Embodiments relate to tenant-side detection and mitigation of performance degradation
resulting from interference generated by a noisy neighbor in a distributed computing
environment. A first machine-learning model such as a k-means nearest neighbor classifier
is operated by a tenant to detect an anomaly with a computer system emulator resulting from
a co-located noisy neighbor. A second machine-learning model such as a multi-class
classifier is operated by the tenant to identify a contended resource associated with the …

Embodiments relate to tenant-side detection and mitigation of performance degradation
resulting from interference generated by a noisy neighbor in a distributed computing
environment. A first machine-learning model such as a k-means nearest neighbor classifier
is operated by a tenant to detect an anomaly with a computer system emulator resulting from
a co-located noisy neighbor. A second machine-learning model such as a multi-class
classifier is operated by the tenant to identify a contended resource associated with the …