AgnoSVD: Dynamic resource allocation for serverless workloads using collaborative filtering

In serverless computing, determining the optimal resource configurations for workloads poses significant challenges, particularly due to the cloud provider’s limited visibility into workload specifics. This complexity is amplified when dealing with diverse workloads that vary in their characteristics. In this paper, we present AgnoSVD, an approach for predicting the optimum resource configuration for an incoming workload using Singular Value Decomposition (SVD). The proposed model uses collaborative filtering to extract the latent factors of the workloads and resource profiles. Therefore, the model remains agnostic to the specific details of the functions and the resource configurations. We tested our approach on well-known serverless systems like AWS lambda and Apache OpenWhisk and evaluated the system using 99 functional workloads. These workloads encompass both individual functions and chains of functions, addressing a range of computational and learning problems. To validate the system’s ability to adapt to changes, we also evaluated our system using functions with different input parameter sizes. Our evaluation shows that the model reaches convergence within 2 feedback iterations and results in a 32.41% decrease in average cost and a 5.18% average speedup, outperforming other state-of-the-art approaches. • Workload optimization is hard as providers have limited visibility into functions. • Existing monitoring-based methods raise privacy and instrumentation concerns. • AgnoSVD predicts the optimal CPU and memory settings in a function-agnostic way. • Uses collaborative filtering to learn workloadconfiguration patterns from sparse data. • Reduces cost by 32% and improves speed by 5% with minimal profiling overhead.