Publicación:

PARSEC: an adaptive and efficient platform for reducing cold start in serverless computing

Resumen

Serverless computing has revolutionized application development but faces a significant challenge: cold starts, which introduce delays when a function is called after a period of inactivity. Addressing these delays is crucial because they affect efficiency, performance, cost, and scalability. Existing mitigation strategies come with trade-offs, such as increased resource overhead and the need for precise resource management predictions. Also, optimizing the function startup process requires detailed knowledge of the runtime characteristics and the isolation technique used, such as using a container-based or a micro virtual machine setup. This work presents PARSEC, a comprehensive solution for cold start issues in serverless computing. By focusing on reducing initialization latency in idle containers, this research seeks to preserve scalability and ease of deployment features of serverless computing while overcoming cold start limitations. The proposed architecture improved cold start by streamlining the initialization of containers to reduce overhead. This involves minimizing unnecessary operations and customizing launches for serverless needs, aiming for a faster and more efficient setup. It also enhances the provisioning of Zygotes to speed up sandbox launches. The results show better performance for PARSEC when compared with other architectures, particularly at shorter wait times, suggesting effective cold start management. The strategic management of Zygotes and their provisioning scaling plays a critical role in managing large numbers of packages and instances, thereby enhancing the performance of package management. The cache system also evolves to become more selective, reducing overhead by focusing on essential packages