|BARISTA: Efficient and Scalable Serverless Serving System for Deep Learning Prediction Services
Pre-trained deep learning models are increasingly being used to offer a variety of compute-intensive predictive analytics services such as fitness tracking, speech and image recognition. The stateless and highly parallelizable nature of deep learning models makes them well-suited for serverless computing paradigm. However, making effective resource management decisions for these services is a hard problem due to the dynamic workloads and diverse set of available resource configurations that have their deployment and management costs.
|Year of Publication
IEEE International Con- ference on Cloud Engineering (IC2E),
Prague, Czech Republic
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