Assessing the Performance and Cost-Efficiency of Serverless Computing for Deploying and Scaling AI and ML Workloads in the Cloud
Abstract
This study investigates the efficacy of serverless computing for deploying and scaling artificial intelligence (AI) and machine learning (ML) workloads in cloud environments. We employ a comprehensive methodology to assess performance and cost-efficiency, conducting experiments using popular AI/ML frameworks on leading serverless platforms. Key performance indicators such as latency, throughput, and scalability are measured, alongside an in-depth cost analysis considering resource utilization, operational costs, and total cost of ownership. Our findings reveal that serverless computing offers significant advantages in scalability and cost-efficiency for certain AI/ML workloads, particularly those with intermittent computational needs. However, limitations such as cold start latencies and resource constraints are identified. This research contributes valuable insights for practitioners and researchers, informing decision-making processes for organizations considering serverless computing for AI/ML initiatives.
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Copyright (c) 2024 Nikhil Singla, Rajkumar Balasubramanian, Siddhant Benadikar, Rishabh Rajesh Shanbhag, Ugandhar Dasi
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