Junguk Cho · Diman Zad Tootaghaj · Lianjie Cao · Puneet Sharma

Keywords: [ hardware-efficient ml ] [ ml for systems ]


The current design of Serverless computing frameworks assumes that all the requests and underlying compute hardware are homogeneous. This homogeneity assumption causes two challenges in running ML workloads like Deep Neural Network (DNN) inference services on these frameworks. Such workloads can have various request types and might require heterogeneous accelerators. First, existing serverless frameworks are threshold-based and use simple query per second or CPU utilization as autoscaling rules, thus ignoring heterogeneous requests and accelerators, resulting in sub-optimal performance. Second, ignoring infrastructure heterogeneity for workload scheduling and inference request distribution can lead to further performance inefficiencies. To address these challenges, we propose SLA-aware ML Inference Framework, which is a novel application and hardware-aware serverless computing framework to manage ML (\eg, DNN) inference applications in a heterogeneous infrastructure. Our framework designs an intelligent autoscaling strategy by leveraging rich, precise workload-specific metrics and heterogeneous GPU compute capability. We schedule functions on the suitable GPU accelerators and proportionally distribute inference requests to the deployed functions based on the autoscaling decision. In addition, our framework enables efficient shares of GPU accelerators with multiple functions to increase resource efficiency with minimal overhead. Unlike prior works, we use application-specific SLA metrics to make scheduling/autoscaling decisions. We implement a prototype of our framework based on the Knative serverless framework and evaluate its performance with various DNN models.

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