Performance Management for Serverless Computing

Serverless computing provides developers the freedom to build and deploy applications without worrying about infrastructure. Resources (memory, cpu, location) specified for a function can affect performance, as well as cost, of a serverless platform, so configuring these resources properly is critical to both performance and cost. COSE uses a statistical learning approach to dynamically adapt the configurations of serverless functions while meeting QoS/SLA metrics and lowering the cost of cloud usage. This project evaluates COSE on a commercial serverless platform (AWS Lambda) as well as in multiple simulated scenarios, proving its efficacy.

A related paper by Nabeel Akhtar, Ali Raza, Vatche Ishakian, and Ibrahim Matta entitled COSE: Configuring Serverless Functions using Statistical Learning will appear in the Proceedings of  IEEE International Conference on Computer Communications (INFOCOM), Beijing, China, July, 2020.


Research Area(s)


Project Resources



Project Team