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Zhou and Colleagues Examine Burstiness in I/O Behaviors in a Cloud Platform

With advancements in machine learning technology and the availability of large machine learning-as-a-Service (MLaaS) clouds, accurately understanding I/O behaviors in the storage subsystem of an MLaaS cloud platform is crucial for resource scheduling and optimization. New research by TSYS School computer scientist Yi Zhou and his colleagues from Guangxi Minzu University, Jinan University, the University of Maine and Zhejiang University provides valuable insights into the correlation of I/O request arrivals in a representative and dynamic MLaaS workload – Alibaba PAI (a machine learning platform for artificial intelligence). Regarding the I/O requests in the PAI workload at the machine level, their diagnosis reveals that the I/O arrival process exhibits significant bursts. Their findings also highlight the existence of a certain level of correlation between I/O request arrivals on long-term time scales. Zhou and his colleagues create self-similar workload models to synthesize I/O request series based on the inputs measured from the PAI trace. Experimental results demonstrate that their models outperform existing models in accurately simulating burstiness.

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