Collaborative Research: CNS Core: Medium:HardLambda: A new FaaS Abstraction for Cross-Stack Resource Management in Disaggregated Datacenters

Datacenters use computer servers that are no longer able to address the performance and scaling demands of emerging applications such as those in healthcare, smart infrastructure design, and high-speed physics. There is a fundamental mismatch between the capabilities of traditionally designed servers and the dynamic requirements of modern applications.

This mismatch leads to poor utilization and significant waste of resources. A new way to design datacenters, called the disaggregated approach, can address this problem by creating a need-based on-demand model for computing. Here, servers are specialized to perform specific functions, and applications use only those specialized servers that best perform the functions needed by each application.

While the disaggregated approach improves utilization and makes datacenters easier to manage, it comes at a performance cost: disaggregation requires applications to access critical resources spread across a set of specialized servers over the datacenter network. To mitigate such challenges of resource disaggregation, this project designs HardLambda, a new Function-as-a-Service (FaaS) abstraction that brings the functional and hardware requirements of an application together in a unified fashion.

HardLambda enables datacenters to allocate resources in ways that best meet application needs while retaining the resource utilization and management flexibility of disaggregated hardware. The designed algorithms and system software will enable scalable control and sharing of disaggregated resources, and create new approaches to adaptive resource allocation.

HardLambda will make disaggregated datacenters a viable and sustainable option for numerous applications in science and industry. The project especially targets machine and deep learning (ML/DL) applications due to their increasingly crucial role in many aspects of modern computing-powered life. At the same time, HardLambda will improve the sustainability of large-scale datacenters, where high utilization, efficiency, and continuous adaptation to application requirements are all essential factors.

The research will create new knowledge on hardware and software co-designed FaaS systems and services, and yield insights for efficiently supporting ML/DL applications at extremely large scales. The project will engage with partners in industry and national research laboratories to deploy HardLambda in real systems and will undertake educational and broadening participation activities to improve community awareness and understanding of the scaling and sustainability challenges of large-scale computing infrastructure.

Special emphasis will be given to engaging students from underrepresented groups in the research and educational activities.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.