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arXiv · 2602.22852

Workload Buoyancy: Keeping Apps Afloat by Identifying Shared Resource Bottlenecks

Oliver Larsson·Thijs Metsch·Cristian Klein·Erik Elmroth
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Abstract

Modern multi-tenant, hardware-heterogeneous computing environments pose significant challenges for effective workload orchestration. Simple heuristics for assessing workload performance, such as CPU utilization or application-level metrics, are often insufficient to capture the complex performance dynamics arising from resource contention and noisy-neighbor effects. In such environments, performance bottlenecks may emerge in any shared system resource, leading to unexpected and difficult-to-diagnose degradation. This paper introduces buoyancy, a novel abstraction for characterizing workload performance in multi-tenant systems. Unlike traditional approaches, buoyancy integrates application-level metrics with system-level insights of shared resource contention to provide a holistic view of performance dynamics. By explicitly capturing bottlenecks and headroom across multiple resources, buoyancy facilitates resource-aware and application-aware orchestration in a manner that is intuitive, extensible, and generalizable across heterogeneous platforms. We evaluate buoyancy using representative multi-tenant workloads to illustrate its ability to expose performance-limiting resource interactions. Buoyancy provides a 19.3% better indication of bottlenecks compared to traditional heuristics on average. We additionally show how buoyancy can act as a drop-in replacement for conventional performance metrics, enabling improved observability and more informed scheduling and optimization decisions.

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Oliver Larsson, Thijs Metsch, Cristian Klein, Erik Elmroth. 2026-02-26. Workload Buoyancy: Keeping Apps Afloat by Identifying Shared Resource Bottlenecks. https://arxiv.org/abs/2602.22852

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