Eva: Cost-Efficient Cloud-Based Cluster Scheduling
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917950043193344 |
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| author | Chang, Tzu-Tao Venkataraman, Shivaram |
| author_facet | Chang, Tzu-Tao Venkataraman, Shivaram |
| contents | Cloud computing offers flexibility in resource provisioning, allowing an organization to host its batch processing workloads cost-efficiently by dynamically scaling the size and composition of a cloud-based cluster -- a collection of instances provisioned from the cloud. However, existing schedulers fail to minimize total cost due to suboptimal task and instance scheduling strategies, interference between co-located tasks, and instance provisioning overheads. We present Eva, a scheduler for cloud-based clusters that reduces the overall cost of hosting long-running batch jobs. Eva leverages reservation price from economics to derive the optimal set of instances to provision and task-to-instance assignments. Eva also takes into account performance degradation when co-locating tasks and quantitatively evaluates the trade-off between short-term migration overhead and long-term provision savings when considering a change in cluster configuration. Experiments on AWS EC2 and large-scale trace-driven simulations demonstrate that Eva reduces costs by 42\% while incurring only a 15\% increase in JCT, compared to provisioning a separate instance for each task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07437 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Eva: Cost-Efficient Cloud-Based Cluster Scheduling Chang, Tzu-Tao Venkataraman, Shivaram Distributed, Parallel, and Cluster Computing Cloud computing offers flexibility in resource provisioning, allowing an organization to host its batch processing workloads cost-efficiently by dynamically scaling the size and composition of a cloud-based cluster -- a collection of instances provisioned from the cloud. However, existing schedulers fail to minimize total cost due to suboptimal task and instance scheduling strategies, interference between co-located tasks, and instance provisioning overheads. We present Eva, a scheduler for cloud-based clusters that reduces the overall cost of hosting long-running batch jobs. Eva leverages reservation price from economics to derive the optimal set of instances to provision and task-to-instance assignments. Eva also takes into account performance degradation when co-locating tasks and quantitatively evaluates the trade-off between short-term migration overhead and long-term provision savings when considering a change in cluster configuration. Experiments on AWS EC2 and large-scale trace-driven simulations demonstrate that Eva reduces costs by 42\% while incurring only a 15\% increase in JCT, compared to provisioning a separate instance for each task. |
| title | Eva: Cost-Efficient Cloud-Based Cluster Scheduling |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2503.07437 |