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The paper introduces "buoyancy," a novel performance abstraction for multi-tenant systems that integrates application-level metrics with system-level resource contention insights to provide a holistic view of performance dynamics. By capturing bottlenecks and headroom across multiple resources, buoyancy facilitates resource-aware and application-aware orchestration. Experiments with representative multi-tenant workloads demonstrate that buoyancy provides a 19.3% better indication of bottlenecks compared to traditional heuristics, enabling improved observability and informed scheduling.
Forget CPU utilization – "buoyancy" reveals hidden performance bottlenecks in multi-tenant systems by tracking resource contention across the entire stack.
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.
