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An Elastic Job Scheduler for HPC Applications on the Cloud
Distributed, Parallel, and Cluster Computing · Submitted 16 Oct 2025 · Published version
abstract compiled · 2026-09-29 · no language-model review
The authors' main claim We show that our elastic scheduler, with the ability to rescale HPC jobs with minimal overhead, demonstrates significant performance improvements over traditional static schedulers.
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The abstract, sentence by sentence
The last few years have seen an increase in adoption of the cloud for running HPC applications. The pay-as-you-go cost model of these cloud resources has necessitated the development of specialized programming models and schedulers for HPC jobs for efficient utilization of cloud resources. A key aspect of efficient utilization is the ability to rescale applications on the fly to maximize the utilization of cloud resources. Most commonly used parallel programming models like MPI have traditionally not supported autoscaling either in a cloud environment or on supercomputers. While more recent work has been done to implement this functionality in MPI, it is still nascent and requires additional programmer effort. Charm++ is a parallel programming model that natively supports dynamic rescaling through its migratable objects paradigm. In this paper, we present a Kubernetes operator to run Charm++ applications on a Kubernetes cluster. We then present a priority-based elastic job scheduler that can dynamically rescale jobs based on the state of a Kubernetes cluster to maximize cluster utilization while minimizing response time for high-priority jobs. We show that our elastic scheduler, with the ability to rescale HPC jobs with minimal overhead, demonstrates significant performance improvements over traditional static schedulers.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The last few years have seen an increase in adoption of the cloud for running HPC applications. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | The pay-as-you-go cost model of these cloud resources has necessitated the development of specialized programming models and schedulers for HPC jobs for efficient utilization of cloud resources. | background 0.99 | 0.04 | 0.02 | asserted 1.00 |
| s2 | A key aspect of efficient utilization is the ability to rescale applications on the fly to maximize the utilization of cloud resources. | background 0.99 | 0.04 | 0.03 | asserted 1.00 |
| s3 | Most commonly used parallel programming models like MPI have traditionally not supported autoscaling either in a cloud environment or on supercomputers. | background 0.61 | 0.04 | 0.01 | asserted 1.00 |
| s4 | While more recent work has been done to implement this functionality in MPI, it is still nascent and requires additional programmer effort. | background 0.62 | 0.04 | 0.02 | asserted 0.99 |
| s5 | Charm++ is a parallel programming model that natively supports dynamic rescaling through its migratable objects paradigm. | background 0.99 | 0.05 | 0.02 | asserted 1.00 |
| s6 | In this paper, we present a Kubernetes operator to run Charm++ applications on a Kubernetes cluster. | contribution 0.88 | 0.92 | 0.04 | asserted 0.99 |
| s7 | We then present a priority-based elastic job scheduler that can dynamically rescale jobs based on the state of a Kubernetes cluster to maximize cluster utilization while minimizing response time for high-priority jobs. | contribution 0.84 | 0.91 | 0.20 | asserted 0.96 |
| s8 | We show that our elastic scheduler, with the ability to rescale HPC jobs with minimal overhead, demonstrates significant performance improvements over traditional static schedulers. | result 1.00 | 0.96 | 0.88 | asserted 1.00 |
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The paper
Original abstract
The last few years have seen an increase in adoption of the cloud for running HPC applications. The pay-as-you-go cost model of these cloud resources has necessitated the development of specialized programming models and schedulers for HPC jobs for efficient utilization of cloud resources. A key aspect of efficient utilization is the ability to rescale applications on the fly to maximize the utilization of cloud resources. Most commonly used parallel programming models like MPI have traditionally not supported autoscaling either in a cloud environment or on supercomputers. While more recent work has been done to implement this functionality in MPI, it is still nascent and requires additional programmer effort. Charm++ is a parallel programming model that natively supports dynamic rescaling through its migratable objects paradigm. In this paper, we present a Kubernetes operator to run Charm++ applications on a Kubernetes cluster. We then present a priority-based elastic job scheduler that can dynamically rescale jobs based on the state of a Kubernetes cluster to maximize cluster utilization while minimizing response time for high-priority jobs. We show that our elastic scheduler, with the ability to rescale HPC jobs with minimal overhead, demonstrates significant performance improvements over traditional static schedulers.
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