Exploring Serverless Computing for Neural Network Training
Lang Feng, Prabhakar Kudva, et al.
CLOUD 2018
In serverless computing, developers define a function to handle an event, and the serverless framework horizontally scales the application as needed. The downside of this function-based abstraction is it limits the type of application supported and places a bound on the function to be within the physical resource limitations of the server the function executes on. In this paper we propose a new abstraction for serverless computing: a developer supplies a process and the serverless framework seamlessly scales out the process's resource usage across the datacenter. This abstraction enables processing to not only be more general purpose, but also allows a process to break out of the limitations of a single server - making serverless computing more serverless. To realize this abstraction, we propose ServerlessOS, comprised of three key components: (i) a new disaggregation model, which leverages disaggregation for abstraction, but enables resources to move fluidly between servers for performance; (ii) a cloud orchestration layer which manages fine-grained resource allocation and placement throughout the application's lifetime via local and global decision making; and (iii) an isolation capability that enforces data and resource isolation across disaggregation, effectively extending Linux cgroup functionality to span servers.
Lang Feng, Prabhakar Kudva, et al.
CLOUD 2018
Himanshu Gupta, Sandeep Hans, et al.
CLOUD 2018
Mu Qiao, Luis Bathen, et al.
CLOUD 2018
Jun Duan, Alexei Karve, et al.
CLOUD 2018