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Cloud infrastructure in production constantly experiences gray failures: a degraded state in which failures go undetected by system mechanisms, yet adversely affect end-users. Addressing the underlying anomalies on host nodes is crucial to address gray failures. However, current approaches suffer from two key limitations: first, existing detection relies solely on singular-dimension signals from hosts, thus often suffering from biased views due to differential observability; second, existing mitigation actions are often insufficient, primarily consisting of host-level operations such as reboots, which leave most production issues to manual intervention. This paper presents PANACEA, a holistic framework to automatically detect and mitigate host anomalies, addressing gray failures in production cloud infrastructure. PANACEA expands beyond host-level scope: it aggregates and correlates insights from VMs and application layers to bridge the detection gap, and orchestrates fine-grained and safe mitigation across all levels. PANACEA is versatile, designed to support a wide range of anomalies. It has been deployed in production at millions of hosts. 

Abstract The skew mean curvature flow is an evolution equation for a $$d$$ dimensional manifold immersed into $$\mathbb {R}^{d+2}$$, and which moves along the binormal direction with a speed proportional to its mean curvature. In this article, we prove small data global regularity in low-regularity Sobolev spaces for the skew mean curvature flow in dimensions $$d\geq 4$$. This extends the local well-posedness result in [7].