FedExP: Speeding up Federated Averaging via Extrapolation
Divyansh Jhunjhunwala, Shiqiang Wang, et al.
ICLR 2023
For many years, the distributed systems community has struggled to smooth the transition from local to remote computing. Transparency means concealing the complexities of distributed programming like remote locations, failures or scaling. For us, full transparency implies that we can compile, debug and run unmodified single-machine code over effectively unlimited compute, storage, and memory resources.
We elaborate in this article why resource disaggregation in serverless computing is the definitive catalyst to enable full transparency in the Cloud. We demonstrate with two experiments that we can achieve transparency today over disaggregated serverless resources and obtain comparable performance to local executions. We also show that locality cannot be neglected for many problems and we present five open research challenges: granular middleware and locality, memory disaggregation, virtualization, elastic programming models, and optimized deployment.
If full transparency is possible, who needs explicit use of middleware if you can treat remote entities as local ones? Can we close the curtains of distributed systems complexity for the majority of users?
Divyansh Jhunjhunwala, Shiqiang Wang, et al.
ICLR 2023
Elton Figueiredo de Souza Soares, Emilio Ashton Vital Brazil, et al.
Future Generation Computer Systems
Helgi I. Ingolfsson, Chris Neale, et al.
PNAS
Romeo Kienzler, Johannes Schmude, et al.
Big Data 2023