With user-facing apps adopting serverless computing, good latency performance of serverless platforms has become a strong fundamental requirement. However, it is difficult to achieve this on platforms today due to the design of their underlying control and data planes that are particularly ill-suited to short-lived functions with unpredictable arrival patterns. We present Atoll, a serverless platform, that overcomes the challenges via a ground-up redesign of the control and data planes. In Atoll, each app is associated with a latency deadline. Atoll achieves its per-app request latency goals by: (a) partitioning the cluster into (semi-global scheduler, worker pool) pairs, (b) performing deadline-aware scheduling and proactive sandbox allocation, and (c) using a load balancing layer to do sandbox-aware routing, and automatically scale the semi-global schedulers per app. Our results show that Atoll reduces missed deadlines by ~66x and tail latencies by ~3x compared to state-of-the-art alternatives.

Many distributed machine learning (DML) workloads are increasingly being run in shared clusters. Training in such clusters can be impeded by unexpected compute and network contention, resulting in stragglers. We present MLfabric, a contention-aware DML system that manages the performance of a DML job running in a shared cluster. The DML application hands all network communication (gradient and model transfers) to the MLfabric communication library. MLfabric then carefully orders transfers to improve convergence, opportunistically aggregates them at idle DML workers to improve resource efficiency, and replicates them to support new notions of fault tolerance, while systematically accounting for compute stragglers and network contention. We find that MLfabric achieves up to 3x speed-up in training large deep learning models in realistic dynamic cluster settings.