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Time-series anomaly detection (TSAD) identifies deviations from patterns learned from historical data. In non-stationary settings, distribution drift and true anomalies can cause similar local changes, making it difficult to tell whether a deviation reflects abnormality or evolving context. Existing methods typically a...
DeepShare is a scheduler that uses a continuous tenant-assurance signal to coordinate these decisions at runtime to achieve a more advantageous utilization-QoS trade-off than optimizing quotas, scheduling, and resource sharing independently.
A prediction-guided runtime that uses workflow forecasts to construct and optimize a physical execution graph and reduces end-to-end makespan and overall p95 completion latency under burst arrivals by up to 36.8% and 25.8% over state-of-the-art workflow schedulers.
This work formalizes the problem, identifies five intrinsic characteristics underlying its core challenges, and organizes prior work into two complementary paradigms, which sharpens decision boundaries through coarse-grained, structural, and semantic anomaly injection.
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