AI RESEARCH
A Heavy-Load-Enhanced and Changeable-Periodicity-Perceived Workload Prediction Network
arXiv CS.LG
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ArXi:2308.01917v3 Announce Type: replace-cross Cloud providers can greatly benefit from accurate workload prediction. However, the workload of cloud servers is highly variable, with occasional workload bursts, which makes workload prediction challenging. The time series forecasting methods relying on periodicity information, often assume fixed and known periodicity length, which does not align with the periodicity-changeable nature of cloud service workloads.