and Vitali, Giacomo and Viviani, Paolo and Scionti, Alberto and Terzo, Olivier},\n year = {2024},\n month = apr,\n pages = {14--20},\n publisher = {ACM},\n address = {Athens Greece},\n doi = {10.1145/3642978.3652835},\n urldate = {2024-04-23},\n isbn = {9798400705465},\n abstract = {In High-Performance Computing (HPC), workflows are utilized to define and manage a set of interdependent computations which allow the users to extract insights from (scientific) numerical simulations or data analytics. 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