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Dynamic Task Scheduling and Adaptive GPU Resource Allocation in the Cloud
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AI
DOI：10.1109/TNSM.2025.3635529.png)
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Abstract
En 中文
The growing demand for computational power in cloud computing has made Graphics Processing Units (GPUs) essential for providing substantial computational capacity. Efficiently allocating GPU resources is crucial due to their high cost. Additionally, it’s necessary to consider cloud environment characteristics, such as dynamic workloads, multi-tenancy, and requirements like isolation. One key challenge is efficiently allocating GPU resources while maintaining isolation and adapting to dynamic workload fluctuations. Another challenge is ensuring scheduling maintains fairness between tenants while meeting task requirements (e.g., completion deadlines). While existing approaches have addressed each challenge individually, none have tackled both challenges simultaneously. This is especially important in dynamic environments where applications continuously request and release GPU resources. This paper introduces a new dynamic GPU resource allocation method, incorporating fair and requirement-aware task scheduling. We present a novel algorithm that leverages the multitasking capabilities of GPUs supported by both hardware and software. The algorithm schedules tasks and continuously reassesses resource allocation as new tasks arrive to ensure fairness. Simultaneously, it adjusts allocations to maintain isolation and satisfy task requirements. Experimental results indicate that our proposed algorithm offers several advantages over existing state-of-the-art solutions. It reduces GPU resource usage by 88% and significantly decreases task completion times.
Keywords:
Fair resource sharing
dynamic resource management
task scheduling
GPU resource allocation
efficiency in cloud computing
Journal
IF:
5.4
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527
Citations:
9.2K