  The event-driven and elastic nature of serverless runtimes makes them a very
efficient and cost-effective alternative for scaling up computations. So far,
they have mostly been used for stateless, data parallel and ephemeral
computations. In this work, we propose using serverless runtimes to solve
generic, large-scale optimization problems. Specifically, we build a
master-worker setup using AWS Lambda as the source of our workers, implement a
parallel optimization algorithm to solve a regularized logistic regression
problem, and show that relative speedups up to 256 workers and efficiencies
above 70% up to 64 workers can be expected. We also identify possible
algorithmic and system-level bottlenecks, propose improvements, and discuss the
limitations and challenges in realizing these improvements.
