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arXiv · 2503.07437

Eva: Cost-Efficient Cloud-Based Cluster Scheduling

Tzu-Tao Chang·Shivaram Venkataraman
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Abstract

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.

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Tzu-Tao Chang, Shivaram Venkataraman. 2025-03-10. Eva: Cost-Efficient Cloud-Based Cluster Scheduling. https://arxiv.org/abs/2503.07437

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